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A proactive drilling system to prevent stuck pipe and differential A proactive drilling system to prevent stuck pipe and differential

sticking sticking

Ethar H. K. Alkamil

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Department: Geosciences and Geological and Petroleum Engineering Department: Geosciences and Geological and Petroleum Engineering

Recommended Citation Recommended Citation Alkamil, Ethar H. K., "A proactive drilling system to prevent stuck pipe and differential sticking" (2018). Doctoral Dissertations. 2883. https://scholarsmine.mst.edu/doctoral_dissertations/2883

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A PROACTIVE DRILLING SYSTEM TO PREVENT STUCK PIPE AND

DIFFERENTIAL STICKING

by

ETHAR HISHAM KHALIL ALKAMIL

A DISSERTATION

Presented to the Faculty of the Graduate School of the

MISSOURI UNIVERSITY OF SCIENCE AND TECHNOLOGY

In Partial Fulfillment of the Requirements for the Degree

DOCTOR OF PHILOSOPHY

IN

PETROLEUM ENGINEERING

2018

Approved by:

Ralph Flori (Advisor)

Andreas Eckert

Shari Dunn-Norman

Mingzhen Wei

Rickey Hendrix

2018

ETHAR HISHAM KHALIL ALKAMIL

All Rights Reserved

iii

PUBLICATION DISSERTATION OPTION

This dissertation has been prepared in the form of three articles, formatted in style

used by the Missouri University of Science and Technology:

Paper I: Pages 14-45 have been published in Journal of Petroleum Science and

Engineering.

Paper II: Pages 46-73 have been submitted to Journal of Petroleum Science and

Engineering.

Paper III: Pages 74-95 have been submitted to Journal of Petroleum Science and

Engineering.

iv

ABSTRACT

During drilling operations for the E oilfield in the Mishrif formation in southern

Iraq, stuck pipe and differential sticking have been identified as significant geomechanical

and drilling problems for several deviated wells. In this work, an integrated approach with

three phases is presented to serve as a proactive geo-drilling system to prevent wellbore

instability. In the first phase, a comprehensive geomechanical assessment of the Mishrif

formation has been carried out to evaluate the in-situ stresses, maximum horizontal stress

orientation, pore pressure, rock properties, and rock strength parameters. Moreover, the

geomechanical evaluation has been incorporated into the mud design using three rock

failure criteria: the Mohr-Coulomb, Mogi-Coulomb, and Modified Lade. In the second

phase, the feasibility of using managed pressure drilling (MPD) in oilfield E (the Mishrif

formation with a narrow mud window between collapse pressure and differential sticking)

has been evaluated. MPD provides the fully automated capability to maintain nearly

constant bottomhole pressure by varying the surface backpressure, thus compensating for

pressure fluctuations during drilling operations. The MPD approach yields several

operational benefits, such as increasing rate of penetration, managing surge and swab

related pressure fluctuations, and maintaining hole cleaning efficiency, which helps

prevent stuck pipe. In the third phase of this work, the geomechanical model, well

geometry, the hydraulic model, and drilling parameters sensitivity on the stresses,

distribution around the wellbore and the mud design are combined as inputs to a novel

image processing approach to estimate the collapse volume. This approach can help the

drilling operation engineers in evaluating the mud weight effect on stuck pipe problems in

real time based on the estimated collapse volume and the drilling system hole cleaning

efficiency.

v

ACKNOWLEDGMENTS

I would like to express my sincere gratitude to the Iraqi Ministry of Higher

Education and Scientific Research (MOHESR) for rewarding me a fully funded

scholarship. My sincere gratitude is extended to my PhD advisor, Dr. Ralph Flori, for his

inspiration and invaluable support throughout my research. He has always been an

excellent mentor, contributor, supporter, and friend during the whole study. I would also

like to thank my committee members, Dr. Andreas Eckert (for his discussions and

valuable contribution in my papers), Dr. Shari Dunn-Norman, Dr. Mingzhen Wei, and Dr.

Rickey Hendrix, for their time, valuable advice and recommendations.

Special thanks to Signa Engineering Corp (Dr. Sagar Nauduri and Mr. George

Medley), Weatherford Oil Company (Don Hannagan), and Ikon Science Company for

sharing information and providing me complementary softwares.

I am very thankful for my research group members, Husam R. Abbod and Ahmed

Abbas for being such wonderful partners and helpers. I would also like to thank friends

in my hometown and in Rolla, especially Dr. Ali Albattat and Dr. Ali Alhuraishawy, for

their support and encouragement.

A special thanks to my family, especially my parents, for their love, support,

encouragement, and prayers throughout my study. Special thanks to my family members

and friends who were a great support.

Ultimately, I would like to thank my lovely wife, Dhoha, my sweet daughter,

Manar, and my awesome son, Ahmed, for their love and great patience throughout my

study. Without their steadfast support and kind encouragement, this study would have

never been completed.

vi

TABLE OF CONTENTS

Page

PUBLICATION DISSERTATION OPTION ................................................................. iii

ABSTRACT .................................................................................................................. iv

ACKNOWLEDGMENTS ...............................................................................................v

LIST OF ILLUSTRATIONS ......................................................................................... xi

LIST OF TABLES ...................................................................................................... xiii

SECTION

1. INTRODUCTION ...................................................................................................... .1

1.1. GEOLOGICAL CHARECTARISTICS ................................................................4

1.2. DATA UTILIZATION FOR WELLBORE-STABILITY ANALYSIS .................4

1.2.1. Well Logging Data ......................................................................................4

1.2.2. Daily Drilling Reports .................................................................................7

1.2.3. Daily Mud Reports ......................................................................................7

1.2.4. Daily Mud Logging Reports .......................................................................7

1.2.5. Primary Cementing Reports .......................................................................7

1.2.6. Final Well Report ......................................................................................7

2. LITERATURE STUDY ...............................................................................................8

2.1. MECHANICS EARTH MODELING (MEM).…………..………………………..8

2.2. MANAGED PRESSURE DRILLING (MPD)……………………………………..9

2.3. COLLAPSE VOLUME LOG USING IMAGE PROCESSING LOG.………........9

2.4. LITERATURE REVIEW DISCUSSION. ........................................................... 10

3. RESEARCH OBJECTIVES. ..................................................................................... 13

vii

PAPER

I. CASE STUDY OF WELLBORE STABILITY EVALUATION FOR THE

MISHRIF FORMATION, IRAQ ............................................................................... 14

ABSTRACT .................................................................................................................. 14

1. INTRODUCTION ..................................................................................................... 15

2. METHODOLOGY .................................................................................................... 16

2.1. IN-SITU STRESSES .......................................................................................... 17

2.1.1. Vertical Stress .......................................................................................... 17

2.1.2. Minimum Horizontal Stress ...................................................................... 18

2.1.3. Pore Pressure ............................................................................................ 19

2.1.4. Maximum Horizontal Stress ..................................................................... 20

2.1.5. The Orientation of Maximum Horizontal Stresses………..………….........23

2.2. ELASTIC PARAMETERS ................................................................................. 25

2.3. ROCK STRENGTH ........................................................................................... 25

2.3.1. Unconfined Compressive Strength (UCS) ................................................ 26

2.3.2. Internal Friction Angle ............................................................................. 26

2.3.3. Tensile Strength ....................................................................................... 26

3. WELLBORE STABILITY ........................................................................................ 27

3.1. DRILLING CHALLENGES .............................................................................. 27

3.2. COLLAPSE PRESSURE ................................................................................... 28

3.3. DIFFERENTIAL STICKING ............................................................................. 28

4. SENSITIVITY ANALYSIS ....................................................................................... 29

5. RESULTS AND DISCUSSION................................................................................. 29

6. CONCLUSIONS ...................................................................................................... 31

viii

ABBREVIATIONS ....................................................................................................... 34

ACKNOWLEDGEMENTS .......................................................................................... 35

NOMENCLATURE ..................................................................................................... 35

APPENDICES

A. MISHRIF FORMATION LOG DATA ..................................................................... 37

B. QUALITY RANKING SYSTEM.............................................................................. 38

C. ROCK FAILURE CRITERIA FOR WELLBORE STABILITY ANALYSIS……....39

REFERENCES .............................................................................................................. 41

II. A PROACTIVE MANAGED PRESSURE DRILLING SYSTEM TO

PREVENT STUCK PIPE AND DIFFERENTIAL STICKING IN THE

MISHRIF FORMATION, SOUTHERN IRAQ ......................................................... 46

ABSTRACT .................................................................................................................. 46

1. INTRODUCTION ..................................................................................................... 47

2. METHODOLOGY .................................................................................................... 50

2.1. MPD STRATEGY TO REDUCE STUCK PIPE RISK AND

DIFFERENTIAL STICKING ............................................................................. 50

2.2. MPD CANDIDATE SELECTION APPROACH ................................................ 52

3. RESULTS ................................................................................................................. 53

3.1. WELLBORE STABILITY ASSESSMENT BASED ON 1D MEM

APPROACH…………..………………………………………...……………….53

3.2. MPD VS CONVENTIONAL DRILLING .......................................................... 53

3.3. INTEGRATION OF MEM AND MPD .............................................................. 54

4. DISCUSSION ........................................................................................................... 56

4.1. MPD REDUCED MUD WEIGHT EFFECT ON DRILLING RATE ................ 57

4.2. MPD REAL-TIME BHP CONTROL ............................................................... 58

4.3. HOLE CLEANING .......................................................................................... 59

ix

4.4. MPD SURGE AND SWAB EFFECTS ON BHP .............................................. 61

5. SUMMARY AND CONCLUSIONS ......................................................................... 63

ACKNOWLEDGEMENTS ..................................................................................... ..65

REFERENCES ............................................................................................................. .65

APPENDIX……………………………………………………………………………....72

III. A COLLAPSE VOLUME LOG ESTIMATION BASED ON IMAGE

PROCESSING..……………………………………………………………….......….74

ABSTRACT .................................................................................................................. 74

1. INTRODUCTION ..................................................................................................... 75

2. METHODOLOGY….………………………..………………………...……………..77

2.1. ANALYTICAL VISUALIZATION OF FAILURE AREA...…………….……..79

2.2. PREDICTION OF COLLAPSE AREA AND VOLUME USING IMAGE

PROCESSING ................................................................................................... 81

3. RESULTS……………………………………………………………….. ................... 82

3.1. MISHRIF FORMATION ................................................................................... 83

3.2. ZUBAIR FORMATION ..................................................................................... 84

4. DISCUSSION ........................................................................................................... 87

5. CONCLUSIONS ....................................................................................................... 90

ACKNOWLEDGEMENTS ....................................................................................... 91

REFERENCES ............................................................................................................. .91

SECTION

4. CONCLUSIONS AND RECOMMENDATIONS ...................................................... 96

4.1. CONCLUSIONS ................................................................................................ 96

x

4.2. RECOMMENDATIONS .................................................................................... 98

REFERENCES…………………………………………………………………………..99

VITA……………………………………………………………………………………108

xi

LIST OF ILLUSTRATIONS

Figure Page

SECTION

1.1. The stratigraphic column of the E oilfield …………………………….….……….5

1.2. The geological prognosis of the E oilfield ……………………………..……….....6

PAPER I

1. Extended leak-off test in Well A to determine the minimum horizontal stress,

Sh for the Mishrif formation……………………………….………..……….……..19

2. The E Field mud pressure window is based on interpolated pore pressure and

formation breakdown pressures.…………………….………….......……………....20

3. Mishrif Formation stress polygon analysis showing that the inferred stress

magnitudes document a normal faulting stress regime.……………........… ………23

4. FMI log (well A) showing an exemplary borehole breakout oriented towards

146oN and 328oN, indicating an approximately NE-SW maximum horizontal

stress orientation…………..………………………………………………………..24

5. Breakout orientations for Mishrif formation; (a) Shows the breakout orientations

obtained from the FMI log, (b) Shows the Breakout orientations obtained from

the four arm caliper log.………………………………………………………….....25

6. Sensitivity analysis in normal faulting stress regime of Mishrif Formation …………29

7. Minimum mud weight plots for different failure criteria............................................33

8. Minimum mud weight plots for different failure criteria. .………………………….34

PAPER II

1. Operational mud pressure window (highlighted in green) for the Mishrif

Formation in Oilfield E is defined on the low side by the collapse pressure

(Pc) for the Mishrif Formation, and on the high side by the differential

sticking pressure (Pds) for the Mishrif Formation….……………………………....51

2. Minimum mud weight plots using Mogi-Coulomb failure criterion .....……...…….54

xii

3. Combined mud pressure window obtained from the 1D MEM derived

collapse pressure and the surface back pressure adjusted MPD approach…….……56

4. Cleaning efficiency vs. cutting size and cutting density………..…………..……....60

5. Swab/Surge Effect on the BHPDynamic when the rig pumps are OFF….……….…..64

PAPER III

1. Collapse volume log estimation approach workflow diagram.………………............79

2. Inferred area (outlined with black lines) of collapse failure for UCS=45 MPa...…..80

3. Flowchart indicating the calculation of the collapse area.…....………….……........82

4. Mishrif Formation-Shale rock (a) wellbore diameter data obtained from the

6-arm caliper log data (b) calculated borehole diameter obtained from the

image processing approach……………………………………..…..…….…..……84

5. Zubair Formation-Shale rock (a) wellbore diameter data obtained from the


image processing approach…………..…………….……………….….………..….85

6. Zubair Formation-Sand rock (a) wellbore diameter data obtained from the


image processing approach…….………………………………………………..….86

7. Predicted wellbore hole profile while drilling using the image processing

approach…..……………………………………………………………………...…90

xiii

LIST OF TABLES

Table Page

PAPER I

1. MEM parameters for eight wells in Mishrif Formation. ………………………..…...18

2. Well trajectory data, actual used mud weight, recommended mud weight for

the three different failure criteria…....……………………………………………...32

PAPER II

1. MPD definitions of pressures and equations…………………………..……….........52

2. Annular Pressure Data under static and dynamic conditions…………………...........55

3. Parameters considered to determine hole cleaning efficiency………...……….........59

PAPER III

1. MEM input parameters and source of measurement...................................................78

2. Exemplary 1D MEM parameters obtained from wireline logs and various

formation tests for the Mishrif Formation……………………………...……….......83

1

SECTION

1. INTRODUCTION

The obvious goal for drilling operators is to drill economical, safe, and stable wells by

reducing nonproductive time (NPT) due to wellbore stability problems such as borehole

collapse and associated stuck pipe, and borehole breakdown and associated loss of

circulation. A key issue for successful drilling operations in geomechanically challenging

zones is considering all relevant factors including formation strength properties, in-situ

stresses, pore pressure, and applied pressure by the drilling mud. The Mishrif Formation is

the most challenging one in the E oilfield in southern Iraq due to its stratigraphic

characteristics such as high heterogeneity (Aqrawi et al., 2005; Jassim and Goff, 2006).

Historically, wellbore instability problems occur during drilling operations in field E

(termed oilfield E in this work) through this formation. In recent years the problems are

greater since more deviated wells are being drilled to achieve production efficiency and

better recovery. Wellbore instability problems such as stuck pipe and differential sticking

introduce significant non-productive time to the drilling program, and in some wells, these

problems require drilling sidetracks which are undesirable and expensive. Therefore, it is

crucial that drilling engineers consider these wellbore instability problems during well

planning and design and introduce appropriate solutions (Numbere and Okoli, 2014).

Borehole failure problems, which are very likely especially when drilling overbalanced

without geomechanical consideration, cost the petroleum industry several billions of

dollars each year. Prevention of these problems requires clear understanding of the

interaction between formation strength, in-situ stresses, and drilling practice. Since in-situ

stress and rock strength are not controllable parameters, adjusting the drilling practices (i.e.

2

selecting optimal trajectory and bottom-hole pressure) is the common way to inhibit

wellbore failure, which can be achieved by performing specialized geomechanical studies.

Therefore, the objective of this work is to mitigate the differential sticking and stuck pipe

problems in Mishrif formation in field E. (Alkamil et al., 2017, Alkamil et al., 20181, 2). To

achieve this objective, a real-time proactive drilling system is developed to prevent the

stuck pipe and differential sticking problems.

This study analyzed data from eight wells in field E that penetrated the Mishrif

formation to determine the 1D MEM factors that contributed to wellbore instability like

borehole collapse. Furthermore, the analyses accounted for other drilling input parameters

like bottomhole assembly (BHA), deviated well geometry, and mud rheology to evaluate

the feasibility of utilizing the managed pressure drilling (MPD) to ensure drilling a safe

well by avoiding related drilling problems, taking into account drilling practice effects such

as swab, surge, hole cleaning, and rate of penetration (Malloy and Shayegi, 2010).

First, the Mishrif formation geomechanical assessment was built based on eight wells

(A-H) data such as in-situ stresses, pore pressure, bottomhole pressure, and formation

properties. This assessment is applied to the wellbore stability of offset wells to verify and

calibrate it. The goal of the analysis is to determine the proper mud weight range along

with an optimum wellbore profile to prevent collapse problems, stuck pipe, or differential

sticking.

Second, this work investigates using cutting-edge drilling technology like MPD to

optimize the drilling process. MPD allows the use of the lowest reasonable mud weight,

as it achieves the needed downhole mud pressure by applying varying backpressure at the

surface. Where, the MPD drilling parameters are modified due to the pore pressure of the

3

formation (Rehm, 2008; Tian et al., 2007). To do so, the mud weight can be reduced with

the surface back pressure (SBP) adjustment based on the pore pressure of the drilled layer,

to cope with a significant change in the regime of pore pressure (Zambrano et al., 2015).

Both MPD hydraulic planning and simulations are run with many possible mud weights

with their required surface back pressure to find out the optimum senario to achieve the

target equivalent circulating density (ECD) at the top of the pressure window (Cui et al.,

1999; Alkamil et al., 2017).

Third, a novel approach determining the area/volume of collapse failure by using

image processing is presented. The presented approach is independent of any failure

criterion and very versatile. Based on the failure criterion applied to determine borehole

collapse, the detailed 2D area of collapse can be determined, thus limiting the degree of

underestimation compared to analytical techniques, which assume a triangular breakout

geometry.

Last of all, a proactive drilling system is developed to estimate the collapse volume

log while drilling using an image processing approach (Alkamil et al., 2017) by

performing a real-time evaluation of MEM and MPD parameters. Based on an MEM

approach, the computed breakout angle vs. depth indicates a narrow (but acceptable) mud

weight window vs. depth, which ensures a stable wellbore or at least minimizes breakout

occurrence. The MPD system is utilized for precise pressure control by using the MEM

recommended mud weight and a proposed surface backpressure window to reduce the

overbalanced pressure and to avoid differential sticking. The collapse volume log can aid

the drilling engineers in evaluating the mud weight effect on the hole cleaning efficiency

4

to avoid stuck pipe problems. In addition, knowledge of the collapse volume provides

better estimates on the required mud and cement volumes.

1.1. GEOLOGICAL CHARECTARISTICS

The E oilfield in the southern of Iraq is a double-plunging symmetrical anticline about

60 km long and 15 km wide, with closure in the order of 400 m for the middle and early

Cretaceous reservoirs. Thirteen separate hydrocarbon-bearing horizons have been

identified in carbonate and clastic reservoirs, including Miocene (Ghar formation), late

Cretaceous (Shiranish, Hartha, Saadi, Tanuma and Khasib formations), and early

Cretaceous (Mishrif, Ahmadi, Nahr Umr, Shuaiba, Yamama and Zubair formations). The

source rocks for the field are thought to be the Middle Jurassic shale of the Sargelu and

Naokelekan formations(Aqrawi et al., 2005; Jassim and Goff, 2006).

Several regional unconformities and shales provide seals for the oil pools, with Nahr

Umr shale being a particular effective seal horizon for major accumulations. The

stratigraphic column of the E oilfield is illustrated in Figure 1.1, and the geological

prognosis is based on the most recent mapping of the field structure illustrated in Figure

1.2.

1.2. DATA UTILIZATION FOR WELLBORE-STABILITY ANALYSIS

Data needed for wellbore-stability analysis are discussed in the following subsections.

1.2.1 Well Logging Data. Well logging data are available for several wells drilled in

the study field. Well log data were used to build petrophysical models. In addition, image

and sonic log data collected in a limited number of wells were utilized to obtain in-situ

stress magnitudes as well as stress orientations and to estimate the level of stress

5

anisotropy. Moreover, the image logs were used to correlate the drilling data and observe

borehole conditions to identify the specific intervals causing wellbore stability issues.

Figure 1.1. The stratigraphic column of the E oilfield.

6

Figure 1.2. The geological prognosis of the E oilfield.

7

1.2.2 Daily Drilling Reports. Daily drilling reports can be a helpful source to identify

unstable intervals and causes for rock failure when well-log data are not available.

Challenges observed during the drilling process such as string over-pulls, dragging, and

mud losses were correlated with caliper and well image log data to identify the unstable

intervals. The time effect associated with the chemical interactions was indirectly implied

from the drilling performance and the caliper data.

1.2.3 Daily Mud Reports. Daily mud reports were utilized to identify the mud

characteristics: MW, rheological properties, and sand percent. In addition, the report

describe the formation’s cuttings size and provides an indirect clue to the hole cleaning

issues during drilling of the directional wells.

1.2.4 Mud Logging Reports. Daily mud logging reports were used to acquire: (1)

input data for petrophysical modeling. (2) identify the high pore pressure zones. (3) the

size and shape of cuttings were used to verify the active wellbore- failure mechanism

taking place in the field to make a critical decision about whether to increase mud weight

or to hold it at the same level. (4) gas show readings were used to pinpoint the pore

pressure for the hydrocarbon saturated shale intervals.

1.2.5 Primary Cementing Reports. The utilization of the cementing reports

represents an indirect way to correlate different factors, which help in the prediction of an

allowable equivalent circulation density (ECD) to drill a planned section.

1.2.6 Final Well Report. The final drilling reports were used to evaluate the drilling

parameters and estimate the feasibility of the drilling operations for the oilfield

development plan.

8

2. LITERATURE STUDY

The main objective of this section is to review and discuss the wellbore instability

problems in the Mishrif Formation with the consideration of all possible parameters that

will affect them. Previous modeling efforts used to evaluate these problem mitigation

solutions like geomechanics and one of the leading drilling technology like MPD are

evaluated. Moreover, the literature review is extended to cover studies about detailed

breakout geometry. A detailed review of the current usage of MEM and/or MPD is

followed by a critical review of these methods to identify the current limitations.

2.1 MECHANICS EARTH MODELING (MEM)

A MEM consists of three major parts: well geometry, in-situ stresses and pore pressure,

and rock physical properties. A properly constructed 1D MEM model based on the classic

Kirsch equations for stresses around a cylindrical hole (for both vertical and deviated wells)

and the classical 2D Mohr-Coulomb failure criteria is used in this paper (Bell and Cough,

1979; Zoback et al., 1985; Aadnoy and Looyeh, 2011). The 1D MEM results are represented

by the hoop, radial and axial stresses (σƟƟ, σrr, and σzz) around the wellbore and the required

unconfined to determine the collapse pressure (i.e., minimum mud weight) for the Mishrif

Formation. Three different failure criteria, the Mohr-Coulomb, Mogi-Coulomb, and

Modified Lade criteria (Mohr, 1900; Ewy, 1999; Al-Ajmi and Zimmerman, 2005; Maleki,

et al., 2014; Rahimi and Nygaard, 2015) were investigated in order to analyze the existing

wellbore stability problems for the eight wells (termed A–H), and to determine feasible

(safe) drilling trajectories (i.e. azimuths and inclinations) and mud weight conditions for

many different wells in the Mishrif Formation.

9

2.2 MANAGED PRESSURE DRILLING (MPD)

The Mishrif formation in oilfield E represents a depleted reservoir with high degree of

heterogeneity, which causes a significant declination in both pore pressure and fracture

pressure and leads to a narrow acceptable mud weight. In effort to apply the latest

technology and new drilling methods to mitigate the challenges mentioned but in Saudi

Arabia, Saudi Aramco started using Managed Pressure Drilling (MPD) in late 2012 to

workover the Campaign Field wells. Previous experience in Saudi Arabia has proven the

benefits of the MPD technology to considerably reduce NPT related to differential

sticking, lost circulation, and formation fluid influx. This was achieved by having more

accurate control of the annular pressure profile and a more precise monitoring of the well,

which allowed for a much quicker response (Al-Thuwaini et al., 2010). MPD is known as

a drilling process optimization tool where the main objectives are to mitigate the drilling

hazards to enhance control of the well and decrease NPT. In other words, the goal is to

drill successfully to the planned target while saving costs and improving safety conditions

(Babajan, et al, 2010). MPD provides the ability to navigate through a narrow drilling

window (Rehm, 2008).

2.3 COLLAPSE VOLUME LOG USING IMAGE PROCESSING LOG

The proposed image processing approach to assess wellbore collapse (i.e., shear

failure) is based on parameters obtained from an MEM accounting for well geometry, in-

situ stresses and pore pressure, and rock physical properties. The MEM determines the

resulting wellbore stresses (i.e., hoop, radial and axial stresses: σƟƟ, σrr, and σzz) based on

the classic equations for stresses around a cylindrical hole for both vertical (Kirsch, 1898)

10

and deviated wells (Peska and Zoback, 1995). Based on the rock strength properties

applied, the area of collapse can be calculated. In order to provide a proactive geo-drilling

assessment tool for the collapse volume, while many necessary MEM input parameters can

be obtained pre-drilling or during measurements while drilling the horizontal stress

magnitudes need to be determined from offset well data (or from a nearby field), as their

determination/estimation is only possible post-drilling (Zoback et al., 1985; Bell and

Babcock, 1986; Mastin, 1988; Tingay et al., 2011). It is clear that the magnitudes of the in-

situ stresses and the rock strength properties will affect the relationship between breakout

width and depth, and therefore a detailed description of the geometry of the breakout is

necessary (Moos et al., 2007; Zoback, 2010).

In order to validate the predicted breakout area obtained from the image processing

approach, the breakout width is initially determined analytically based on the MEM. As

stated by Zoback (2010), breakout width is a critical parameter for assessing the severity

of collapse. If the width of the breakout, WBO, exceeds 90° and 60o for vertical and deviated

respectively, a severe breakout occurs resulting in wellbore collapse. While the width of

breakouts can be determined analytically, an analytical solution for the breakout depth, to

the authors’ knowledge, does not currently exist.

2.4. LITERATURE REVIEW DISCUSSION

From the literature review, all previous studies agreed on the significant effect of

MEM improve the understanding of the rock properties and the in situ stresses. However,

the resulting 1D MEM is not enough to mitigate the instability problems with a narrow

acceptable range of mud weight to prevent stuck pipe and differential sticking. Moreover,

the MEM predicts the static mud weight to ensure stability, but that is only in the case of

11

rig pumps that are switched off. When the pumps are on, an additional pressure is needed

to compensate the annular friction pressure effect, which increases the ECD. Furthermore,

there are some of uncertainties in estimation the MEM parameters like SH, that’s why

sensitivity analysis for the MEM parameters is required.

On the other hand, MPD drilling system is evaluated to keep constant annular pressure

while drilling. However, MPD is usually used to ensure safety by controlling the

bottomhole pressure (BHP) through reducing the possibility of getting kick. But in Mishrif

formation, differential sticking represents a driver to use MPD; therefore, the drilling

system in this case work on collapse pressure is predicted by MEM, not the pore pressure.

The MPD controllable parameters are evaluated in order to ensure self-optimized

parameters to increase the MPD controllability. As a result, MEM and MPD approaches

fix the gap of each other with drilling practice considerations like hole cleaning, rate of

penetration (ROP), and swab and surge effect.

Furthermore, as an analytical solution for breakout depth does not exist, the expected

breakout depth can only be determined post-drilling, either using numerical modeling

approaches or by measurements obtained from 4- or 6-arm caliper logs. While numerical

analyses are impractical to assess collapse for a complete well profile (i.e., it would require

a large number of modeling runs for different well sections and would have to assume

average material properties for these sections), 4- and 6-arm caliper log analysis has been

used to determine wellbore collapse post-drilling for better cement volume estimations

(Jarosiński, 1998). A disadvantage of using caliper log data is the inherent assumption of

a triangular breakout geometry (Escobar et al., 2014), which underestimates the actual

area. In addition, the post-drilling application is a disadvantage to drilling operations as a

12

proactive real-time assessment of the expected collapse volume represents important

knowledge during drilling operations. In this study, an image processing approach is

proposed that utilizes the wellbore stresses and rock strength properties obtained from the

MEM to calculate the area of collapse based on the Coulomb failure criterion. Under the

condition that horizontal stress magnitudes can be obtained pre-drilling from a nearby field

or from an offset well, all other MEM parameters can be obtained “proactively” during

drilling operations (e.g., using logging while drilling). These dynamic MEM properties

can then be used in realtime to continuously process images of wellbore cross sections and

then determine the expected collapse volume during drilling.

Finally, the presented image processing approach has the potential to be used as a

proactive geo-drilling approach, which helps in avoiding severe collapse failure and

decreasing associated uncertainties and non-productive time.

13

3. RESEARCH OBJECTIVES

In this research, a proactive drilling system is developed to compute the possible

wellbore breakout angle/area/volume in a real-time mode using MEM and MPD

principles. Based on an MEM approach, the computed breakout angle vs. depth indicates

a narrow (but acceptable) mud weight window vs. depth, which will ensure a stable

wellbore or at least minimize breakout occurrence. To overcome the identified gaps in the

literature, the main research objective for this dissertation is to develop a method to

estimate a collapse volume log while drilling and determine how it is related to the wireline

data stream and the manipulated mud weight.

The main objective can be broken down to the following three sub objectives:

1. Assess and address existing wellbore stability problems to provide guidance for

future well plans that increase the drilling efficiency by reducing the nonproductive

time. Use a Mishrif formation geomechanical assessment to ensure wellbore

stability.

2. Evaluate the feasibility of using MPD to keep the bottomhole pressure constant,

which helps in reducing the overbalanced pressure and reducing differential sticking.

3. Integrate MEM with MPD to reduce both collapse failure and differential sticking

by keeping the bottomhole pressure constant (i.e., adhere to the collapse pressure

from MEM), with drilling practice considerations.

4. Use the image processing approach to estimate the collapse volume log while

drilling, which can help as a real-time proactive geo-drilling system to prevent stuck

pipe.

14

PAPER

I. CASE STUDY OF WELLBORE STABILITY EVALUATION FOR THE

MISHRIF FORMATION, IRAQ

Ethar H.K. Alkamil, Husam R. Abbood, Ralph E. Flori, Andreas Eckert

Missouri University of Science and Technology

(Published in Journal of Petroleum Science and Engineering 164 (2018) 663–674)

ABSTRACT

During drilling operations for the E oilfield in the Mishrif formation in southern Iraq,

stuck pipe has been identified as a significant geomechanical problem for several wells. In

this study, a 1-D mechanical earth model (MEM) of the Mishrif formation is compiled

based on its state of stress and rock strength parameters, and is utilized to assess the

contribution of borehole collapse leading to the stuck pipe problems. The results of this

study show that wells characterized by stuck pipe are drilled along azimuths which promote

wellbore collapse. Three different failure criteria, the Mohr-Coulomb, Mogi-Coulomb, and

Modified Lade rock failure criteria, are investigated in order to determine feasible drilling

trajectories (i.e. azimuths and inclinations) and mud pressure conditions for many different

wells in the Mishrif Formation. If a specific azimuth for a well cannot be altered, an

optimum inclination is recommended to reduce the severity of the borehole collapse.

However, as the intermediate principal in-situ stress increases the optimum drilling

inclination progressively changes. The presented study shows that 1-D MEMs are an

important tool to both assess and address existing wellbore stability problems and to

provide guidance for future well plans for better drilling efficiency.

15

1. INTRODUCTION

\\\

It is estimated that more than 60% of the world's oil and 40% of the world's gas reserves

are held in carbonate reservoirs. The Arabian plate, as an example, is dominated by

carbonate fields, with around 70% of oil and 90% of gas reserves held within these

reservoirs. The Mishrif Formation in southern Iraq represents heterogeneous organic

detrital limestones, with beds of algal, rudist, and coral reef limestones, capped by limonitic

fresh water limestones (Aqrawi et al., 2010; Jassim and Goff, 2006). The thickness of the

formation is around 237 m, ranging from the top 2393m true vertical depth (TVD) to the

bottom of the formation at 2630m TVD.

For improved drilling and production efficiency, non-vertical, deviated production

wells are adopted in a particular oilfield in the Mishrif Formation (termed Oilfield E in this

paper). In some cases, a substantial distance horizontally away from the drilling location

was reached using deviated boreholes (Schroeter and Chan, 1989). Moreover, the deviated

boreholes are crucial to reach not accessible locations by vertical boreholes due to

Explosive Remnants of War (ERW; Hooft van Huysduynen et al., 2014; Tianshou et al.,

2015; Mansourizadeh et al., 2016). However, drilling non-vertical boreholes accounts for

a variety of problems like cuttings transport, drill string friction, casing setting and its

cementing job. In the E oilfield, many wells were characterized by differential sticking

(Helmick and Longley, 1957) across the Mishrif formation and also had some challenges

during in-hole cleaning as the “J” and “S” shaped wells had a tangent section between 20o

and 42o degree inclination. Moreover, several wells experienced significant wellbore

stability problems with stuck pipe as a consequence of borehole collapse being the most

frequent (Charlez, 1991). The wellbore stability problems were observed in wells with

16

azimuths ranging from 9o to 310o. A review of the drilling operation data shows that the

used mud weight window was based on formation pore pressure and formation breakdown

pressure only. Detailed geomechanical calculations necessary to determine the safe mud

pressure window for deviated wellbore trajectories (e.g. PesKa and Zoback, 1995),

including the in-situ stress magnitudes, rock strength properties and oriented wellbore data,

were not considered.

This study utilizes a 1D MEM approach (e.g. Kristiansen, 2007; Gholami et al., 2014;

Alkamil et al., 2017; Das and Chatterjee, 2017) in order to determine the collapse pressure

(i.e. minimum mud weight) for the Mishrif Formation. The geomechanical model includes

the in-situ principal stresses and their orientations obtained from wireline logging

measurements, measurements while drilling (MWD), and leak off tests (LOT). Rock

strength properties are obtained from empirical equations and extended leak off tests. Three

different failure criteria, the Mohr-Coulomb, Mogi-Coulomb, and Modified Lade criteria,

representing a conservative, realistic and optimistic criterion (Ewy, 1999; Al-Ajmi and

Zimmerman, 2005; Maleki et al., 2014; Rahimi and Nygaard, 2015; Gholami et al., 2015;

Najibi et al., 2017) are investigated in order to analyze the existing wellbore stability and

differential sticking problems for 8 wells (termed Wells A – H), and to determine feasible

(i.e. safe) drilling trajectories (i.e. azimuths and inclinations) and mud weight conditions

for many different wells in the Mishrif Formation.

2. METHODOLOGY

An analysis of the optimal mud weight for drilling a new well through depleted

reservoirs requires a field-specific geomechanical model, termed a 1D Mechanical Earth

17

Model (MEM), that consists of characterization of the elastic parameters, rock strength

properties, pore pressure and in-situ stresses. The components of the 1D MEM for the

Mishrif Formation are derived from daily drilling reports, daily mud reports, formation

integrity tests (FIT), and wireline well logs (Far et al., 2016).

2.1. IN-SITU STRESSES

Stable drilling trajectories are directly dependent on the knowledge of the in-situ state

of stress (Bell, 1990). Since detailed information about the in-situ stress regime of the

Mishrif formation is unknown (or confidential), the assumed Andersonian state of stress

(Jaeger et al., 2007) is determined by a procedure, which initially determines the vertical

stress from wireline density logs, followed by minimum horizontal stress determination

from extended leak-off tests and the estimation of the maximum horizontal stress using

borehole breakout data (Zajac and Stock, 1992), which in turn is validated by stress

polygon analysis (Zoback et al., 1986; Moos and Zoback, 1990). Stress orientations are

derived from breakout orientations (e.g. Zoback et al., 1985; Bell and Babcock, 1986;

Mastin, 1988; Tingay et al., 2011).

2.1.1. Vertical Stress. The weight of the overburden is calculated by integrating the

bulk density log (shown in Appendix A) based on Eq. (1).

σ = ∫ ρg dz (1)z

0

Where z is vertical depth, g is the gravitational acceleration constant, and ρ is the rock

bulk density at a specific depth. The vertical stress in the Mishrif Formation ranges from

59 MPa to 66 MPa (based on data from eight wells in the Mishrif Formation; Table 1).

18

2.1.2. Minimum Horizontal Stress. The minimum horizontal stress is determined by

an extended leak-off test (Zoback et al., 1985) conducted in Well A of the E Oilfield.

Table 1. MEM parameters for eight wells in Mishrif Formation.

The magnitude of the minimum horizontal stress (σh) is represented either by the

instantaneous shut-in pressure (ISIP; if low viscosity fluids such as water or thin oils are

used) or the fracture closure pressure (FCP; if higher viscosity fluids such as oil are used)

on the mini-frac test plot (Figure 1; Zoback, 2010). As the fracturing fluid for the mini-frac

test in the Mishrif formation was water, the ISIP is used to determine the minimum

horizontal stress of 27–32 MPa at a depth of 2534m (Figure 1; Table 1).

MEM

parameters

Well

A

Well

B

Well

C

Well

D

Well

E

Well

F

Well

G

Well

H

σv 59.6 60.3 56.7 61.5 62.7 62.6 61.2 63.5

σh 27.0-32.0

σH 53.6 45.0 43.4 56.6 57.9 52.1 65.5 50.5

σH

orientation 51.0

Pp 26.0 26.0 26.0 26.0 26.0 26.0 26.0 26.0

UCS 47.8 37.3 29.1 60.9 60.9 47.6 99.5 47.6

To 8.00

φ 21.02 21.61 25.53

19

Figure 1. Extended leak-off test in Well A to determine the minimum horizontal stress,

Sh for the Mishrif formation. The ISIP indicates a Sh of 32 MPa.

2.1.3. Pore Pressure. The Mishrif Formation is characterized by highly variable pore

pressures. Figure 2 shows pore pressure measurements from more than 40 wells. The pore

pressure measurements are based on repeat formation tests (Stewart and Wittmann, 1979)

for the Oilfield E including the Mishrif Formation and over- and underlying formations

(Figure 2). Due to inconsistencies in the measured pore pressure values because of the

reservoir depletion (i.e. the pore pressure data distribution represents more than 40 wells)

resulting in maximum (Max Pp) and minimum pore pressure (Min Pp) distributions,

drilling operations were based on an interpolated pore pressure across the whole field (Int

Pp). This interpolated pore pressure is also used in the following calculations for the

updated mud weight window.

Figure 2 also shows the formation breakdown pressure (FBP) obtained from leak-off

tests for more than 40 wells. Similar to the pore pressure measurements an interpolated

FBP is calculated based on the maximum FBP (Max FBP) and minimum FBP (Min FBP)

20

measurements. The interpolated pore pressure and FBPs were subsequently used to

calculate the operating mud weight window.

2.1.4. Maximum Horizontal Stress. As the maximum horizontal stress magnitude

cannot be measured directly, several methods to obtain an estimate are employed. The first

estimate is obtained by data obtained from the extended leak-off test (Haimson and

Fairhurst, 1969). For a hydraulic fracture to propagate, the formation breakdown pressure

is given by:

FBP = 3σh − σH + To − Pp (2)

The tensile strength, To, can be estimated from repeat cycles of an extended leak-off test

Figure 2. The E Field mud pressure window is based on interpolated pore pressure and

formation breakdown pressures. Pore pressures in the Mishrif Formation range from 16

MPa to 29 MPa.

21

(Fjaer, 1992; To=8 MPa for the Mishrif Formation), σH is given by:

σH = 3σh − FBP + To − Pp (3)

For the extended leak-off test conducted in well A in the Mishrif Formation σH = 41

MPa. Since measurements/estimates for pore pressure, FBP and tensile strength are also

available (based on extended leakoff tests) for wells B-H, assuming that σh from Well A

applies for the whole field, additional stress magnitude estimates for σH (for wells B-H)

can be obtained (Table 1).

The second estimate for σH is obtained using the technique of circumferential wellbore

modeling (Zoback et al., 2003). The fact that drilling induced tensile failure is not observed

in any well in the Mishrif Formation requires:

3σh − σH − Pp − Pi > −To (4)

With the previously determine magnitudes for σh, pore pressure, mud pressure and

tensile strength, wellbore fluid pressure, σH > 46 MPa in the Mishrif Formation.

A similar constraint on σH can be obtained considering the observation of breakouts in

a deviated well following Zoback and Peska (1995). However, since the following analysis

evaluates the influence of three different failure criteria (Modifier Lade, Mohr-Coulomb,

Mogi-Coulomb) on the observed well stability problems in the E oilfield, Zoback and

Peska's (1995) procedure would have to be conducted for the three different failure criteria.

Moreover, Fjær et al. (2008) have shown that six different permutations of the axial, hoop

and radial stress have to be considered in order to map the occurrence of instability regions

in a deviated wellbore. Such an extensive analysis of the estimation of σH is beyond the

scope of this study and will be considered in a separate contribution. For the assumption

22

of a vertical well (for a Mohr-Coulomb failure criterion) a simple estimate of σH can be

obtained by requiring:

Pore pressures in the Mishrif Formation range from 16 MPa to 29 MPa.

σ1 ≥ UCS + σ3

1 + sin

1 − sin (5)

Where σ1 = σƟƟ (hoop stress), σ3 = σrr (radial stress), UCS is the unconfined compressive

strength, and ϕ is the coefficient of internal friction. This gives:

3σH − σh − Pp − Pi ≥ UCS + (Pi − Pp)1 + sin

1 − sin (6)

Where Pi represents the wellbore fluid pressure. Hence, σH can be estimated by:

σH ≥1

3[UCS + (Pi − Pp)

1 + sin

1 − sin + σh + Pp + Pi] (7)

The data for the Mishrif formation for well A yields σH > 53 MPa, which coincides

with the previous estimate of σH > 46 MPa. Since breakouts and wellbore collapse is

observed in several wells in the Mishrif formation, σH = 53 MPa is used for the subsequent

wellbore stability analysis, UCS and ϕ (Table 1).

In addition, to further evaluate the previous constraints for σH, stress polygon analysis

(Figure 3; Zoback et al., 1986) shows that the σH magnitudes determined favor an

extensional (i.e. normal faulting) stress regime and that the σH magnitudes arrange are on

the periphery of the polygon, which is often observed for crustal stresses in frictional

equilibrium (Zoback, 2010).

23

Figure 3. Mishrif Formation stress polygon analysis showing that the inferred stress

magnitudes document a normal faulting stress regime.

2.1.5. The Orientation of Maximum Horizontal Stresses. Stress orientations of σH

were determined from borehole breakouts interpreted from resistivity image logs and four-

arm caliper data. By definition, the maximum horizontal stress direction is perpendicular

to the breakout azimuth (Zoback et al., 1985). Breakout orientation data in the Mishrif

Formation determined from Formation Micro-Imager (FMI) log data (Figure 4) comprises

6 breakout zones of a combined length of ~7m yielding a maximum horizontal stress

direction of 51o12o (Figure 5a). Following the quality criteria defined by the world-stress-

map data base (Appendix B, World Stress Map, 2008; Zoback, 2010), Quality B is

assigned. Based on interpretation of the 4-arm caliper log data (Jarosinski, 1998), only one

breakout of 0.5m length could be identified, yielding a maximum horizontal stress direction

of 54o (i.e. resulting in Quality D; Figure 5b). While the stress orientation data is not

24

extensive, a close correlation to nearby stress measurements from an oilfield in Kuwait

(Azim et al., 2011), which shows a maximum horizontal stress direction of 45o, was

obtained.

Figure 4. FMI log (well A) showing an exemplary borehole breakout oriented towards

146ºN and 328 ºN, indicating an approximately NE-SW maximum horizontal stress

orientation.

25

Figure 5. Breakout orientations for Mishrif formation; (a) Shows the breakout orientations

obtained from the FMI log, (b) Shows the Breakout orientations obtained from the four

arm caliper log.

2.2. ELASTIC PARAMETERS

Due to the absence of laboratory core measurements and S-wave velocities not being

recorded on the sonic log, the Poisson's ratio is assumed to be 0.25, and the sensitivity

analysis on the influence of the Poisson's ratio on the mud design was performed by

(Alkamil et al., 2017) and it will be discussed later in this work.

2.3. ROCK STRENGTH

Since the following wellbore stability analyses are based on the Mohr-Coulomb, the

Mogi-Coulomb and the Modified Lade failure criteria, the rock strength parameters of

cohesion (determined from the unconfined compressive strength), So, internal friction

angle, ϕ, and tensile strength, To, need to be determined.

26

2.3.1. Unconfined Compressive Strength (UCS). Due to the absence of laboratory

core measurements, UCS is determined using empirical relationships based on wireline

logging measurements (Chang et al., 2006). For limestone, UCS is related to the porosity

by (Chang et al., 2006):

UCS = 143.8 exp(−6.95) (8)

The porosity is determined directly from the Neutron log. For the Mishrif Formation

data from eight wells gives UCS in the range of 29–99.5 MPa (Table 1). The UCS can be

related to the cohesion and the internal friction angle by Eq. (9) (Al-Ajmi and Zimmerman,

2005).

UCS = (2 So cosϕ)/(1 − sin ϕ) (9)

Where So is the rock cohesion and ϕ is the internal friction angle.

2.3.2. Internal Friction Angle. It can be determined by correlating physical laboratory

test data to a typical downhole log (commonly acoustic or density) by an empirical

equation. Due to the lack of core data the internal friction angle can be estimated from Eq.

(10) and (11) (Plumb, 1994).

= 26.5 − 37.4( 1 − NPHI − Vshale ) + 62.1 (1 − NPHI − Vshale)2 (10)

Where NPHI is the neutron porosity, and Vshale is the volume of shale obtained by

Vshale = GR − GRmin

GRmax − GRmin (11)

For the Mishrif Formation ϕ is in the range of 21o–25o (Table 1).

27

2.3.3. Tensile Strength. Due to the absence of a Brazilian strength test, To is estimated

from the extended leak-off test (Torres et al., 2003), for which To can be estimated by the

difference between the FBP and ISIP as shown in Figure 1. For the Mishrif Formation a

tensile strength of 8 MPa is determined (based on data from Well A; Table 1).

3. WELLBORE STABILITY

3.1. DRILLING CHALLENGES

Due to the heterogeneity of the Mishrif reservoir, the formation pore pressure fluctuates

across the entire reservoir zone, which causes localized fluctuations in the near-wellbore

stresses. Under this scenario, high enough mud-weight values (while maintaining

overbalanced drilling conditions) are required to minimize breakout severity (i.e. shear

failure: e.g. Zoback, 2010; Aadnoy and Looyeh, 2011). However, in the case of low

reservoir pore pressure (as also observed in the Mishrif Formation), the pore pressure might

be close to hydrostatic or sub-hydrostatic; thus, a higher mud weight is likely to cause a

large overbalance, increasing the chances of getting differentially stuck while drilling

across these reservoirs (Helmick and Longley, 1957). It needs to be restated that the

interpolated pore pressure was used to calculate the operating mud weight window.

Due to the uncertainty in the distribution of the pore pressure along the planned

trajectory, the predicted mud weight will have uncertainties both for minimizing breakouts

and managing differential sticking. Because a drilling problem could result from one or a

combination of these parameters, an integrated approach to select the optimum mud weight

between the minimum mud weight required to prevent collapse failure and the maximum

overbalance allowed to prevent the differential sticking occurrence, is used here.

28

3.2. COLLAPSE PRESSURE

The minimum mud weight, i.e. also termed collapse pressure, is determined based on

the compiled 1D MEM for all possible wellbore trajectories (PesKa and Zoback, 1995).

The equations for the calculation of the required tangential wellbore stresses in an

arbitrarily oriented wellbore are given in detail in Aadnoy (1989), PesKa and Zoback

(1995), Zoback (2010) and are therefore not repeated here. Based on the MEM, three

different failure criteria (Mohr-Coulomb, Mogi-Coulomb, and Modified Lade; Appendix

C) are used to evaluate the risk of borehole collapse. Figures. 6 and 7 show the collapse

pressure for two of the eight wells in Field E for different wellbore orientations.

3.3. DIFFERENTIAL STICKING

Differential sticking can result when pressure from an overbalanced mud column acts

on the surface area of the drill string against a filter cake deposited across a permeable

formation. The surface area of the pipe that is embedded into the mud cake has a pressure

equal to the pore pressure acting from one direction while the hydrostatic pressure acts in

the other direction. When the hydrostatic pressure in the wellbore is higher than the

formation pressure, the pressure differential forces the pipe towards the borehole wall. This

usually occurs along the drill collars because there is less annular clearance to begin with,

the drill collars usually have larger diameter, which increases the crossectional area that is

in contact with the borehole, and the drill collars are the first section of the pipe to encounter

the permeable formation (Rehm et al., 2008). The best method to limit the risk of

differential sticking is by using the minimum mud weight (Helmick and Longley, 1957).

29

4. SENSITIVITY ANALYSIS

A sensitivity analysis was carried out for the Mishrif formation overbalance pressure

using the Mogi–Coulomb criteria. The results in Figure 6 show clearly that the top three

factors which impact the recommended mud weight from the MEM model are the

maximum horizontal stress, cohesion and friction angle (Alkamil et al., 2017).

Figure 6. Sensitivity analysis in normal faulting stress regime of Mishrif Formation.

5. RESULTS AND DISCUSSION

An analytical model incorporating three failure criteria is adopted to help predicting

the mud weight window as a function of the wellbore inclination and azimuth. This model

is applied to analyze the mechanical stability of eight deviated wells in the Mishrif

formation oilfield E (wells A-H). Two wells (A and B) are considered as exemplary studies

in order to address the geomechanical problems of stuck pipe (Well A) and differential

sticking (Well B), respectively (Table 2). Since comparing different failure criteria is not

the objective of this study, the Mohr-Coulomb, the Mogi-Coulomb and the Modified Lade

30

criterion are used as examples of including/excluding the intermediate principal stress on

wellbore stability (Rahimi and Nygaard, 2015).

Figures 7 and 8 show stereographic contours (for all possible azimuths and inclinations)

for the minimum mud weight for Well A and B, respectively using the three different

failure criteria (PesKa and Zoback, 1995). Both figures indicate the most stable drilling

azimuth (i.e. requiring the lowest mud weight) is parallel to the minimum horizontal stress

for inclinations of more than 50o. For the case of drilling in the direction of the maximum

horizontal stress a higher mud weight is required to keep the well stable. For inclinations

up to 30o, the well azimuth only has a slight effect on the mud weight.

For Well A (drilled with a mud weight of 1.1 sg), the results show (independent of

failure criteria) that the field operator used a mud weight less than required for the planned

azimuth and inclination (triangle symbol in Figures 7a, b, c) which led to wellbore collapse.

As the results for the various failure criteria show (for the actual drilled well), the Modified

Lade criterion (Figure 7a) predicts a mud weight of 1.175 sg. The Mohr-Coulomb criterion

(Figure 7b) predicts stable mud weights as high as 1.38–1.4 sg, and the Mogi-Coulomb

criterion (Figure 7c) predicts stable mud weights of 1.23 sg. A recent study by Rahimi and

Nygaard (2015) has shown that while the Modified Lade is an overly optimistic criterion,

and the Mohr-Coulomb criterion being overly conservative, the Mogi-Coulomb criteria

yields a more reliable and realistic estimate of the minimum mud weight. For the case of

Well A, an increase in mud weight of 0.13–0.15 sg would have resulted in a “trouble-free”,

stable well for the drilled trajectory. As Figure 6c shows, a mud weight of 1.1 sg would

have required an azimuth of 141o (parallel to the minimum horizontal stress ordination)

and an inclination angle higher than 60o. As can be seen from Table 2, all wells in Field E

31

of the Mishrif Formation experiencing wellbore collapse and associated “stuck pipe”

(Wells A, E and H) have been drilled with a mud weight less than suggested by the Mogi-

Coulomb criterion. It is therefore concluded that the presented 1D MEM approach can be

used to mitigate all wellbore collapse problems observed in Field E.

For Well B, the operator tried to support the wellbore by increasing the mud weight

(1.22 sg; without geomechanical consideration) resulting in high overbalance pressure

conditions, which caused differential sticking. The Modified Lade criterion (Figure 8a)

suggests that a reduction to 1.09 sg would be possible, however as shown for Well A, this

would increase the likelihood of collapse. The Mohr-Coulomb criterion (Figure 8b) even

suggests a higher minimum mud weight than used, and therefore cannot be considered. The

Mogi-Coulomb criterion would enable a reduction of 0.05 sg before risking the onset of

collapse. If this reduction still results in differential sticking, the optimal drilling trajectory

(with an azimuth of 141o and an inclination of more than 60o) would enable to use a mud

weight as low as 1.05 sg. As can be seen in Table 2, all wells in Field E of the Mishrif

Formation experiencing differential sticking (Wells B, C, D, F and G) have been drilled

with a mud weight higher than suggested by the Mogi-Coulomb criterion.

6. CONCLUSIONS

This study shows that drilling operations in the Mishrif formation were conducted

without considering an appropriate geomechanical analysis. The operating minimum mud

weight was assigned based on the interpolated pore pressure distribution, and widespread

32

borehole collapse was observed in several wells in the Mishrif Formation. A simple 1D

MEM used to calculate the minimum mud weight (based on the principal stresses of an

arbitrary oriented wellbore) shows that the widespread stability problems could have been

prevented.

The results of this study document the prediction of the minimum mud weight based

on three different failure criteria. The results obtained from the Mogi–Coulomb failure

criterion, which are chosen as the most indicative failure criterion to assess wellbore

collapse (e.g. Rahimi and Nygaard, 2015), indicate that all wells experiencing collapse and

associated stuck pipe have been drilled with too low of a mud weight. The 1D MEM

approach can be used to design an optimal minimum mud weight for future wells based on

the results presented. Based on the horizontal stress orientations, this study recommends

well azimuths along the minimum horizontal stress direction with inclinations higher than

40o.

Table 2. Well trajectory data, actual used mud weight, recommended mud weight for the three different failure criteria.

Well

No. Azi. Inc.

Actual

MW

[sg]

Min. MW

(Mohr-

Coulomb)

Min. MW

(Mogi-

Coulomb)

Min. MW

(Modified

Lade)

Drilling

Challenge

A 188 38 1.1 1.38 1.23 1.17 Stuck pipe

B 158 19 1.22 1.31 1.17 1.09 Diff. sticking

C 228 33 1.22 1.2 1.07 0.98 Diff. sticking

D 39 20 1.2 1.36 1.15 1.1 Diff. sticking

E 187 40 1.11 1.46 1.31 1.18 Stuck pipe

F 38 31 1.2 1.28 1.14 1.12 Diff. sticking

G 279 37 1.1 1.04 0.9 0.82 Diff. sticking

H 214 41 1.22 1.62 1.43 1.37 Stuck pipe

33

Figure 7. Minimum mud weight plots for different failure criteria. The triangular symbol

shows the azimuth and inclination of the actual well (drilled with a mud weight of 1.1 sg)

which experienced wellbore collapse. a) Modified Lade failure criterion, b) Mohr-

Coulomb failure criterion, c) Mogi-Coulomb failure criterion. In the contour plots, the

azimuths (from north 0º to 360º) are labeled around the perimeter; and the well inclination

(from vertical 0º to horizontal 90º) are labeled along the radial direction.

In addition to addressing wellbore collapse, the 1D MEM approach can also be used to

mitigate the occurrence of differential sticking as observed for several wells in the Mishrif

Formation. The results presented show that all wells experiencing differential sticking have

been drilled with a mud weight higher than suggested by the Mogi-Coulomb criterion. It is

therefore concluded that adhering to the minimum mud weight predicted by the Mogi-

Coulomb failure criterion reduces the likelihood of wellbore collapse and also limits the

potential for differential sticking in the E oilfield in the Mishrif Formation.

34

Figure 8. Minimum mud weight plots for different failure criteria. The triangular symbol

shows the azimuth and inclination of the actual well (drilled with a mud weight of 1.22 sg)

which experienced differential sticking. a) Modified Lade failure criterion, b) Mohr-

Coulomb failure criterion, c) Mogi-Coulomb failure criterion. In the contour plots, the

azimuths (from north 0O to 360O) are labeled around the perimeter; and the well inclination

(from vertical 0O to horizontal 90O) are labeled along the radial direction.

ABBREVIATIONS

FBP, Formation breakdown pressure; FCP, Fracture closure pressure; FIT, Formation

integrity tests; FMI, Formation micro-imager; Int Pp, Interpolated pore pressure; ISIP,

Instantinous shutt-in pressure; LOT, Extensed leak-of-test; Max FBP, Maximum formation

breakdown pressure; Max Pp, Maximum pore pressure; Min FBP, Minimum formation

35

breakdown pressure; Min Pp, Minimum pore pressure; MEM, Mechanical earth model;

MW, Mud weight; MWD, Measuring while drilling; NPHI, Neutron porosity; TPN, Non-

productive time; TVD, True vertical depth; UCS, Unconfined compressive strength;

Vshale, Shale volume.

ACKNOWLEDGEMENTS

We would like to express our appreciation to the Iraqi Ministry of Higher Education

and Scientific Research (MOHESR) - University of Basrah for sponsoring Ethar Alkamil

to finish this work. Husam Abbood would like to thank South Oil Company for the

permission to publish the data of the Mishrif Formation.

NOMENCLATURE

ϕ Internal friction angle

Co Unconfined compressive strength

DTCO Sonic log

Edyn Dynamic Young's Modules

Estat Static Young's Modules

G Bulk Modules

i Inclination

k Stress path coefficient

NF Normal Fault

Pp Pore pressure

Pw Mud Weight

36

q Flow factor parameter

r Distance from wellbore

R Wellbore radius

RHOB Density log

37

APPENDIX A.

MISHRIF FORMATION LOG DATA

38

APPENDIX B.

QUALITY RANKING SYSTEM

Table B1. Quality assessment table for stress orientation data used by the world stress map data base (WSM, 2008; Zoback, 2010).

A B C D

Earthquake

focal

mechanisms

Average P-axis or formal

inversion of four or more single-

event solutions in close

geographic proximity(at least

one event M≥ 4.0, other events

M≥ 3.0)

Well-constrained single-event

solution (M≥ 4.5) or average of

two well-constrained single-

event solutions (M≥ 3.5)

determined from first motions

and other methods (e.g. moment

tensor wave-form modeling, or

inversion)

Single-event solution

(constrained by first motions

only, often based on

author’squality assignment)(M≥

2.5). Average of several well-

constrained composites (M≥ 2.0)

Single composite solution. Poorly

constrained single-event solution.

Single-event solution for M < 2.5

event

Wellbore

breakouts

Ten or more distinct breakout

zones in a single well with sd ≤ 12◦ and/or combined length >300

m. Average of breakouts in two

or more wells in close geographic

proximity with combined length

>300 m and sd ≤ 12◦

At least six distinct breakout

zones in a single well with sd ≤ 20◦ and/or combined length >

100 m.

At least four distinct breakouts

with sd < 25◦ and/or combined length > 30 m.

Less than four consistently

oriented breakout or >30 m combined length in a single well.

Breakouts in a single well with sd

≥ 25◦.

Drilling-

induced tensile

fractures

Tenor more distinct tensile

fractures in a single well with sd

≤ 12◦ and encompassing a

vertical depth of 300 m, or more

At least six distinct tensile

fractures in a single well with sd

≤ 20◦ and encompassing a

combined length > 100 m

At least four distinct tensile

fractures with sd < 25◦ and

encompassing a combined length

> 30 m.

Less than four consistently

oriented tensile fractures with <30

m combined length in a single

well. Tensile fracture orientations

in a single well with sd ≥ 25◦.

Hydraulic

fractures

Four or more hydrostatic

orientations in a single well with

sd ≤ 12◦ depth >300 m. Average

of hydrofrac orientations for two

Three or more hydrofrac

orientations in a single well with

sd < 20◦. Hydrofrac orientations

in a single well with 20◦ < sd <

25◦

Hydrofac orientations in a single

well with 20◦ < sd < 25◦. Distinct

hydrofrac orientation change

with depth, deepest

measurements assumed valid.

One or two hydrofrac

orientations in a single well.

Single hydrofrac measurements at

<100 m depth.

38

39

APPENDIX C.

ROCK FAILURE CRITERIA FOR WELLBORE STABILITY ANALYSIS

Mohr-Coulomb failure criterion. The Mohr-Coulomb failure criterion is the most

commonly used failure criterion in mechanical earth modeling, which does not consider

the effect of the intermediate principal stress in contrast to the triaxial stress state of rock.

The Mohr-Coulomb criterion is based on the assumption that f (σ) is a linear function of σ

as shown in Eq. (C.1):

τ = μ σ + So

μ = tanϕ (C. 1)

Regarding the principal stresses, the Mohr-Coulomb failure criterion can be expressed

in Eq. (C.2).

σ1 = qσ3 + UCS (C. 2)

Where:

q =1 + sinϕ

1 − sinϕ

UCS =2S cosϕ

1 − sinϕ (C. 3)

Mogi-Coulomb failure criterion. It was first introduced by Al-Ajmi and Zimmerman

(Al-Ajmi and Zimmerman, 2005, 2009). This failure criterion considers the effect of the

intermediate principal stress. The Mogi-Coulomb criterion can be formulated in Eq. (C.4).

τoct = κ + mσoct (C. 4)

Where τoct and σoct are the octahedral shear and normal stresses, defined as in Eq. (C.5).

40

τoct =1

3√(σ1 − σ2)2 + (σ1 − σ3)2 + (σ2 − σ3)2

σoct = 1

3(σ1 + σ2 + σ3) (C. 5)

τoct = a + bσm,2

Where:

σm,2 =σ1 + σ3

2

a =2√2

3So cos

b =2√2

3sin

Modified Lade failure criterion. The Modified Lade failure criterion is a three-

dimensional failure criterion that was originally proposed for cohesion-less sands. Then

the criterion was adopted for analyzing rocks with finite values of cohesion (So) and To by

Ewy (1999) and such a formulation was later linked (Ewy, 1999) with the standard rock

mechanics parameters such as ϕ and So as shown in Eqs. (C.6) and (C.7).

(I1′ )3

I3′ = 27 + η (C. 6)

Where, I1 and I3’ are stress invariants.

I1′ = (σ1 + S − Pp) + (σ2 + S − Pp) + (σ3 + S − pp)

I3′ = (σ1 + S − Pp)(σ2 + S − Pp)(σ3 + S − pp) (C. 7)

Where, S is related to the cohesion of the rock, and η represents the internal friction.

41

Parameters S and η can be derived directly from the Mohr-Coulomb cohesion So and

internal friction angle ϕ by Eq. (C.8).

S =So

tanϕ η =

4tan2ϕ(9 − 7sinϕ)

1 − sinϕ (C. 8)

Note that So can be linked to Co and ϕ through So =Co/2q1/2, whereas q=tan2 (π/4+ ϕ/2).

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46

II. A PROACTIVE MANAGED PRESSURE DRILLING SYSTEM TO PREVENT

STUCK PIPE AND DIFFERENTIAL STICKING IN THE MISHRIF

FORMATION, SOUTHERN IRAQ

Ethar Alkamil, Andreas Eckert, Ralph Flori, Husam Abbood


(Submitted to Journal of Petroleum Science and Engineering)

ABSTRACT

Conventional drilling approaches in oilfield E (southern Iraq) of the Mishrif and

Zubair Formation (drilled in the same hole section) have led to significant occurrences of

non-productive time, both due to wellbore collapse and differential sticking in Mishrif

Formation. The geomechanical assessment for the Mishrif Formation highlights two major

points: (1) the collapse pressure is greater than the pore pressure (i.e. overbalanced drilling

is required); (2) a narrow acceptable mud weight window is present that cannot be handled

by conventional drilling approaches. This study integrates the mechanics earth modeling

(MEM) derived collapse pressure with a managed pressure drilling (MPD) approach to

mitigate the risks of both collapse and differential sticking. The feasibility of MPD for

oilfield E is obtained by reducing the initial mud weight from 9.16-10.17 ppg to 8.2-8.4ppg

and maintaining a constant bottom hole pressure via the surface back pressure. This

approach enables keep the bottom hole pressure above the collapse pressure and below the

pressure for which differential sticking occurs.

In addition to be able to navigate the narrow mud weight window conditions, the

presented MPD approach yields several operational benefits, such as (1) an increased rate

of penetration compared to conventional drilling; (2) the ability for real-time bottom hole

47

pressure management; (3) the ability to manage surge and swab related pressure

fluctuations; and (4) maintaining a hole cleaning efficiency of ~90% (based on Mishrif

Formation oilfield reported drill cuttings and MPD evaluation discussed in this study).

1. INTRODUCTION

In recent years, Managed Pressure Drilling (MPD) has become an active approach

to well control and mitigate the wellbore stability problems. MPD systems represent a

closed loop system for the fluid circulation that enables to manage bottom hole pressure,

formation pore pressure, and formation fracture pressure (Rehm et al., 2008; Chin 2012;

Marcia et al., 2017). The active component is represented by the capability to adjust the

surface backpressure in real time to account for downhole pressure variations (Chin 2012;

Kaasa et al., 2012). This represents a significant advantage over adjusting the mud density,

as a statically underbalanced mud weight can be initially applied, and the equivalent

circulating density (termed BHPDynamic in this study), be increased with the MPD surface

back pressure. A common objective in most MPD well approaches is to optimize casing

point design (Frink, 2006; Aadnoy et al., 2012; Sugden et al., 2014; Nguyen et al., 2017).

However, the rapid response times of the MPD technique has shown to be an effective

alternative to maintain wellbore stability in difficult situations (e.g., rapidly narrowing or

changing mud pressure window; Alkamil et al., 2018a). In particular, a recent study has

shown that MPD can be successfully applied in wells prone to wellbore collapse and

differential sticking (Soto et al., 2017; see Appendix A for definitions).

Such complex conditions have been frequently observed in wells in the E onshore

Oilfield located in southern Iraq (termed oilfield E in this study). Oilfield E is considered

one of the largest oil and gas fields in the Middle East, with more than thirteen carbonate

48

and sandstone reservoirs (Aqrawi et al., 2005; Jassim and Goff, 2006). The two main

reservoirs are the Mishrif Formation, a highly heterogeneous limestone formation (Aqrawi

et al., 2005; Jassim and Goff, 2006) at 7849 ft true vertical depth (TVD) to 8645 ft TVD

(i.e., 796 ft thickness), and the Zubair Formation at 10942 ft to 12299 ft TVD (i.e., 1357 ft

thickness). Due to the heterogeneity of the Mishrif reservoir, the formation pore pressure

fluctuates across the entire reservoir zone around 4.16 ppg (i.e., being under-pressured),

however the Zubair formation is normally pressured around 9.5 ppg (Alkamil, et al., 2018a;

Alkamil, et al., 2018b). This causes localized fluctuations in the near-wellbore stresses (i.e.

Helmick and Longgley, 1957; Teale 1965; Zoback 2010; Aadnoy and Looyeh, 2011) and

therefore leads to uncertainties in predicting the operational mud weight window required

for minimizing breakouts (lower limit; i.e., collapse pressure), managing differential

sticking (upper limit), and the minimum overbalanced pressure required to prevent fluid

influx (i.e., kick prevention; Rehm, 2008; Smith and Patel, 2012). To reduce drilling costs

both formations were initially drilled in the same hole (8 ½-in. section); however, this plan

led to a high percentage of non-productive time (NPT) as the resulting acceptable mud

weight window in the Mishrif Formation became very small. As a result wellbore

instability (i.e., collapse) in deviated production wells (which are adopted to improve

drilling and production efficiency; Schroeter et al., 1989; Huysduynen et al., 2014)

occurred. In addition, if the mud weight was slightly increased to mitigate the risk of

collapse, one of the most significant drilling operation challenges in the Mishrif formation

was differential sticking (Helmick and Longley, 1957; Charlez, 1991), resulting from the

low formation pore pressure and the high mud weight required to keep the bottom hole

pressure (BHP) higher than the pore pressure exposed in the Zubair and other formations

in this hole.

49

A recent geomechanical assessment of Oilfield E in the Mishrif Formation (Alkamil

et al., 2018a) has shown that all wells experiencing collapse and associated stuck pipe have

been drilled with too low of a mud weight (based on the Mogi-Coulomb failure criterion;

Maleki et al, 2014; Rahimi and Nygaard, 2015). In addition, Alkamil et al.’s (2018b) results

show that all wells experiencing differential sticking have been drilled with a mud weight

higher than suggested by the Mogi-Coulomb criterion (Alkamil et al, 2018). It is therefore

concluded that adhering to the minimum mud weight predicted by the Mogi-Coulomb

failure criterion reduces the likelihood of wellbore collapse and also limits the potential for

differential sticking in the Mishrif Formation.

The main objective of this study is to investigate the feasibility of the MPD

approach to mitigate the prevalent drilling problems in the Mishrif Formation (i.e. stuck

pipe and differential sticking) for the present very narrow acceptable mud weight window.

While Alkamil et al.’s (2018) geomechanical assessment of the Mishrif Formation

considers the static wellbore conditions (i.e. when the pumps are shut off), resulting in the

calculation of the static mud density (PStatic), which in this case is equal to the collapse

pressure, Pc; Figure 1; Soto et al., 2017), the added annular frictional pressure (AFP) to

maintain the dynamic bottom hole pressure (i.e., BHPDynamic; BHPDynamic= PStatic +AFP) will

cause BHPDynamic to become larger than the pressure, Pds, to prevent the onset of

differential sticking in the Mishrif Formation (dashed red line in Figure 1). The

combination of MPD and the initial geomechanical assessment provides an integrated

approach to mitigate these risks by adjusting the BHPDynamic via the surface back pressure

and an automated MPD approach also enables to monitor pit gain and to prevent fluid

influx. Based on data obtained from eight wells in oilfield E a 1D mechanical earth

modeling (MEM) approach (Alkamil et al., 2017) is used to calculate the borehole collapse

50

pressure to provide the static geomechanical constraints for wellbore stability. The analysis

of formation pore pressure, collapse pressure, in combination with many other drilling

input parameters such as the required BHPDynamic, bottom-hole assembly (BHA), deviated

well geometry, and mud rheology is used to evaluate the feasibility of using the MPD

technique for the Mishrif Formation. The goal is to ensure that the selected mud weight is

larger than the minimum mud weight required to prevent collapse/stuck pipe and lower

than the maximum mud weight allowed to prevent differential sticking, while being

overbalanced enough to prevent fluid influx. The MPD approach for a suggested target

well is discussed with respect to its operational capabilities such as real time adjustments,

hole cleaning, surge and swab effects (Malloy and Shayegi, 2010), rate of penetration, and

alternative approaches such as underbalanced drilling.

2. METHODOLOGY

2.1. MPD STRATEGY TO REDUCE STUCK PIPE RISK AND DIFFERENTIAL

STICKING

The MPD method (the fundamental principles, and how BHPDynamic is calculated is

shown by Alkamil et al., 2018b)) can enhance drilling practice by reducing the overbalance

pressure against any formation, minimizing differential pressure and preventing wellbore

collapse and associated stuck pipe problems by applying many approaches (Rehm et al.

2008). In this study, the Constant Bottom Hole Pressure (CBHP) MPD technique is

adopted.

51

Figure 1. Operational mud pressure window (highlighted in green) for the Mishrif

Formation in Oilfield E is defined on the low side by the collapse pressure (Pc) for the

Mishrif Formation, and on the high side by the differential sticking pressure (Pds) for the

Mishrif Formation. Using conventional drilling without the consideration of the collapse

pressure and using the pore pressure for the Zubair Formation as the lower limit (i.e. mud

weight PStatic < Pc) will either result in wellbore collapse during static conditions, or lead to

differential sticking during dynamic conditions (i.e. BHPDynamic > Pds).

The CBHP technique enables to adjust the BHPDynamic by applying a Surface Back

Pressure, SBP, via an MPD system (Table 1; Rehm et al., 2008). This technique, (1)

optimizes the casing design to drill both the Mishrif Formation and Zubair Formation in

the same hole section in spite of the pore pressure difference between them, (2) provides

the ability to maintain constant pressure on the wellbore during drilling, connection, and

tripping in or out of the hole, thus reducing cycling of the pressure on the wellbore and

hence reduces the risk of stuck pipe, and (3) enables to minimize the overbalanced mud

weight while applying SBP to avoid differential sticking (Rehm et al. 2008).

In addition, an MPD system using a programmable logic controller (PLC) for

automatic control ability can exert and relieve pressure on the wellbore as required to

52

increase or decrease the BHPDynamic nearly instantly (Hannegan 2011). This controllability

can be achieved by manipulating the MPD choke or pump at the surface, which provides

the ability to manipulate the BHPDynamic as required, and to get the drill string unstuck

within minutes. Automated control systems have been improved by using techniques such

as smart instrumentation with real-time diagnostics, large diaphragm seal transducers,

multi-sensor voting systems, auto tracking pressure relief valve control, and adaptive self-

tuning surface back pressure (SBP) control (Moosavinia et al. 2016). Finally, MPD can

directly affect a project’s financial viability and improve safety by reducing mud weight

and the non-productive time (NPT) and improving precise pressure control.

Table 1. MPD definitions of pressures and equations.

2.2. MPD CANDIDATE SELECTION APPROACH

In order to evaluate the feasibility of MPD for wells in Oilfield E, the MPD

Candidate Selection Model (CSM) software (Nauduri and Medley, 2010) has been used

in a previous study to mitigate the risk of differential sticking (Alkamil et al., 2018b),

without considering the prevalent collapse problems as the CSM software does not

account for the collapse pressure. However, due to the narrow mud weight windows

observed in oilfield E, MPD can help in avoiding collapse failure and differential sticking

Conventional Drilling Managed Pressure Drilling

Hydrostatic

pressure PStatic = MW PStatic = Reduced density MW

Rig pumps off BHPDynamic = PStatic BHPDynamic = PStatic + SBPStatic

Rig pumps on BHPDynamic = PStatic + AFP BHPDynamic = PStatic + AFP +

SBPDynamic

53

by reducing the static mud design to be greater than PStatic and adjusting the BHPDynamic to

fall in-between Pc and Pds (Figure 1) in the Mishrif Formation. To do so, the presented

approach integrates the static calculations for collapse pressure from the 1D MEM

approach with the dynamic requirements obtained from the MPD calculations.

3. RESULTS

3.1. WELLBORE STABILITY ASSESSMENT BASED ON 1D MEM APPROACH

The Mishrif formation geomechanical assessment has been presented by Alkamil

et al. (2018a). The 1D MEM parameters used are listed in Appendix A. The study shows

that the Mogi-Coulomb failure criterion (Figure 2) predicts stable minimum mud weights

of 10.25 ppg (triangle in Figure 2a for Well A) and 9.75 ppg (triangle in Figure 2b for Well

B) to prevent collapse failure. If the mud weight is increased slightly, i.e., 0.42-0.83 ppg

larger than the predicted collapse pressure, differential sticking was recorded in well B

(i.e., resulting in Pds=10.17 ppg; Alkamil et al., 2018a). This shows that the Mishrif

Formation has a narrow acceptable mud weight range (Figure 1) to prevent collapse (lower

limit) and differential sticking (upper limit). Therefore, two main problems should be

addressed: (1) optimization of the casing point design for the hole section that includes the

Mishrif and Zubair Formations, (2) tight pressure window and since the Mishrif Formation

collapse pressure and differential sticking pressure are close to each other (Alkamil et al.,

2018a), the MPD technique is considered to handle such conditions.

3.2. MPD VS CONVENTIONAL DRILLING

Conventional drilling practices require the calculation of the minimum mud weight

based on the collapse pressure, Pc and keeping the bottom hole pressure constant at a

54

pressure greater than Pc (Note: for formations where the pore pressure, Pp, exceeds Pc, the

pore pressure is the minimum mud weight).

Figure 2. Minimum mud weight plots using Mogi-Coulomb failure criterion (after Alkamil

et al., 2018a). a) The triangular symbol shows the azimuth and inclination of the actual

well A (drilled with a mud weight of 9.16 ppg) which experienced wellbore collapse using.

b) The actual well B (drilled with a mud weight of 10.16 ppg) experienced differential

sticking.

For static conditions this is equal to PStatic, which should be greater than Pc (e.g.

9.75 ppg for Well B), PStatic = MW (Table 1). However, when the rig pumps are ON,

additional pressure will be added to account for the annular friction pressure in the well

(AFP = 1.45 ppg for Well B). Therefore, BHPDynamic will be 11.2 ppg, which exceeds the

differential sticking pressure (e.g., Pds = 10.17 ppg, as observed during operations) shown

in Figure 1. As an alternative, Managed Pressure Drilling (MPD) is recommended to be

evaluated to keep the bottom hole pressure constant and adhere to the safe drilling pressure

(Rehm et al., 2008).

3.3. INTEGRATION OF MEM AND MPD

If the Mishrif and Zubair Formations are to be drilled in the same hole section (as

is practice in the oilfield E) the MPD approach needs to account for the MEM results

55

(Alkamil, et al.1, 2017) such that the BHPDynamic is larger than the collapse pressure, Pc

(i.e., 9.75 ppg for Well B), determined by the MEM, and lower than the operational

differential sticking pressure, Pds (i.e., 10.17 ppg). As the conventional analysis has shown,

the BHPDynamic requires to lower the initial MW. This can be achieved using a closed loop

system such as MPD. Based on the selection of a lower initial MW (ranging from 8.1 to

8.7 ppg), Table 2 shows the representative calculations of the static and dynamic SBP (after

Table 1) for an exemplary circulation rate of 700 gpm. The SBP includes the calculations

of the AFP for each MW considered (calculations after Rehm et al., 2008; pp. 65-66).

Figure 3 shows that if the collapse pressure is not considered in the calculations (i.e., the

pore pressure of the Zubair Formation represents the minimum mud weight), a wider mud

weight window is suggested, which does not indicate the risk of borehole collapse (red

area). The combination of the MEM and the MPD approach shows that an acceptable mud

weight window can be determined (green area) accounting for both mitigating collapse and

differential sticking; i.e., BHPDynamic is kept in-between the collapse pressure and the

differential sticking pressure (dashed red line in Figure 3).

Table 2. Annular Pressure Data under static and dynamic conditions. It needs to be noted

that the AFP can be adjusted by using different circulation rates, which in turn would result

in different surface back pressure magnitudes (i.e. AFP = 476.5 psi based on a circulation

rate of 700gpm).

PStatic = 9.75 ppg BHPDynamic = 10.16 ppg

MW (ppg) Static SBP (psi) Dynamic SBP (psi) Comments

8.1 741.7 274.2

8.2 696.8 229.3

8.3 651.8 184.3

8.4 606.9 139.4

8.5 561.9 94.4

8.6 517 49.5

8.7 472 4.5

8.8 427 -40.5 Not Possible

56

Figure 3. Combined mud pressure window obtained from the 1D MEM derived collapse

pressure and the surface back pressure adjusted MPD approach. By using MPD the

dynamic BHPDynamic remains below the differential sticking pressure (compare

conventional the dynamic BHPDynamic in Figure 1).

4. DISCUSSION

The presented approach by integrating the MEM derived collapse pressure into the

constant bottom hole pressure MPD approach has shown that for oilfield E in southern

Iraq, MPD represents a viable approach to mitigate the prevalent drilling problems (i.e.,

prevent collapse, differential sticking) and thus minimize NPT (Foster and Steiner, 2007;

Kulikov et al., 2014; Alkamil et al., 2017). The following sections discuss the implications

of the MPD approach with respect to its operational capabilities such as rate of penetration,

real time adjustments, hole cleaning, surge and swab effects. It needs to be stated that while

it is clear that alternative approaches such as underbalanced drilling (UBD) can also resolve

drilling-hazard problems (especially differential sticking) this approach is not viable for

Oilfield E as overbalanced drilling is required due to the prevalent collapse problems (i.e.,

the collapse pressure is larger than the pore pressure).

57

4.1. MPD REDUCED MUD WEIGHT EFFECT ON DRILLING RATE

The rate of penetration (ROP) can be affected by many different parameters such

as operating conditions, rock properties and formation characteristics, bit type and most

importantly drilling fluid properties (Kulikov et al., 2014; Mitchell et al., 2017). The

drilling fluid properties that affect the rate of penetration are its solids contents, filtration

characteristics, rheological flow properties, chemical composition and density. As the

drilling fluid density increases, the BHP will increase as well and lead to an increase in the

differential pressure (i.e., overbalance) between the formation fluid pressure and the

drilling fluid pressure. As a result of overbalance change, the ROP is affected as well, as

demonstrated by Bourgoyne et al. (1974) based on multiple regression analysis of the

drilling data obtained over short intervals. The simplified version of the derived expression

is shown in eq.1, relating changes in mud density to change in penetration rates (Cheathem

et al., 1985; Bourgoyne et al., 1991; Nauduri et al., 2009; Patel et al., 2013; Kulikov et al.,

2014).

ROP2 = ROP1e(∆P) (1)

where: ROP is the rate of penetration (m/h), is a coefficient related to the rock

properties and P is the overbalance pressure (psi).

As conventional drilling approaches require adjusting the mud density, an increase in

P results, which in turn decreases the resulting ROP. In contrast, by keeping the

overbalance pressure close to the collapse pressure, MPD will significantly improve the

rate of penetration from ~36 to ~50 ft/hr.

58

4.2. MPD REAL-TIME BHP CONTROL

Pressure stability in heterogeneous and depleted formations such as the Mishrif

Formation in oilfield E, is a key success parameter in closed loop pressure drilling

operations. One operational target of MPD is to eliminate or minimize any type of pressure

anomaly that may cause the BHP to fall outside its safe operating window (i.e. for oilfield

E; Pc < BHP < Pds). Common pressure spikes are caused either by changing stress or pore

pressure conditions surrounding the wellbore, or while adjusting the rig pump rate, which

occurs during connections and trips such as swab or surge (Dupriest et al., 2005; Azim et

al. 2011; Imtiaz et al., 2017).

To keep the bottom hole pressure constant and within its safe operating window,

MPD systems are equipped with the dynamic annular pressure control (DAPC) facility,

which is an example of an automated back-pressure control system. It is designed to keep

the bottom hole pressure constant while drilling (even if the drilled section passes through

two formations with different pore pressures such as oilfield E with the Zubair Formation

and the Mishrif Formation), for both scenarios of rig pumps ON or OFF, respectively. The

DAPC main components are: choke manifold, back-pressure pump, hydraulics model, and

integrated pressure manager. The DAPC moves the choke of the MPD system to the

required position providing the required back pressure to keep the BHP constant while

drilling in oilfield E. It adheres to the stable conditions, by maintaining the bottom hole

pressure at the set point (i.e. Pc < BHP < Pds) without pressure spikes through providing the

dynamic SBP (Rehm et al., 2008).

The MPD setup enables to achieve real time automated control on accurate bottom

hole pressure calculations by equipping the DAPC with a programmable logic controller

(PLC) (Hannegan 2011). The PLC system collects pressure measurements and feedback

59

from the DAPC system components. By connecting the PLC hardware to the hydraulic

model software (i.e. MicrofluxTM), and by linking it to the data acquisition network,

manifold and pump, and machine/human interface, real time monitoring and controlling of

the bottom hole pressure can be achieved. This allows for real-time, short delay

adjustments to suddenly occurring bottom hole pressure variations. This represents a

significant advantage over conventional drilling approaches where surface based mud

density adjustments and its effect on the circulating fluid column take considerably more

time (Carlsen et al., 2013; Vega et al., 2018).

4.3. HOLE CLEANING

70% of NPT is caused by stuck pipe, with a significant reason being insufficient

hole cleaning (Massie et al., 1995; Zhang et al., 2017). Although hole cleaning has been a

major issue in the oil industry (Kenny et al., 1996), the problem becomes worse when

drilling deviated wells (Li and Walker, 2001). In these highly deviated wellbores, the mud

alone cannot clean the borehole, but many other parameters must be considered (Li and

Walker, 2001; Zhang et al., 2017) as shown in Table 3.

Table 3. Parameters considered to determine hole cleaning efficiency.

Flow rate Cutting size

Rotary speed Cutting dispersion

Penetration rate Wellbore angle

Pipe rotation and reciprocation Turbulent or laminar flow

Drill pipe diameter Wellbore stability

Mud rheology Washouts

Mud weight

60

In underbalanced drilling (UBD), efficient hole cleaning has long been recognized as

one of the key success drilling parameters (Tian, Medley, 2000; Li and Walker, 2001),

however, it is even more significant with MPD (Tian et al, 2007; Nauduri et al. 2009),

especially when lower mud densities are used.

In this study, the MPD approach can reduce the friction by decreasing the

circulation rate (Rehm et al., 2008) to avoid exceeding the upper pressure limit (i.e., Pds).

In addition by using a reduced mud weight (resulting in an increased cuttings loading), a

manifold, negative effect on hole cleaning efficiency is the consequence. This in turn may

increase the chance of high torque and drag, pack-off in the annulus, and eventually result

in stuck pipe, etc (Kenny et al., 1996; Nauduri et al. 2009; Ozbayoglu et al., 2010).

Moreover, for cases where Pc represents the lower limit of the pressure window, as in the

Mishrif Formation, the early indicators of a hole cleaning problem may be misdiagnosed

as a wellbore stability problem (Nauduri et al. 2009).

Figure 4. Cleaning efficiency vs. cutting size and cutting density. For Well A in Oilfield E,

the presented MPD approach yields a cleaning efficiency of 90% based on reported cutting

densities of 15ppg and average cutting sizes of ~0.15in.

61

In order to evaluate if the MPD approach suggested for the wells in Oilfield E

represents hole cleaning challenges, a commercial software simulation (i.e., MicrofluxTM;

Santos et al., 2007) is used. Using the operational drilling parameters for Well A (which

experiences collapse), the simulation results show that the most significant parameters

affecting the cleaning efficiency are the mud flow rate, cutting size, and cutting density.

By keeping the MPD flow rate constant and controlling the BHP by adjusting the SBP

(Table 2), the cleaning efficiency behavior is calculated for different cutting sizes and

densities at a specific flow rate (700 gpm) as a shown in Figure 5.

Cutting densities range from 10 ppg to 35 ppg, and the range of the cutting size is

from 0.1 to 3 inches. For each case, the software estimates the cleaning efficiency

(calculations based on Santos et al., 2007) and returns a singular cleaning efficiency value.

By combining all possible cases, Figure 5 presents a guideline to hole cleaning efficiency

applicable to every well. For Well A in the Mishrif Formation cutting densities are ~15 ppg

based on the available drilling reports. The cutting size has not been reported but for an

average size of 0.15 in. (Well A drilling operator; personal communication), the resulting

efficiency is ~90%, making the MPD approach viable. This estimation helps the drilling

engineer to manipulate the other drilling parameters to control the cleaning efficiency.

Hence, it is important to mention the cleaning efficiency chart in Figure 5 can be generated

for each individual planned well, and can then be utilized for the real-time estimation of

the hole cleaning efficiency, while drilling.

4.4. MPD SURGE AND SWAB EFFECTS ON BHP

For wells where collapse failure is a risk, the drill string dynamic motion resulting in

pressure variations during swab needs to be carefully monitored in order to remain in the

62

safe MPD operational window (Lubinski et al., 1977; Mitchell 1988; Samuel et al., 2002).

As example, for evaluating the MPD feasibility in Oilfield E, the swab related pressure

drop may be lead to collapse failure for a small period of time, and if the hole cleaning is

not sufficient enough, stuck pipe may occur. In addition, surge related pressure increase

may lead to differential sticking. Therefore, it is necessary to precisely control the annular

pressure throughout the wellbore, while drilling, tripping, and connection (Rehm et al.,

2008; Samuel and Lovorn, 2016). This is particularly important for the Mishrif Formation,

as it has been shown that drilling beyond the mentioned narrow acceptable mud range for

even short time has historically led to expensive wellbore problems.

This section presents a swab/surge model, whereby the BHP (i.e., PHydrostatic; pumps

are OFF) can be predicted and adjusted automatically using DAPC (Rehm et al., 2008;

Samuel and Lovorn, 2016). To estimate the pipe movement associated maximum pressure

fluctuation, Lapeyrouse (2002) proposed a steady-state models with the assumption of

power-law fluid. In this model, the maximum fluid velocity and drilling fluid properties

are calculated to estimate maximum pressure fluctuations.

The drilling fluid properties are given in eq.2 and eq.3:

n = 3.32 log (R600

R300) (2)

K =R300

511n (3)

Where R300 and R600 = relevant rotational viscometer readings; n = power-law constant;

and K = consistency index. And the drilling fluid maximum velocity can be estimated as

in eq. 4:

63

vm = [0.675 +1.5dp

2

dh2 − dp

2] (4)

Where vm = drilling fluid maximum velocity, ft/sec.; dp = drill pipe outside diameter, in.;

dh = hole diameter, in.; vp = drill pipe maximum velocity, ft/sec.

The pressure fluctuation can then be estimated using eq. 5:

Pms = (144vm

dh − dpX

2n + 1

3n)

n

XKL

300(dh − dp) (5)

Where pms = maximum swab/surge pressure, psi; L = drill string length, ft.

Figure 5 shows that the Mishrif Formation BHP while tripping the drill pipe inside

the wellbore is the summation of PHydrostatic and Pms. However, when tripping the drill pipe

outside of the wellbore, Pms is subtracted from the mud weight hydrostatic pressure.

Therefore, due to the swab/surge effect on the bottom hole pressure, the ranges of the MPD

reduced mud weight mentioned in Section 3.3 have to be adjusted to 8.2 ppg < MW < 8.4

ppg. These mud weights allow for maximum pipe velocities ranging between 120-300

ft/min. For example, for a maximum pipe velocity of 240 ft/min., the acceptable mud

weight ranges are 8.1 < MW < 8.5 ppg (orange line in Figure 5).

5. SUMMARY AND CONCLUSIONS

For Oilfield E in southern Iraq, where two reservoirs, the Mishrif Formation and the

Zubair Formation, each having different stress conditions, are commonly drilled in the

same section, resulting in a narrow acceptable mud weight window. Conventional drilling

approaches have resulted in significant occurrences of NPT due to borehole collapse and

differential sticking.

64

Figure 5. Swab/Surge Effect on the BHPDynamic when the rig pumps are OFF. a) For the

string trip in (surge effect), BHPDynamic will be increased directly for increasing vp. While

accounting for this effect BHPDynamic has to be kept in the safe range by adjusting the

surface back pressure: i.e., BHPDynamic = PStatic + SBPStatic + Surge Effect (which adds

additional pressure) < Pds (i.e. 10.17 ppg). E.g., in case of MW=8.5 ppg (green line), vp

should be less than 260 ft/min. b) For the string trip out (swab effect), the BHPDynamic will

be decreased directly with vp increasing. While accounting for this effect BHPDynamic has

to be kept in the safe range by adjusting the surface back pressure: i.e., BHPDynamic = PStatic

+ SBPStatic - Surge Effect (which reduces the bottom hole pressure) < Pc (i.e. 9.7 ppg). E.g.,

in case of MW=8.1 ppg (brown line), vp should be larger than 220 ft/min.

In this study, by integrating the collapse pressure calculated based on a MEM and

an MPD approach, the narrow mud weight window conditions can be handled. By reducing

the initial mud weight from 9.16-10.17 ppg to 8.2-8.4 ppg and adjusting the dynamic

bottomhole pressure via the dynamic surface back pressure both the risk of wellbore

collapse and differential sticking be mitigated. For the Mishrif Formation the provided

surface back pressure enables to maintain Pc < BHP < Pds (i.e., when the rig pumps are

OFF PStatic > Pc; however, when the rig pumps are ON Pc < BHPDynamic < Pds). The mud

weight calculations presented also show that for the Mishrif Formation, conventional

65

drilling approaches fail to provide acceptable mud weights and differential sticking is the

likely result. In addition to be able to navigate the narrow mud weight window conditions,

the presented MPD approach yields several operational benefits, such as (1) an increased

rate of penetration (compared to conventional drilling); and (2) the ability for real-time

bottom hole pressure management (utilizing a programmable logic controller and the

dynamic annular pressure control (DAPC) facility to adaptively keep the bottom hole

pressure constant by providing the surface back pressure thus compensating pressure

fluctuations while drilling).

The presented case study for the Mishrif Formation also shows that other important

processes during drilling operations such as (1) surge and swab related pressure

fluctuations can be safely handled using MPD; and (2) hole cleaning efficiency for average

is maintained at 90% using MPD.

ACKNOWLEDGEMENTS


and Scientific Research (MOHESR) - University of Basrah for sponsoring Ethar Alkamil

to finish this work. Husam Abbood would like to thank Basrah Oil Company for the

permission to publish the data of the Mishrif Formation.

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72

APPENDIX

BOREHOLE PROBLEMS.

1. COLLAPSE PRESSURE

The minimum mud weight, i.e. also termed collapse pressure, is determined based

on the compiled 1D MEM for all possible wellbore trajectories (Peska and Zoback, 1995;

Alkamil et at 2018). The equations for the calculation of the required tangential wellbore

stresses in an arbitrarily oriented wellbore are given in detail in (Aadnoy, 1989; Peska and

Zoback, 1995; Zoback, 2010; Alkamil et at 2018) and are therefore not repeated here. The

Mogi-Coulomb failure criteria is used to evaluate the risk of borehole collapse. The Mogi-

Coulomb failure criterion was first introduced by Al-Ajmi and Zimmerman (Al-Ajmi and

Zimmerman, 2005). This failure criterion considers the effect of the intermediate principal

stress. The Mogi-Coulomb criterion can be formulated in Eq. 1.

τoct = κ + mσoct (1)

Where τoct and σoct are the octahedral shear and normal stresses, defined as in Eq. 2.

τoct =1

3√(σ1 − σ2)2 + (σ1 − σ3)2 + (σ2 − σ3)2

σoct = 1

3(σ1 + σ2 + σ3) (2)

τoct = a + bσm,2

σm,2 =σ1 + σ3

2

a =2√2

3So cos ∅

b =2√2

3sin ∅

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2. DIFFERENTIAL STICKING.

Differential sticking can result when pressure from an overbalanced mud column

acts on the surface area of the drill string against a filter cake deposited across a permeable

formation. The surface area of the pipe that is embedded into the mud cake has a pressure

equal to the pore pressure acting from one direction while the hydrostatic pressure acts in

the other direction. When the hydrostatic pressure in the wellbore is higher than the

formation pressure, the pressure differential forces the pipe towards the borehole wall

(Rehm and et al., 2008). The best method to limit the risk of differential sticking is by using

the minimum mud weight (Helmic and Longgley, 1957).

3. 1D MEM PARAMETERS FOR MISHRIF FORMATION

Table 1. 1D MEM parameters for Mishrif Formation (after Alkamil et al, 2018a).

Elastic parameters

Assumption 0.25-0.3

Rock strength properties

UCS Limestone empirical eq. 29 - 99.5 Chang et al., 2006

φ Empirical equation 21°- 25° Plumb, 1994

To Extended leak-off test 4.0-8.0 Torres et al., 2003

In-situ stresses and pore pressure

σv (MPa) Bulk density log 59.6

σh (MPa) Mini-frac test (ISIP) 32.0 Zoback et al., 1985

σH (MPa) Circumferential model 53.6 Zoback & Peska, 1995

σH orientation

degree Image Log 51.0 Zoback, 2010

Pp (MPa) Repeat formation tests 26.0 Stewart & Wittmann, 1979

74

III. A COLLAPSE VOLUME LOG ESTIMATION BASED ON IMAGE

PROCESSING

Alkamil, Ethar, Eckert, A., Flori, R., Abbas, A.


(Submitted in Journal of Petroleum Science and Engineering)

ABSTRACT

Wellbore collapse as a result of severe borehole breakouts represents a major problem

in many drilling operations. In order to quantify the risk associated to wellbore collapse a

reliable estimate of the collapse volume is necessary. In this study, a novel approach

determining the area/volume of collapse failure by using an image processing approach is

presented. The presented approach is independent of any failure criterion and very

versatile. Based on the failure criterion applied to determine borehole collapse, the detailed

2D are of collapse can be determined therefore limiting the degree of underestimation

compared to analytical techniques which assume a triangular breakout geometry. The

method shows a good agreement with breakout depths obtained from caliper logs obtained

from the Mishrif and Zubair Formations in Southern Iraq. For hydrocarbon fields where

Mechanical Earth Modeling (MEM) approaches capable of predicting the spatial

distribution of horizontal stresses exist, the presented image processing approach is utilized

to generate an automated log of collapse volume while drilling. Based on this log, mud

pressure adjustments can be undertaken while drilling a new well based on the predicted

collapse volume. This can help the drilling engineers in evaluating the mud weight effect

on the hole cleaning efficiency to avoid stuck pipe problems. In addition, knowledge of the

collapse volume provides better estimates on the required mud and cement volumes.

75

1. INTRODUCTION

The International Association of Drilling Contractors (IADC) defines the drilling

window as “the difference between the maximum pore pressure and the minimum effective

fracture pressure” (API, 2013) and does not consider the collapse pressure which can be

higher than the pore pressure. However, it has been observed that wellbore collapse (i.e.

shear failure of the borehole wall resulting in excessive breakouts) and associated stuck

pipe problems are a major reason for a non-productive time during drilling operations

(Howard and Glover, 1994; Salminen et al., 2017).

In order to assess and predict wellbore stability and to design appropriate mud pressures

to prevent collapse, Mechanical Earth Models (MEM) represent a common approach (e.g.,

Goodman and Connolly, 2007; Kristiansen, 2007; Fjaer et al., 2008; Gholami et al., 2014;

Alkamil et al., 2017b). MEMs include key parameters including formation strength

properties, in-situ stresses, and pore pressure. Once the MEM is validated, it can be used

to predict applications such as wellbore stability (Cheatham, 1984; Kaushik et al., 2016;

Alkamil et al., 2018). While the general conditions for collapse occurrence are understood

and can be calculated analytically based on the MEM, which in turn can be used to

determine the breakout width analytically (Zoback et al., 1985), no analytical solution for

the detailed breakout geometry (especially the breakout depth) exists (Moos et al., 2007).

Knowledge about the severity of breakouts (i.e., WBO > 90o for vertical wells and WBO >

60o for deviated wells; Zoback, 2010) and the resulting breakout geometry provide/enable

estimates for the collapse volume which represents important knowledge for drilling

operations (Alkamil et al. 2018). The severity of breakouts can be assessed post-drilling

either by numerical modeling approaches accounting for plastic failure (Herrick and

76

Haimson, 1994; Chatterjee and Mukhopadhyay, 2003; Wu et al., 2016; Li et al., 2017), or

based on wireline logging operations using either 4-arm or 6-arm caliper logs (Atiunson

and Pointer, 1975; Wagner et al., 2004). One recently presented analytical method uses the

caliper reading as an indicator of the breakout depth and approximates the caving area to

the area of a triangle (Escobar et al., 2014). However, as also shown in their study, when

compared to a numerical model predicting the area of borehole failure, a significant

discrepancy of ~27% between the analytical and numerical solution remains.

As shown by Zoback et al. (1985) and Moos et al. (2007) the actual shape of the breakout

area in a radial cross-section is different from a triangle, and the triangle height can be

estimated from the caliper log, which is after drilling the hole section. However, an

analytical description of the breakout area is not presented (Moos et al., 2007; Zoback,

2010). This study presents an approach to estimate the area of collapse failure in a 2D radial

cross section by using an image processing approach, rather than approximating the

collapse area by a simplified geometrical shape (Alkamil et al., 2017a). Once the 2D area

of collapse is determined, a vertical integration enables to estimate the resulting collapse

volume for a well. Quality criteria for the estimation of the collapse volume while drilling

based on the proposed image processing approach will be obtained by comparison to the

collapse volume predicted by the caliper logs for the evaluated formations in this work.

This approach is useful to provide a proactive real-time geo-drilling geomechanical model,

which, based on input parameters of the wellbore stresses (i.e., σH, σh, PP, PM), can estimate

the required minimum mud pressure to minimize the resulting collapse volume. Active

geo-drilling knowledge about the expected collapse volume can help in predicting cutting

volumes for hole cleaning optimization, predict expected cement volumes, and predict

77

requirements for the installation of completions such as expandable screens for which

maintaining hole size within a specific range is an absolute requirement (Moos et al., 2007).

The proposed methodology is applied for two data sets obtained for the Mishrif Formation

and the Zubair Formation in Southern Iraq (both wells experience wellbore collapse and

associated stuck pipe problems). Quality criteria for the estimation of the collapse volume

while drilling based on the proposed image processing approach will be obtained by

comparison to the collapse volume predicted by the caliper logs for both formations.

2. METHODOLOGY

The proposed image processing approach to assess wellbore collapse (i.e. shear failure)

is based on parameters obtained from a MEM accounting for well geometry, in-situ stresses

and pore pressure, and rock physical properties. The various MEM input parameters and

their source of measurement are listed in Table 1. Figure 1 shows the depicted flowchart

of the MEM approach including MEM input parameters, model derivatives, and outcomes.

The MEM determines the resulting wellbore stresses (i.e., hoop, radial and axial stresses:

σƟƟ, σrr, and σzz) based on the classic equations for stresses around a cylindrical hole for

both vertical (Kirsch, 1898) and deviated wells (Peska and Zoback, 1995). Based on the

rock strength properties applied the area of collapse can be calculated. In order to provide

a proactive geo-drilling assessment tool for the collapse volume, while many necessary

MEM input parameters can be obtained pre-drilling or during measurements while drilling

(see Table 1) the horizontal stress magnitudes need to be determined from offset well data

(or from a nearby field), as their determination/estimation is only possible post-drilling

(Zoback et al., 1985; Bell and Babcock, 1986; Mastin, 1988; Tingay et al., 2011).

78

Table 1. MEM input parameters and source of measurement.

MEM input parameters Measurement/Source Comment

Wellbore

geometry

Azimuth

Planned/executed wellbore trajectory Can be obtained either pre-

drilling in the planning phase,

or while drilling

Inclination

MW

In-situ state of stress

and Pore

Pressure

V Integrated density log Calculated while drilling

h

Minifrac test; extended leak-off test Post-drilling measurement preferably from offset well

Analytically from uni-axial strain model

(𝜎ℎ =𝜈

1−𝜐(𝜎𝑉 − 𝑃𝑃) + 𝑃𝑃), Poisson’s

ratio from sonic log;

Estimated while drilling: based

on assumption that no

horizontal strain is present

Based on poro-elastic model:

Estimated while drilling: based on assumption that horizontal

strain is known:

H

Based on poro-elastic model:

Estimated while drilling: based on assumption that horizontal

strain is known:

Stress polygon method (Zoback et al., 1986; Moos and Zoback, 1990)

Post-drilling estimate

Estimates based on borehole failure

occurrence Post-drilling estimate

H,h

orientation

DITF, Breakout analysis (Tingay et al., 2011)

Can be obtained post-drilling; can be obtained from nearby

field

Pore

Pressure

Empirical methods based on wireline logging measurements (e.g., resistivity

log)

Estimated while drilling

Repeat Formation test Determined post-drilling

Elastic

Parameters

E

υ

Dynamic Young’s modulus can be

obtained from sonic log

Dynamic Poisson’s Ratio can be obtained

from sonic log

Calculated while drilling

Rock Properties

Friction coefficient from empirical correlations based on porosity logs


Friction coefficient from tri-axial compression tests

Determined post-drilling

UCS

Unconfined compressive strength from empirical correlations based on porosity

logs


Unconfined compressive strength from tri-

axial compression tests Determined post-drilling

2 2

1 2

1 1 1 1h p x y

E EP

2 2

1 2

1 1 1 1H p y x

E EP

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Figure 1. Collapse volume log estimation approach workflow diagram.

2.1. ANALYTICAL VISUALIZATION OF FAILURE AREA

Since image processing principles are being used to evaluate the area of failure, the

following procedure is independent of the type of failure criterion applied to a stressed

wellbore environment. The following methodology is presented exemplary using the

Coulomb failure criterion (Bell and Cough, 1979; Zoback et al., 1985; Aadnoy and Looyeh,

2011) and is based on the required rock strength parameters listed in Table 1. The criterion

in principal stress coordinates can be expressed by Eq. (1):

𝜎1 = 𝑞𝜎3 + 𝑈𝐶𝑆 (1)

where UCS is the uniaxial compressive strength, q can be related to the cohesion, c, and

the angle of internal friction, ϕ, by Eq. (2) and Eq. (3):

80

𝑞 = 𝑡𝑎𝑛2(𝜋 4⁄ + 2⁄ ) (2)

𝑈𝐶𝑆 = 2𝑐 𝑐𝑜𝑠 (1 − sin ) (3)⁄

In this form the failure criterion can be used to calculate the required UCS for which

shear failure (i.e. collapse) is initiated (Figure 2; Zoback, 2010; Alkamil et al., 2017a). In

order to validate the predicted breakout area obtained from the image processing approach,

the breakout width is initially determined analytically based on the MEM. As stated by

Zoback (2010), breakout width is a critical parameter for assessing the severity of collapse.

If the width of the breakout, WBO, exceeds 90°, 60o for vertical and deviated respectively,

a severe breakout resulting in wellbore collapse occurs. The width of a breakout for the

Coulomb failure criterion can be determined analytically by:

2𝜃𝑏 = 𝑐𝑜𝑠−1(𝑈𝐶𝑆 + 𝑃𝑤 + 𝑃𝑝 − 𝜎ℎ − 𝜎𝐻 2𝜎ℎ − 2𝜎𝐻⁄ ) (4)

where 2θb ≡ -WBO, Pw is the differential pressure.

For the breakout area depicted in Figure 2, Eq. (4) predicts a breakout width of 116°

(Alkamil et al., 2017a).

Figure 2. Inferred area (outlined with black lines) of collapse failure for UCS=45 MPa.

WBO is the breakout width and DBO the breakout depth (Alkamil et al., 2017a).

81

2.2 PREDICTION OF COLLAPSE AREA AND VOLUME USING IMAGE

PROCESSING

While the width of breakouts can be determined analytically, an analytical solution for

the breakout depth, to the authors’ knowledge, does not exist currently. However, the area

of collapse failure visualized in Figure 2 (based on the Coulomb failure criterion) can be

calculated by using image processing approach. A Matlab algorithm is applied which

follows the flowchart depicted in Figure 3. It first calculates the total area of the near

wellbore zone considered in the results diagram. Then, the total number of colored pixels

within the results diagram are counted. In the third step, the number of pixels along the

maximum extent of failure enables to determine the breakout depth. The fourth step counts

the number of colored pixels exceeding the threshold value for the applied value of UCS.

Based on this count, the area of the collapse failure can be simply calculated. In addition,

the width of the breakout, WBO, can be determined. For the example shown in Figure 2, the

image processing approach matches the analytically determined breakout width very

closely, i.e. 111° compared to 116° (Alkamil et al., 2017a).

If a severe breakout is predicted (i.e., WBO > 90o for vertical wells and WBO > 60o for

deviated wells; Zoback, 2010), the actual volume of collapse is of interest, as it may exceed

the hydraulic lifting capacity of the mud circulation and result in the stuck pipe. If UCS is

known for a specific formation, the collapse volume for a vertical section of a well can be

calculated by vertically integrating wellbore sections showing collapse. In addition, the

sensitivity of the expected breakout width and collapse volume to changes in the mud

pressure can be considered. This will be discussed for two case studies shown in the

following results analysis.

82

Figure 3. Flowchart indicating the calculation of the collapse area.

3. RESULTS

The presented image processing approach is applied to two case studies from oilfields

E and F in Southern Iraq, for which considerable collapse problems have been recorded

(Abbas et al., 2018; Alkamil et al., 2018). Oilfield E is considered one of the largest oil and

gas fields in the Middle East, with more than thirteen carbonate and sandstone reservoirs.

The main reservoir is the Mishrif Formation, a highly heterogeneous limestone formation

(Aqrawi et al., 2005; Jassim and Goff, 2006; Alkamil et al., 2017b) at 2393 m true vertical

depth (TVD) to 2630 m TVD (i.e., 237 m thickness). Meanwhile, Oilfield F has the Zubair

Formation which measures approximately 400–500 m in average gross vertical thickness.

The Zubair reservoir is composed mainly of shale and sandstone intercalation, with minor

83

streaks of limestone and siltstone. The following paragraphs compare the image processing

approach to data obtained from caliper logs for the mentioned oilfields.

3.1. MISHRIF FORMATION

The Mishrif Formation geomechanical assessment for the deviated wellbore profile

used in the Mishrif Formation (Aqrawi et al., 2005; Jassim and Goff, 2006) has been

presented by Alkamil et al. (2018) and is not repeated here. Exemplary 1D MEM

parameters obtained from the logging data used as input for the image processing approach

is shown in Table 2.

Table 2. Exemplary 1D MEM parameters obtained from wireline logs and various

formation tests for the Mishrif Formation. Data obtained from Alkamil et al. (2018).

Elastic parameters

υ 0.25-0.3

Rock strength properties

UCS Limestone empirical eq. 29 - 99.5 Chang et al., 2006

ϕ Empirical equation 21°- 25° Plumb, 1994

To Extended leak-off test 4.0-8.0 Torres et al., 2003

Pore pressure and in-situ stresses

σv (MPa) Bulk density log 59.6

σh (MPa) Mini-frac test (ISIP) 32.0 Zoback et al., 1985

σH (MPa) Circumferential model 53.6 Zoback and Peska, 1995

Pp (MPa) Repeat formation tests 26.0 Stewart and Wittmann,

1979

Figure 4a shows the wellbore diameter data obtained from the 6-arm caliper log data

recorded in the Mishrif Formation. Figure 4b shows the calculated borehole diameter

obtained from the image processing approach. The data shows a close visual match of the

two borehole profiles. The vertical integration of the borehole diameter data resulting in

84

the collapse volume predicts a collapse volume of 0.156 m3 for the caliper log data and

0.133 m3 for the image processing. These values represent an 85% agreement.

Figure 4. Mishrif Formation-Shale rock (a) wellbore diameter data obtained from the 6-

arm caliper log data (b) calculated borehole diameter obtained from the image processing

approach.

3.2 ZUBAIR FORMATION

An integrated workflow was applied to conduct a geomechanical model analysis for

drilling through the Zubair Formation. The first step in generating the 1D MEM was

collecting the open hole wireline logs for the Zubair Formation. The developed models

were further calibrated using all the available data, such as drilling observations, mini-frac

85

tests, laboratory measurements of rock mechanical properties, etc. The model parameters

used are presented by Abbas et al. (2018).


recorded for the shale interval (3400 m – 3475 m) in the Zubair Formation. Figure 5b shows

the calculated borehole diameter obtained from the image processing approach. The data

shows some correlation of the borehole diameter. The vertical integration of the borehole

diameter data resulting in the collapse volume predicts a collapse volume of 1.322 m3 for

the caliper log data and 0.595 m3 for the image processing, resulting in a 48% agreement.

Figure 5. Zubair Formation-Shale rock (a) wellbore diameter data obtained from the 6-arm

caliper log data (b) calculated borehole diameter obtained from the image processing

approach.

86


recorded for the sandstone interval (3325 m – 3375 m) in the Zubair Formation. Figure 6b

shows the calculated borehole diameter obtained from the image processing approach. The

vertical integration of the borehole diameter data resulting in the collapse volume predicts

a collapse volume of 0.17 m3 for the caliper log data and 0.149 m3 for the image processing.

These values represent an 88% agreement.

Figure 6. Zubair Formation-Sand rock (a) wellbore diameter data obtained from the 6-arm

caliper log data (b) calculated borehole diameter obtained from the image processing

approach.

87

4. DISCUSSION

Common 1D MEM approaches can be used to derive an analytical solution for the

width of breakouts (WBO in Figure 2), which in turn is used to assess the severity of

wellbore collapse (Moos et al., 2007, Zoback, 2010). The width of a breakout is expected

to remain stable with an increased radius of collapse failure as the breakouts tend to deepen

(Barton et al., 1988). In combination, breakout width and breakout depth determine the

geometry of the resulting breakout and are therefore necessary to determine collapse

volume. As an analytical solution for breakout depth does not exist the expected breakout

depth can only be determined post-drilling, either using of numerical modeling approaches

or by measurements obtained from 4- or 6-arm caliper logs. While numerical analyses are

impractical to assess collapse for a complete well profile (i.e. it would require a large

number of modeling runs for different well sections and would have to assume average

material properties for these sections), 4- and 6-arm caliper log analysis has been used to

determine wellbore collapse post-drilling for better cement volume estimations (Jarosiński,

1998). A disadvantage of using caliper log data is the inherent assumption of a triangular

breakout geometry (Escobar et al., 2014, which underestimates the actual area. In addition,

the post-drilling application is a disadvantage to drilling operations as a proactive real-time

assessment of the expected collapse volume represents important knowledge during

drilling operations. In this study, an image processing approach is proposed that utilizes

the wellbore stresses and rock strength properties obtained from the MEM to calculate the

area of collapse based on the Coulomb failure criterion. Under the condition that horizontal

stress magnitudes can be obtained pre-drilling from a nearby field or from an offset well,

all other MEM parameters can be obtained “proactively” during drilling operations (e.g.,

88

using logging while drilling; Table 1). These dynamic MEM properties can then be used

in real-time to continuously process images of wellbore cross sections (Figure 3) and then

determine the expected collapse volume during drilling. It is clear that the determination

of the required UCS to initiate failure (i.e. Figure 2) can be calculated for various different

failure criteria; thus the image processing approach is very versatile. The Coulomb criterion

is used exemplarily for this study due to its simplicity. The proposed method of using image

processing can, therefore, determine the breakout width/area for any failure criterion. This

is important as breakout width and depth, to the authors’ knowledge has not been

documented analytically for different failure criteria.

It is important to note that for cases where horizontal stress measurements do not exist,

the presented approach can still be applied based on the assumption of applying the uniaxial

strain model to estimate minimum horizontal stress (Sh). The uniaxial strain model enables

to obtain an estimate of Sh while drilling (based on the dynamic Poisson’s ratio (υ) and the

vertical stress (Sv); Table 1). In such cases, the maximum horizontal stress (SH) has to be

assumed as a ratio of Sh. It is clear that this introduces a significant degree of uncertainty

in the presented calculations. However, due to the difficulty in obtaining accurate SH

magnitude estimates (Bell, 1990; Zoback, 2010), this is an often encountered and well-

known limitation in many geomechanical and drilling applications (Zoback and Peska,

1995).

In order to compare the image processing approach to data obtained from caliper logs,

field data obtained for two different formations of two oilfields in Southern Iraq, the

Mishrif Formation, and the Zubair Formation have been presented. For the limestone beds

in the Mishrif Formation and the sandstone beds in the Zubair Formation, the results of the

proposed image processing approach to predict the wellbore collapse volume demonstrate

89

a precise width of breakout and collapse area when compared with caliper log data (i.e.

84% agreement for the Mishrif and 88% agreement for the Zubair Formation). This data

can subsequently be processed to provide an accurate wellbore profile (Figure 7) and the

vertical integration of the predicted breakout area can be used to determine the expected

collapse volume during drilling. It needs to be noted that the wellbore geometry predicted

by the image processing approach represents only a 48% agreement in the shale beds of

the Zubair Formations. Since the image processing approach is based on mechanical failure

criteria it cannot account for additional processes, such as chemical and thermal effects due

to sloughing (Mody and Hale 1993; Bybee, 2002; Zoback, 2010), affecting wellbore

geometry in shales. The complexity of shale response and the associated expected wellbore

geometry represents additional technological challenges which are beyond the scope of this

contribution and considered in a future study.

Finally, the presented image processing approach has the potential to be used as a

proactive geo-drilling approach, which helps in avoiding severe collapse failure and

decreasing associated uncertainties and non-productive time. For the example of the Zubair

Formation, Figure 7a shows the predicted wellbore profile for a mud weight of 10 ppg for

the wellbore section from 3400m-3500m. The resulting collapse volume is 1.2 m3. By

utilizing the image processing approach and proactively increasing the mud weight to 11

ppg breakout severity can be significantly limited to 0.73 m3. In addition to minimizing the

total expected collapse volume, the collapse volume can be related to the hydraulic lifting

capacity of the mud system in the wellbore and thus a better hole cleaning efficiency be

established.

90

Figure 7. Predicted wellbore hole profile while drilling using the image processing

approach.

5. CONCLUSIONS

The presented image processing approach provides an improved method to

quantitatively determine the geometry (i.e. the area) of the wellbore collapse based on the

applied failure criterion. Assumptions limited to a triangular area are not necessary,

therefore limiting the degree of underestimating the collapse area. The method shows a

good agreement with breakout depths obtained from caliper logs from the Mishrif and

Zubair Formations in Southern Iraq. By integrating the image processing approach to the

geomechanical input parameters obtained from a dynamic MEM, image processing has the

91

potential to be used as a proactive geo-drilling approach, which helps in avoiding severe

collapse failure and decreasing associated uncertainties and non-productive time. This

approach can help the drilling operation engineers in evaluating the mud weight effect on

stuck pipe problems in real-time based on the estimated collapse volume and the drilling

system hole cleaning efficiency. This, in turn, could lead to design criteria to select mud

properties to achieve the desired degree of hole quality for running modern completions.

In addition, knowledge of the collapse volume provides better estimates on the required

mud and cement volumes.

ACKNOWLEDGMENTS


and Scientific Research (MOHESR) - the University of Basrah for sponsoring Ethar

Alkamil to finish this work.

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96

SECTION

4. CONCLUSIONS AND RECOMMENDATIONS

4.1. CONCLUSIONS

In this dissertation, both a comprehensive evaluation of a Mishrif Formation

geomechanics parameters and historical drilled wells with stuck pipe and differential

sticking problems. The major findings of this research are summarized below:

The results obtained from the Mogi–Coulomb failure criterion, which are chosen as

the most indicative failure criterion to assess wellbore collapse, indicate that all wells

experiencing collapse and associated stuck pipe have been drilled with too low of a

mud weight.

Based on the horizontal stress orientations, this study recommends well azimuths

along the minimum horizontal stress direction with inclinations higher than 40o.

The 1D MEM approach can also be used to mitigate the occurrence of differential

sticking as observed for several wells in the Mishrif Formation.

The Mishrif Formation has a narrow mud weight window conditions between collapse

failure and differential sticking.

Adhering to the minimum mud weight predicted by the Mogi-Coulomb failure

criterion reduces the likelihood of wellbore collapse and also limits the potential for

differential sticking in the E oilfield in the Mishrif Formation.

The MPD approach reduces the initial mud weight from 9.16-10.17 ppg to 8.2-8.4

ppg and adjusting the dynamic bottomhole pressure via the dynamic surface back

pressure both the risk of wellbore collapse and differential sticking be mitigated.

97

For the Mishrif Formation the provided surface back pressure enables to maintain

Pc < BHP < Pds (i.e., when the rig pumps are OFF PStatic > Pc; however, when the

rig pumps are ON Pc < BHPDynamic < Pds).

The presented MPD approach yields several operational benefits, such as (1) an

increased rate of penetration (compared to conventional drilling); and (2) the ability

for real-time bottom hole pressure management (utilizing a programmable logic

controller and the dynamic annular pressure control (DAPC) facility to adaptively

keep the bottom hole pressure constant by providing the surface back pressure thus

compensating pressure fluctuations while drilling).

The presented case study for the Mishrif Formation also shows that other important

processes during drilling operations such as (1) surge and swab related pressure

fluctuations can be safely handled using MPD; and (2) hole cleaning efficiency for

average is maintained at 90% using MPD.

The presented image processing approach provides an improved method to

quantitatively determine the geometry (i.e. the area) of the wellbore collapse based

on the applied failure criterion.

The method shows a good agreement with breakout depths obtained from caliper

logs from the Mishrif and Zubair Formations in Southern Iraq.

By integrating the image processing approach to the geomechanical input

parameters obtained from a dynamic MEM, image processing has the potential to

be used as a proactive geo-drilling approach, which helps in avoiding severe

collapse failure and decreasing associated uncertainties and non-productive time.

98

This approach can help the drilling operation engineers in evaluating the mud

weight effect on stuck pipe problems in real-time based on the estimated collapse

volume and the drilling system hole cleaning efficiency.

This, in turn, could lead to design criteria to select mud properties to achieve the

desired degree of hole quality for running modern completions. In addition,

knowledge of the collapse volume provides better estimates on the required mud

and cement volumes.

4.2. RECOMMENDATIONS

The main objective of this work is to mitigate the differential sticking and stuck

pipe problems in Mishrif formation in field E. To achieve that, a real-time proactive

drilling system is developed to prevent these problems, using the integration between

MEM., MPD, and image processing approach. The future academic research potentials are

outlined to extend the current research in the following points:

Using the Mishrif Formation field core data to construct the geomechanical model.

Include the thermal and chemical effect in the Mishrif Formation mechanics earth

modeling to boast the results precision.

Improve the real-time geomechanical model presented in this work to develop an

event triggering system while drilling.

99

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VITA

Ethar Alkamil was born in Basrah, Iraq. He was received B.Sc. and M.Sc. degrees in

mechatronics engineering from University of Baghdad, Baghdad, Iraq, in 2005 and 2008.

After graduating with his BS degree, he worked as a quality control engineer at many

projects. After finishing his Master degree, he worked as a faculty member in the Computer

Engineering department of the Iraq University College, Basrah, Iraq. Finally, he worked as

a faculty member in the Petroleum Engineering department of the University of Basrah,

Basrah, Iraq.

He was granted a Ph.D. scholarship by the Ministry of Higher Education and Scientific

Research in 2013 to study at Missouri S&T. He has been a member of many professional

organizations, such as International Association of Drilling Contractors (IADC), 2017;

Center for Chemical Process Safety (CCPS), 2017; American Institute of Chemical

Engineers (AIChE), 2017; American Association of Rock Mechanics (ARMA); 2017;

Society of Exploration and Geophysics (SEG), 2017; American Association For Drilling

Engineers (AADE), 2015; Society of Petroleum Engineering (SPE), 2014; International

Electrical Electronics Engineers (IEEE), 2005; American Society of Mechanical Engineers

(ASME), 2005; and Iraqi Engineers Union (IEU), 2005.

He received hid Ph.D. in petroleum engineering from Missouri University of Science

and Technology, Rolla, MO, in December 2018. His research interests included

constructing geomechanical models; performing real time drilling data and MPD analysis,

assessment and control; and achieving greater wellbore stability. This work has been

extended into new areas such as drilling automation and control and machine learning. He

has published a number of journal and conference papers in these research areas.

A proactive drilling system to prevent stuck pipe and ...

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