Top Banner
INTERNATIONAL JOURNAL OF MODELING AND SIMULATION FOR THE PETROLEUM INDUSTRY, VOL. 9, NO. 1, APRIL 2015 21 UNISIM-I: Synthetic Model for Reservoir Development and Management Applications Avansi, Guilherme D. 1 and Schiozer, Denis J. 1 [email protected], [email protected] 1 Petroleum Engineering Department, Faculty of Mechanical Engineering, State University of Campinas, Campinas, Brazil Abstract. Several methodologies related to reservoir management applications were created recently. Many times, it is difficult to know the applicability of these methodologies when applied in real reservoirs that are unknown. In order to test them, a synthetic model was created (UNISIM-I-R) where the real reservoir is substituted by a reference model with known properties, so methodologies can be tested and compared. The reference model was built in a high resolution geocellular model, using public data from Namorado Field, Campos Basin, Brazil. The level of details is high to ensure that geological model is reliable in order to guarantee derivative suitable models for simulations that honor the used data. In addition, a simulation model with uncertainties (UNISIM-I-D) was created in a medium numerical grid resolution after the upscaling of a geomodel realization with some information of the reference model. The reference and simulation models were then submitted to production and injection scheme to compare the results. The focus of the application was an initial stage of the field management. The comparison between reference and simulation model was done to check the consistency and highlight the differences, using a base production strategy to ensure the quality and reliability of the UNISIM-I-R. The main result of this work is then a model which can be used for future comparative project solutions. Index Terms – Reservoir Characterization; Reservoir Simulation; Reservoir Management; Uncertainty Quantification. 1 INTRODUCTION Simulation of petroleum reservoirs refers to the construction and operation of numerical models whose objective is field performance estimation, i.e., oil recovery, under producing schemes. Petroleum fields can produce only once at high expenses and long period of time while a model can run many times at low cost and low computational time, being the most powerful predictive tool for reservoir engineering (Ertekin et al., 2001). The general idea of reservoir simulation studies is based on defining the objectives, collecting and analyzing data, approach selection, reservoir description and model design, matching the simulation model, running prediction cases and reporting. Usually, these steps can be more complex, mainly in real reservoirs. In an initial stage of a field man- agement, it is necessary to quantify the impact of uncertain- ties to mitigate risk (Hayashi et al., 2010). Methodologies to mitigate risk and optimize production are then proposed considering the results of numerical simu- lation models and the production prediction (Suslick and Schiozer, 2004). In order to test new methodologies, post- mortem studies could be conducted but this is not an usual procedure. A possible alternative is to test these methodolo- gies in synthetic models with characteristics of real reser- voirs. As a result of this, the proposed work focuses on gener- ating the UNISIM-I-R model, working as a real reservoir with known answer, to test and compare reservoir manage- ment applications with realistic problems by various re- search centers and, a simulation model with uncertainties, UNISIM-I-D, in an initial field management phase. It is applied a production strategy to UNISIM-I-D, in a natural working progress of reservoir studies to introduce the UNI- SIM-I-R through this application. In addition, differences between UNISIM-I-R and UNISIM-I-D models are high- lighted in order to check the consistency and reliability of the created models. 2 MODEL DATA The UNISIM-I-R and UNISIM-I-D models are based on the geomodel of Namorado Field, located in Campos Basin in Brazil. Some judgment is involved during the reservoir modeling because there are seldom enough data and gener- ally uncertainties, mainly in the UNISIM-I-D model due to its initial phase of field management. 2.1 Well Data Core descriptions and well logs of Namorado Field (public data released by the Brazilian National Petroleum Agency, ANP) were used to build UNISIM-I-R. The dataset contains well log information of 56 wells drilled through the upper Macaé formation (Meneses and Adams, 1990), a limited number of 18 wells have the Sonic data and the other 19 have the core description. This field is one of the main res- ervoirs in the Campos Basin, corresponding mostly to sand- stones of turbiditic origin (Guardado et al., 1989a; Guardado et al., 1989b; Guardado et al., 2000). This package contains, essentially, an assembly of five well logs: Gamma Ray (GR), Density (RHOB), Neutron (NPHI), Sonic (DT) and Resistivity (ILD) that describe physical property variations through the formation at the surroundings of correspondent wells, allowing the identification of the oil-bearing Namo- rado sandstone reservoir. Initial discrete data from electro- facies were informed by Petrobras, added to the available dataset. Manuscript received Oct. 16, 2013. Corresponding Author: Guilherme Daniel Avansi (E-mail: [email protected]).
10

Unisim Model Description

Dec 10, 2015

Download

Documents

Felipe Cadena

everal methodologies related to reservoir management
applications were created recently. Many times, it is difficult to
know the applicability of these methodologies when applied in real
reservoirs that are unknown. In order to test them, a synthetic
model was created (UNISIM-I-R) where the real reservoir is
substituted by a reference model with known properties, so
methodologies can be tested and compared
Welcome message from author
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
Page 1: Unisim Model Description

INTERNATIONAL JOURNAL OF MODELING AND SIMULATION FOR THE PETROLEUM INDUSTRY, VOL. 9, NO. 1, APRIL 2015 21

UNISIM-I: Synthetic Model for Reservoir

Development and Management Applications

Avansi, Guilherme D. 1 and Schiozer, Denis J.

1

[email protected], [email protected]

1Petroleum Engineering Department, Faculty of Mechanical Engineering, State University of Campinas, Campinas, Brazil

Abstract. Several methodologies related to reservoir management

applications were created recently. Many times, it is difficult to

know the applicability of these methodologies when applied in real

reservoirs that are unknown. In order to test them, a synthetic

model was created (UNISIM-I-R) where the real reservoir is

substituted by a reference model with known properties, so

methodologies can be tested and compared. The reference model

was built in a high resolution geocellular model, using public data

from Namorado Field, Campos Basin, Brazil. The level of details

is high to ensure that geological model is reliable in order to

guarantee derivative suitable models for simulations that honor the

used data. In addition, a simulation model with uncertainties

(UNISIM-I-D) was created in a medium numerical grid resolution

after the upscaling of a geomodel realization with some

information of the reference model. The reference and simulation

models were then submitted to production and injection scheme to

compare the results. The focus of the application was an initial

stage of the field management. The comparison between reference

and simulation model was done to check the consistency and

highlight the differences, using a base production strategy to

ensure the quality and reliability of the UNISIM-I-R. The main

result of this work is then a model which can be used for future

comparative project solutions.

Index Terms – Reservoir Characterization; Reservoir Simulation;

Reservoir Management; Uncertainty Quantification.

1 INTRODUCTION

Simulation of petroleum reservoirs refers to the construction

and operation of numerical models whose objective is field

performance estimation, i.e., oil recovery, under producing

schemes. Petroleum fields can produce only once at high

expenses and long period of time while a model can run

many times at low cost and low computational time, being

the most powerful predictive tool for reservoir engineering

(Ertekin et al., 2001).

The general idea of reservoir simulation studies is based

on defining the objectives, collecting and analyzing data,

approach selection, reservoir description and model design,

matching the simulation model, running prediction cases

and reporting. Usually, these steps can be more complex,

mainly in real reservoirs. In an initial stage of a field man-

agement, it is necessary to quantify the impact of uncertain-

ties to mitigate risk (Hayashi et al., 2010).

Methodologies to mitigate risk and optimize production

are then proposed considering the results of numerical simu-

lation models and the production prediction (Suslick and

Schiozer, 2004). In order to test new methodologies, post-

mortem studies could be conducted but this is not an usual

procedure. A possible alternative is to test these methodolo-

gies in synthetic models with characteristics of real reser-

voirs.

As a result of this, the proposed work focuses on gener-

ating the UNISIM-I-R model, working as a real reservoir

with known answer, to test and compare reservoir manage-

ment applications with realistic problems by various re-

search centers and, a simulation model with uncertainties,

UNISIM-I-D, in an initial field management phase. It is

applied a production strategy to UNISIM-I-D, in a natural

working progress of reservoir studies to introduce the UNI-

SIM-I-R through this application. In addition, differences

between UNISIM-I-R and UNISIM-I-D models are high-

lighted in order to check the consistency and reliability of

the created models.

2 MODEL DATA

The UNISIM-I-R and UNISIM-I-D models are based on the

geomodel of Namorado Field, located in Campos Basin in

Brazil. Some judgment is involved during the reservoir

modeling because there are seldom enough data and gener-

ally uncertainties, mainly in the UNISIM-I-D model due to

its initial phase of field management.

2.1 Well Data

Core descriptions and well logs of Namorado Field (public

data released by the Brazilian National Petroleum Agency,

ANP) were used to build UNISIM-I-R. The dataset contains

well log information of 56 wells drilled through the upper

Macaé formation (Meneses and Adams, 1990), a limited

number of 18 wells have the Sonic data and the other 19

have the core description. This field is one of the main res-

ervoirs in the Campos Basin, corresponding mostly to sand-

stones of turbiditic origin (Guardado et al., 1989a; Guardado

et al., 1989b; Guardado et al., 2000). This package contains,

essentially, an assembly of five well logs: Gamma Ray

(GR), Density (RHOB), Neutron (NPHI), Sonic (DT) and

Resistivity (ILD) that describe physical property variations

through the formation at the surroundings of correspondent

wells, allowing the identification of the oil-bearing Namo-

rado sandstone reservoir. Initial discrete data from electro-

facies were informed by Petrobras, added to the available

dataset.

Manuscript received Oct. 16, 2013. Corresponding Author: Guilherme

Daniel Avansi (E-mail: [email protected]).

Page 2: Unisim Model Description

AVANSI & SCHIOZER: UNISIM-I: SYNTHETIC MODEL FOR RESERVOIR DEVELOPMENT AND MANAGEMENT APPLICATIONS 22

2.1.1 Grouped Facies Estimation

The initial electrofacies description with eight types was

analyzed and regrouped into four facies groups as in Figure

1.

Figure 1. Illustration of the regrouped facies.

Electrofacies 1, 2, 3 and 4 illustrate a signature of sand-

stone units that are medium, massive, grained and conglom-

eratic (GR1 ≤ 70 and RHOB

2 ≤ 2.3); electrofacies 5 and 8

nearly indicate attributes of shaly sandstone composed of

alternating fine-grained, contorted and interbedded sand-

stone and shale (GR ≤ 70 and 2.3 < RHOB < 2.5); charac-

teristics of electrofacies 6 are near shale with alternating and

interbedded siltstone and shale (GR > 70); and electrofacies

7 practically reflects on carbonate composed of interbedded

shaly siltstone and marl and conglomeratic carbonate (GR ≤

70 and RHOB ≥ 2.5). This regrouped facies is designed as

facies 0, 1, 2 and 3 respectively, following the probabilities

calculated from well data and user-defined input (Galli and

Beucher, 1997). Figure 2 highlights the initial electrofacies

and the regrouped facies estimation histogram.

Figure 2. UNISIM-I-R – well logs: initial electrofacies and regrouped

facies distribution.

The class 0 is defined as reservoir facies with good res-

ervoir properties (porosity), classes 1 and 2 are possible

reservoir facies with medium reservoir properties and class

3 is non-reservoir facies with very low porosity magnitude.

1Recorded in API (American Petroleum Institute) units. 2Recorded in g/cm³.

2.1.2 Shaliness Estimation

In well logging, the commonest natural radioactivity (by

volume) is found in shales (clays), i.e., a high gamma ray

value frequently means shale. In this case, Atlas (1982)

apud Rider (1996) presented an empirical formula of Vsh

estimation based on relationship changes between younger

(unconsolidated) and older (consolidated) rocks. Taking into

account that the Namorado sandstone is dated as Albian-

Cenomanian Age (Winter et al., 2007), i.e., the sediments

are then older rocks, it is applied Dresser Atlas’ equation for

shaliness estimation expressed as

𝑉𝑠ℎ = 0.33 × [2(2×𝐼𝐺𝑅) − 1]

where IGR is gamma ray index (Brock, 1986; Hilchie, 1982)

and it is calculated through Equation (2).

𝐼𝐺𝑅 =𝐺𝑅𝑙𝑜𝑔 − 𝐺𝑅𝑚𝑖𝑛

𝐺𝑅𝑚𝑎𝑥 − 𝐺𝑅𝑚𝑖𝑛

where GRlog is gamma ray well log reading; GRmax is the

maximum value of gamma ray well log for shale facies;

GRmin is the minimum value of gamma ray well log for

sandstone facies. It is assumed GRmin ≈ 22 API units and

GRmax ≈ 125 API units for the sedimentary interval, corre-

sponding to the Namorado Formation.

2.1.3 Effective Porosity Estimation

In addition to the discrete information of facies, effective

porosity values related to pure sandstone (Øeff,0), i.e., facies

0, are calculated following the Equation (3) (Rider, 1996).

∅𝑒𝑓𝑓,0 = 𝜌𝑚𝑎 − 𝜌𝑏

𝜌𝑚𝑎 − 𝜌𝑓

The parameter ρb represents a reading in the density log; the

Namorado Formation has quartz matrix, then the matrix

density (ρma) is defined as 2.65 g/cm³; and the fluid density

(ρf) is assumed 1.10 g/cm³ (Rider, 1996).

In case of effective porosity estimation with some

shale in its structure (Øeff,1,2), i.e., effective porosity with

shale for facies 1 and 2, it is essential to correct the total

porosity with shaliness, considering a shale volume (Vsh) as

a correction factor in the succeeding equation, as follows in

Equation (4):

∅𝑒𝑓𝑓,1,2 = [(𝜌𝑚𝑎 − 𝜌𝑏

𝜌𝑚𝑎 − 𝜌𝑓

) − 𝑉𝑠ℎ × (𝜌𝑚𝑎 − 𝜌𝑠ℎ

𝜌𝑚𝑎 − 𝜌𝑓

)]

where ρsh is density well log in shale facies. Dewan (1983)

showed that a way of assessing the density at the shale point

ρsh is to take the difference between the neutron log and the

total porosity log at its maximum, i.e., max(Øni-Øti), where

Øni and Øti correspond to the ith

sample of the neutron and

the total porosity logs, respectively. In this study, the densi-

ty at the shale point is nearly of 2.65 g/cm³. The porosity of

3132.0 6 2

3132.2 6 2

3132.4 5 1

3132.6 1 0

3132.8 5 1

3133.0 5 1

3133.2 7 3

3133.4 7 3

3133.6 7 3

3133.8 5 1

3134.0 5 1

3134.2 2 0

3134.4 3 0

3134.6 3 0

3134.8 3 0

3135.0 3 0

3135.2 3 0

3135.4 3 0

3135.6 4 0

3135.8 4 0

3136.0 4 0

3136.2 4 0

3136.4 2 0

3136.6 1 0

3136.8 5 1

3137.0 5 1

3137.2 5 1

3137.4 5 1

3137.6 8 1

TVD

0

5

10

15

20

25

30

35

40

1 2 3 4 5 6 7 8

Rela

tive F

req

uen

cy,

%

Electrofacies

Initial Electrofacies Distribution

0

5

10

15

20

25

30

35

0 1 2 3

Re

lati

ve

Fre

qu

en

cy,

%

Facies

Grouped Facies Distribution

Page 3: Unisim Model Description

INTERNATIONAL JOURNAL OF MODELING AND SIMULATION FOR THE PETROLEUM INDUSTRY, VOL. 9, NO. 1, APRIL 2015 23

facies 3 was not computed because it is assumed a non-

reservoir facies.

2.2 3D Seismic and Horizons

The 3D seismic volume and 2D seismic lines are presented

in the public dataset from ANP. These data are used to de-

rive structural (reservoir boundary limit; top, bottom, se-

quences and faults) and sedimentological (zones and hori-

zons) information to reservoir characterization. Extra infor-

mation is added into this dataset, such as well markers,

being measured along the wells and giving a true vertical

depth at the well intersections with surface layer in time

units. In Figure 3, it is possible to observe the top, three

depositional sequences (Ponte and Asmus, 1978) and the

bottom that are estimated for the 3D seismic and converted

to depth units. Top and base had already identified in previ-

ous works such as Johann (1997) and Souza Jr. (1997). The

reference surface is defined as the reservoir top to represent

the orientation at the deposition time.

Figure 3. UNISIM-I-R – horizon modeling: top; sequences 3, 2 and 1; and

bottom.

The uncertainties associated with the structural model

can increase significantly, depending on the volume of

available data. For this reason, it is assumed no uncertainties

for the UNISIM-I-R model and some ones for the UNISIM-

I-D. This takes into account uncertainties in fault location

and mapping due to the initial stage of the field develop-

ment plan and the quality of the seismic acquisition. So, it is

supposed that the 3D seismic acquisition is able to map only

the main fault during this initial period because of his high

slip tendency. The UNISIM-I-R and UNISIM-I-D fault

models are shown in the Figure 4. The faults presented in

the reservoir area of each model are used to construct the

structural model.

(a) (b)

Figure 4. Fault modeling and reservoir boundary limit: (a) UNISIM-I-R

and (b) UNISIM-I-D.

3 UNISIM-I-R

The UNISIM-I-R is constructed based on structural, facies

and petrophysical model, using the available data presented

for Namorado Field. The structural part, including top, bot-

tom, reservoir limit and faults, was previously defined. In

this case, it is desirable to construct a synthetic reservoir

model, with known answer in a high resolution grid, to be

used in numerical simulation and management integrated

studies of petroleum reservoir, being possible to test and

compare methodologies by research groups.

On account of predicting reservoir performance by

small-scale heterogeneities, a grid cell resolution was de-

fined as 25x25x1 m (Figure 5), discretized into a corner

point grid with 326 x 234 x 157 cells (3,408,633 active total

cells). The regrouped facies log is scaled up to the grid reso-

lution previously defined without loss of heterogeneity.

(a) (b)

Figure 5. UNISIM-I-R: (a) Structural model and (b) Grid cell resolution.

Facies modeling is defined using a Sequential Indicator

Simulation (SIS) with vertical trend (Ravenne et al., 2002),

providing 3D realistic images of the reservoir heterogenei-

ties and being useful for controlling fluid flow and assessing

final uncertainties on the production (Seifert and Jensen,

1999). Facies simulations are constrained by regularized

well facies information (facies 0, 1, 2 and 3), calculated on

probabilities of each well and vertical proportion curve. SIS

is not strongly constrained in terms of geological bodies. On

the other hand, this is solved for using variogram models.

Omni-directional variograms are developed for each facies

from blocked well data and worked as a constraint during

the facies modeling. The applied variogram values are illus-

trated in Table 1.

In this type of variogram, it is assumed the equivalent

parameters in facies 0, 1 and 2 because they nearly have

similar classification in terms of reservoir and possible res-

ervoir facies. In another way, different values are used for

facies 3 (non-reservoir facies). The modeled facies outline is

shown in Figure 6.

25 m

1 m

25 m

TABLE 1. UNISIM-I-R – SPHERICAL VARIOGRAM: FACIES MODELING.

Facies Range

Azimuth Parallel Normal Vertical

0 1000 600 9 135

1 1000 600 9 135

2 1000 600 9 135

3 2000 1000 9 135

FC

Top

Bottom

reservoir

boundary

FD

FC

FB

FA

reservoir

boundary

Page 4: Unisim Model Description

AVANSI & SCHIOZER: UNISIM-I: SYNTHETIC MODEL FOR RESERVOIR DEVELOPMENT AND MANAGEMENT APPLICATIONS 24

(a) (b)

Figure 6. UNISIM-I-R – facies modeling: (a) Top and (b) Bottom of the reservoir.

Facies modeling must honor all geological information

of the reservoir, including body form, dimensions and spa-

tial trends. Thus, it is made a statistical analysis (histogram)

to check the quality of the results during the facies modeling

process. It can be seen in Figure 7, a comparison among

well logs, regularized well logs (upscaled) and the distribu-

tion of facies property in the entire reservoir.

Figure 7. UNISIM-I-R: histogram of facies for well logs, upscaled cells

and model.

From this analysis, it is possible to observe that the faci-

es modeling was well done, honoring the upscaled cells

previous determined from well logs measurements.

For more realistic reservoir model, it is important to link

simulated facies and petrophysics, such as porosity and

permeability, taking into account facies distribution for

constraining the simulations in the interwell areas.

Porosity and permeability distribution are a necessary

prerequisite to model flow behavior under both steady and

unsteady state conditions. Porosity data from well logs is

scaled up to grid resolution without loss of heterogeneity

and checked for depth trends. A 3D stochastic modeling,

Sequential Gaussian Simulation (SGS), is used to perform

the petrophysical modeling, combining well logs, omni-

directional variogram (Table 2) and 3D facies model to

control and condition the porosity distribution (Dubrule,

1998; Kelkar et al., 2002).

The assumed spherical variogram, used during the

petrophysical modeling of porosity, is based on the upscaled

well logs, mean and standard deviation from well log meas-

urements. The porosity modeling is illustrated in Figure 8.

(a) (b)

Figure 8. UNISIM-I-R – petrophysical modeling of porosity: (a) Top and (b) Bottom of the reservoir.

The petrophysical modeling was done, although an extra

step is necessary to check the quality of the modeling re-

sults. Therefore, it is made a histogram of porosity for well

logs, the regularized well logs (upscaled) and the modeled

porosity distribution in the entire reservoir as it can be seen

in Figure 9.

Figure 9. UNISIM-I-R: histogram of porosity for well logs, upscaled cells

and model.

In general, there is a smooth difference among the up-

scaled cells and the modeled property qualitatively. Howev-

er, it is important to focus on a quantitative analysis in order

to complete the other one, as illustrated in Table 3.

36.5

25.4 24.1

14.0

37.8

23.9 24.9

13.5

36.5

24.3 25.3

13.9

0

5

10

15

20

25

30

35

40

0 1 2 3

Rela

tive F

req

uen

cy, %

Facies

UNISIM-I-R: Histogram of Facies

Well Logs

Upscaled

Model

0

4

8

12

16

20

24

28

2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34

Rela

tiv

e F

req

ue

ncy, %

Porosity, %

UNISIM-I-R: Histogram of Porosity

Well Logs

Upscaled

Model

TABLE 2. UNISIM-I-R – SPHERICAL VARIOGRAM: PETROPHYSICAL MOD-

ELING.

Property Range

Azimuth Parallel Normal Vertical

Porosity 1000 700 9.5 135

Bottom Top

Bottom Top

Page 5: Unisim Model Description

INTERNATIONAL JOURNAL OF MODELING AND SIMULATION FOR THE PETROLEUM INDUSTRY, VOL. 9, NO. 1, APRIL 2015 25

In this table, it can be seen an insignificant increase and

decrease in the mean and standard deviation respectively in

the entire reservoir model. In short, the petrophysical mod-

eling of porosity is performed satisfactory, maintaining

almost the same distribution pattern from the upscaled well

logs.

Porosity can be measured reliably by logging, but not by

permeability. So, it is necessary to estimate permeability

using the porosity determined from the core analysis data

(Boyer, 1985), giving an important contribution to reservoir

characterization at this stage. Relationship of porosity to

permeability is shown from analysis of the available core

description, being possible to observe in Figure 10.

Figure 10. Core analysis data: porosity versus permeability.

In this case, it is assumed a linear dependence of perme-

ability (logarithm scale) and porosity in the core description

data. So, a linear regression is used in order to obtain a

mathematical equation to represent the permeability distri-

bution as a function of porosity in the entire reservoir mod-

el. The curve fitting (red line) and the equation are illustrat-

ed in the Figure 10. The calculated R square value is 0.89

and reflected in a good fitting, attesting the consistency for

indirect measurements of permeability (porosity) from core.

Thus, it is possible to manipulate the fitting function that it

is highlighted in Figure 10 in order to obtain the Equation

(5).

𝐾ℎ = 10[(0.1346×∅ℎ)−0.9794]

where kh is horizontal absolute permeability and Øh horizon-

tal absolute porosity. In

Figure 11, it is possible to see the permeability distribution

of the UNISIM-I-R after this estimation and applied for the

full reservoir.

(a) (b)

Figure 11. UNISIM-I-R – permeability estimation from cores: (a) Top and

(b) Bottom of the reservoir.

Interval facies cut-off is used to calculate net-to-gross

ratio (NTG) of each area for the model, as illustrated in

Table 4.

The facies type was associated with porosity and this

was the criteria used to set up reservoir NTG. It can be seen

in

Figure 12, NTG estimation based on the distribution

analysis of facies 0, 1, 2 and 3 obtained from the well data

regularization.

(a) (b)

Figure 12. UNISIM-I-R – NTG estimation: (a) Top and (b) Bottom of the reservoir.

The NTG aims to preserve the facies trend during a flow

simulation, accounting for facies proportion and the vario-

gram used in geostatistical simulation. The idea is to use the

UNISIM-I-R in the numerical simulator to create the history

production and map distribution.

Following the idea of modeling important properties to

be used in the reservoir simulation, it can be seen in Table 5

a correlation analysis of these ones.

log10Kh = 0.1346øh - 0.9794

R² = 0.89-2

-1

0

1

2

3

4

0 5 10 15 20 25 30 35

Lo

g1

0(K

h),

mD

Øh, %

Core Description: Porosity vs Permeabilitiy

TABLE 3. UNISIM-I-R: QUALITY CONTROL OF PETROPHYSICAL MODEL-

ING.

Zones

Mean Standard Deviation

Well

Logs Upscaled Porosity

Well

Logs Upscaled Porosity

All 13.7 13.8 14.0 10.4 9.9 9.7

1 15.8 15.8 15.2 10.8 10.4 10.2

2 16.0 16.0 17.3 10.4 9.8 9.5

3 13.5 13.6 14.0 10.6 10.0 9.9

4 10.9 11.1 10.6 9.2 8.7 8.3

Note: 1st and 4th zones are the top and base of the reservoir respectively.

TABLE 4. FACIES CUT-OFF.

Facies NTG

0 1.0

1 0.8

2 0.6

3 0.0

Bottom Top

Bottom Top

Page 6: Unisim Model Description

AVANSI & SCHIOZER: UNISIM-I: SYNTHETIC MODEL FOR RESERVOIR DEVELOPMENT AND MANAGEMENT APPLICATIONS 26

In this table, it is possible to observe that porosity and

horizontal permeability have the same correlation factor that

means they have the same behavior in the entire reservoir.

In fact, this is right, because the permeability is directly

generated as a function of the porosity.

4 UNCERTAINTY VARIABLES

Scenarios around facies, porosity, NTG, permeability, east

structural model, water relative permeability (Krw), Black-

Oil pressure, volume and temperature dependencies (PVT),

water oil contact depth (WOC), rock compressibility (Cpor)

and vertical permeability multiplier (Kz) are considered

during the UNISIM-I-D reservoir modeling. The uncertain-

ties are quantified on purpose to create a comparison project

during a field management. Table 6 summarizes the uncer-

tainty data and scenarios used to construct the UNISIM-I-D.

.

Uncertainties in porosity, NTG (as a function of facies),

fluid contacts and structural model affect the fluid volumes

in the model; and PVT, permeability (as a function of poros-

ity), rock compressibility, vertical permeability multiplier

and water relative permeability are related to the fluid flow

and also as reservoir energy source.

Facies and porosity scenarios are generated using a ran-

dom seed during the facies and petrophysical modeling.

NTG and permeability distribution are then calculated as a

function of facies and porosity respectively. Water relative

permeability tables are included to uncertainty models,

assuming a range of Corey exponents (water wet). PVT

tables are added to reflect the uncertainty around oil density

and gas in solution. Structural model uncertainties are de-

fined to reproduce a possible geological region in the reser-

voir model that is not covered by the initial well develop-

ment planning. In addition, uncertainties in vertical continu-

ity and rock compressibility are presented during the initial

development planning.

5 UNISIM-I-D

The UNISIM-I-D was created for a project developed in the

data t1 (05-31-2017), i.e., an initial stage of field manage-

ment plan under uncertainties, including 4 years of produc-

tion data (2013-2017) considering the available information

of four production wells. There is a well log measurement, a

core description and a seismic data that are used to build the

structural, facies and petrophysical model based on the

previous steps of UNISIM-I-R.

The geomodel used to build the UNISIM-I-D in terms of

resolution is identical and followed the same steps of the

UNISIM-I-R, based on structural, facies and petrophysical

modeling. Therefore, Figure 13 presents facies, porosity,

permeability and NTG distribution of the top of the geo-

model.

(a) (b)

(c) (d)

Figure 13. Top of UNISIM-I-D at geological grid: (a) Facies, (b) Porosity,

(c) Horizontal permeability and (d) NTG distributions.

Using the four conditioning wells, the initial porosity

distribution is obtained by petrophysical modeling (Figure

13b), conditioned by variogram analysis and well log meas-

urement.

The porosity-permeability correlation is corresponded to

the same law of UNISIM-I-R and it is applied for compu-

TABLE 5. UNISIM-I-R: CORRELATION ANALYSIS AMONG POROSITY, HORIZONTAL PERMEABILITY AND NET GROSS.

Property Porosity Horizontal

Permeability

Net Gross

Porosity 1.00 1.00 0.49

Horizontal

Permeability 1.00 1.00 0.49

Net Gross 0.49 0.49 1.00

TABLE 6. UNISIM-I-D: UNCERTAINTY ATTRIBUTES.

Attribute Uncertainty

Type Levels/PDF*

Facies discrete

(scenario)

500 equiprobable realizations

Porosity discrete

(scenario)

NTG, fraction correlated

with facies

Permeability,

md

correlated with porosity

East structural

model, unitless

discrete

(scenario)

presence (0.7);

absence (0.3)

Krw, unitless discrete

(scenario)

Krw0 (0.2); Krw1 (0.2);

Krw2 (0.2); Krw3 (0.2);

Krw4 (0.2)

PVT discrete

(scenario) PVT0 (0.34); PVT1 (0.33);

PVT2 (0.33)

WOC, m continuous (triangular)

0, x<3024

(x-3024)/22500, 3074≤x≤3174 (3324-x)/22500, 3174≤x≤3324

0, x>3324

Cpor,

(106 kgf/cm²)-1

continuous (triangular)

0, y<10

(y-10)/1849, 10≤y≤53 (96-y)/1849, 53≤y≤96

0, y>96

Kz multiplier,

unitless

continuous

(triangular)

0, z<0 2z/4.5, 0≤z≤1,5

( 6-2z)/4,5, 1,5≤z≤3

0, z>3

*Probability Density Function.

Top Top Top

Top Top

Page 7: Unisim Model Description

INTERNATIONAL JOURNAL OF MODELING AND SIMULATION FOR THE PETROLEUM INDUSTRY, VOL. 9, NO. 1, APRIL 2015 27

ting the permeability fields (Equation (5)), as follows in

Figure 13c.

Finally, NTG distribution calculation is conditioned by

facies of each interval for the UNISIM-I-D (Figure 13d),

being represented in the numerical simulation model after

the upscaling procedure.

On the other hand, an upscaling procedure is necessary

to reduce the dimension of the reservoir grid cell in terms of

manageable level of flow simulation.

5.1 Upscaling of UNISIM-I-D

Based on the UNISIM-I-D at high resolution grid, an up-

scaling procedure to a medium reservoir scale is necessary

to decrease the computational effort as a result of the num-

ber of simulation generated during a reservoir management.

The cell scale of the simulation model is defined to reflect

reservoir behavior properly, i.e., the heterogeneities. Thus, it

is assumed a simulation grid cell resolution of 100 x 100 x 8

m (Figure 14), discretized into a corner point grid (81 x 58 x

20 cells, with 36,739 active total cells).

(a) (b)

Figure 14. UNISIM-I-D: (a) Structural model and (b) Grid resolution, highlighting the UNISIM-I-R block size.

Porosity is upscaled by simply using an arithmetic vol-

ume weighted method to ensure that the hydrocarbon pore

volume remains constant when upscaling (additive property

characteristics). Figure 15 highlights the original scale and

upscaled porosity map, i.e., an upscaling procedure of the

geological to the flow simulation model.

(a) (b)

Figure 15. UNISIM-I-D – porosity distribution at: (a) Geological and (b) Flow simulation scale after the upscaling procedure.

Permeability is upscaled using a flow-based upscaling

technique. This one produces effective permeability to rep-

licate the fine scale behavior in overall flow rate by using a

single-phase pressure solver, FLOWSIM (Deutsch, 1989).

When an isotropic permeability is upscaled, the effective

results become anisotropic; three effective permeabilities in

all directions (i, j and k) are then obtained for the upscaled

reservoir (UNISIM-I-D). Figure 16 shows the results of the

upscaling procedure of the original scale permeability to

effective permeability Ki, Kj and Kk.

(a) (b)

(c) (d)

Figure 16. UNISIM-I-D –permeability distribution at: (a) Geological scale,

and (b) Effective Ki, (c) Effective Kj and (d) Effective Kk at flow simulation

scale after the upscaling procedure.

NTG is upscaled using the same procedure as porosity

because it is an additive property and it is directly related to

maintain constant the hydrocarbon volume during this ap-

plication. It can be seen in Figure 17, the original scale and

upscaled values of NTG.

(a) (b)

Figure 17. UNISIM-I-D – NTG distribution at: (a) Geological and (b)

Flow simulation scale after the upscaling process.

Table 7 is generated to illustrate the correlation among

the reservoir properties of the UNISIM-I-D.

In this table, porosity and horizontal permeability do not

have the same correlation factor like the UNISIM-I-D at the

geologic grid because of the upscaling procedure used to

8 m

100 m

100 m

TABLE 7. UNISIM-I-D: CORRELATION ANALYSIS AMONG POROSITY, HORIZONTAL PERMEABILITY AND NET GROSS.

Property Porosity Ki Kj Kk NTG

Porosity 1.00 0.90 0.89 0.96 0.45

Ki 0.90 1.00 0.99 0.80 0.36

Kj 0.89 0.99 1.00 0.80 0.35

Kk 0.96 0.80 0.81 1.00 0.44

NTG 0.45 0.36 0.35 0.44 1.00

Top Top

Top Top

Top Top

Top Top

Page 8: Unisim Model Description

AVANSI & SCHIOZER: UNISIM-I: SYNTHETIC MODEL FOR RESERVOIR DEVELOPMENT AND MANAGEMENT APPLICATIONS 28

transfer from the geological to simulation model, losing

resolution in vertical and horizontal direction.

6 BASE PRODUCTION STRATEGY

The idea of this application is to have a production strategy

to compare the UNISIM-I-R and UNISIM-I-D models. The

original volume of oil of UNISIM-I-D model is 130 million

m3, the oil density is 28º API and the fluid model is the

Black Oil.

The production strategy is selected based on a manual

process proposed by Botechia et al. (2013) with some modi-

fications. The idea is to observe well behavior and perfor-

mance to improve the production strategy, discarding wells

with low performance and changing the configuration of

production system, aiming to maximize an economic indica-

tor, such as Net Present Value (NPV). The use of this meth-

odology on the base production strategy definition relies on

the following assumptions:

History production period of 1461 days (t1) is availa-

ble;

Four vertical wells are presented during the history

production time. So, they cannot be removed from

the strategy because they had already been drilled

and they were already used to build the UNISIM-I-

D;

Waterflooding was chosen as a secondary oil recov-

ery method;

The NPV is used as the main indicator to select the

production strategy. The behavior of wells is ob-

served in order to have a good strategy.

Some of the main fiscal and economic assumptions are

outlined in Table 8. These are mean values used for the

deterministic base production strategy optimization using a

numerical reservoir simulation. The investments in platform

were in the function of its production capacity (Hayashi,

2006).

Thus, a base production strategy is defined by which

presented the maximum economic return based on NPV. In

Figure 18, it can be seen the reservoir simulation outline

with the defined strategy. Due to a matter of visibility, hori-

zontal wells are not included and well names are omitted on

the left and right reservoir model respectively.

(a) (b)

Figure 18. UNISIM-I-D - base production strategy definition: (a) Wells are

in the same visualization level (second layer) and (b) 3D visualization.

The strategy is defined with 25 wells (4 original vertical

producers, 10 horizontal producers and 11 injectors). The

results show a recovery factor of 47 % and a NPV of 1.77

billion dollars, calculated from the economic parameter

defined previously. It is possible to check more alternatives

than the previous found, maximizing the NPV or Recovery

Factor (RF). In addition, a consistency check for the de-

fined exploitation scheme and the geology information of

the initial perforated vertical wells can be carried out, espe-

cially in an initial phase of the field development because of

the high level of uncertainties and risks followed by changes

in alternatives less optimized. Although they are not the

focus of this work.

In reservoir management, several models under uncer-

tainties can be generated using reservoir characterization. In

order to check the consistency of the UNISIM-I-D based on

the production strategy definition and available uncertain-

ties, the uncertainty curves for cumulative oil production

(Np), cumulative water production (Wp) and NPV are cre-

ated as shown in Figure 19.

(a) (b)

(c)

Figure 19. UNISIM-I-D – uncertainty curves: (a) Np, (b) Wp and (c) NPV.

In this uncertainty analysis, it is possible to conclude that

the UNISIM-I-D is among the possible reservoir models and

it can be used in future works.

7 RESULTS

The main goal of this project is to create different models: a

reference with known properties and a simulation with low

available information.

0.0

0.2

0.4

0.6

0.8

1.0

30 40 50 60 70 80 90

Pe

rce

nti

les

Np, MM m³

UNISIM-I-D: Uncertainty Curves

UNISIM-I-D

0.0

0.2

0.4

0.6

0.8

1.0

0 20 40 60 80 100

Pe

rce

nti

les

Wp, MM m³

UNISIM-I-D: Uncertainty Curves

UNISIM-I-D

0.0

0.2

0.4

0.6

0.8

1.0

-1.0 0.0 1.0 2.0 3.0

Pe

rce

nti

les

NPV, USD Billions

UNISIM-I-D: uncertainty Curves

UNISIM-I-D

TABLE 8. UNISIM-I-D: ECONOMIC PARAMETERS FOR SIMULATION MODEL.

UNISIM-I-D Model

Market

Values

Oil price (USD/bbl) 50 Discount rate (%) 9

Taxes

Royalties (%) 10

Special Taxes on G. Revenue (%) 9.25 Corporate Taxes (%) 34

Costs

Oil production (USS/bbl) 10

Water production (USS/bbl) 1

Water injection (USS/bbl) 1 Abandonment (USD Millions) 7.4

Investments

Initial Investment (USD Millions) 768.9

Wells (USD Millions) 13.3

Platform (USD Millions) 786.3

Page 9: Unisim Model Description

INTERNATIONAL JOURNAL OF MODELING AND SIMULATION FOR THE PETROLEUM INDUSTRY, VOL. 9, NO. 1, APRIL 2015 29

The previous stage presented a base production strategy

on UNISIM-I-D. This model is built after 4 years from the

onset of its oil production. The strategy is applied in UNI-

SIM-I-R in order to obtain production curves to be com-

pared with UNISIM-I-D checking the differences between

them. The idea is to illustrate how much they are different,

focusing on production curves, saturation and pressure dis-

tribution maps, highlighting the importance of having a

known “real” reservoir.

Figure 20 illustrates a comparison between oil and water

curves in a production history (HIST) and prediction of

UNISIM-I-D and UNISIM-I-R models.

Figure 20. UNISIM-I-R (REF) and UNISIM-I-D (SIM): oil and water

production curves.

From the oil and water production curve, it is possible to

observe that the productivity of the UNISIM-I-R is lower

than UNISIM-I-D. This decline production occurs due to

the differences of available geological information that is

used to build the UNISIM-I-D, following an initial man-

agement phase.

Figure 21 shows the average reservoir pressure (ARP)

and the oil recovery factor (ORF) in a production history

and prediction of both models.

Figure 21. UNISIM-I-R (REF) and UNISIM-I-D (SIM): ARP and ORF.

It can be noticed, from Figure 21, that the productivity is

related directly to the reservoir pressure, besides the reser-

voir drive mechanism (waterflooding). From these produc-

tion curves, it is possible to affirm the significance of char-

acterizing the reservoir model with uncertainties in order to

get better results during a field management plan.

This analysis can be linked to the oil and water distribu-

tion presented in the reservoir (Figure 22).

(a) (b)

Figure 22. UNISIM-I-R and UNISIM-I-D: ternary distribution of a layer at

2021.

In Figure 22, it is possible to observe the differences in

the movement of the water front, where there are regions

which contained more water than others. This mistake is

affected by the initial management phase that the reservoir

characterization was performed, showing a deviation from

the “real” property to the modeled one. Other differences

can be obtained from the simulation of both models.

As a result, the main objective of creating different

models is completed, creating and comparing UNISIM-I-R

and UNISIM-I-D models considering that little information

was available during the initial field development phase.

8 CONCLUSIONS

The main contribution of this work is to build a reliable

reservoir model (UNISIM-I-R) to be used for future com-

parative projects solutions, bringing options to create, test

and compare new approaches related to reservoir develop-

ment and management by several research centers.

The UNISIM-I-R was used in a related study of petrole-

um reservoir simulation and management, guiding and prov-

ing some information to the UNISIM-I-D after an upscaling

procedure. The results showed the importance of condition-

ing the simulation model to used data and representing the

uncertainties, mainly in an initial stage of a field manage-

ment plan where a small amount of information was availa-

ble.

A base production strategy was defined during the reser-

voir studies, aiming to validate the UNISIM-I-D and intro-

duce the UNISIM-I-R, guiding the choice of one alternative

of production plan with the best exploitation scheme. An

approximate number of wells are defined during this stage,

considering the geological uncertainties. This application

allowed comparison of UNISIM-I-R and UNISIM-I-D

models to check the consistency and reliability of the UNI-

SIM-I case.

ACKNOWLEDGMENT

The authors are grateful to the support of the Center of Pe-

troleum Studies (Cepetro-Unicamp/Brazil), Department of

Petroleum Engineering (DEP-FEM), Petrobras S/A, Brazil-

ian National Agency of Petroleum, Natural Gas and Biofu-

els (ANP) and CGG Geoscience Company for the financial

0

2

4

6

8

10

12

14

16

18

20

0 2000 4000 6000 8000 10000 12000Oil/W

ate

r P

rod

ucti

on

Rate

, 10

3m

³/d

ay

Time, days

UNISIM-I-R and UNISIM-I-D

Oil - REF Oil - SIM Water - REF Water - SIM

HIST PREDICTION

0

67

134

201

268

335

402

0

10

20

30

40

50

60

0 2000 4000 6000 8000 10000 12000

Oil

Re

co

ve

ry F

ac

tor,

%

Time, days

UNISIM-I-R and UNISIM-I-D

ORF - REF ORF - SIM ARP - REF ARP - SIM

HIST PREDICTION

Page 10: Unisim Model Description

AVANSI & SCHIOZER: UNISIM-I: SYNTHETIC MODEL FOR RESERVOIR DEVELOPMENT AND MANAGEMENT APPLICATIONS 30

support for this research. In addition, special thanks to

UNISIM and Petroleum Engineering Department (DEP-

FEM-Unicamp/Brazil).

REFERENCES

Botechia, V.E., Gaspar, A.T.F.d.S. and Schiozer, D.J., 2013. Use

of Well Indicators in the Production Strategy Optimization

Process. SPE EUROPEC. Society of Petroleum Engineers,

London, United Kingdom.

Boyer, R.C., 1985. Geologic Description of East Velma West

Block, Sims Sand Unit, for an Enhanced Oil Recovery Project.

Journal of Petroleum Technology, 37(8): 1420-1428.

Brock, J., 1986. Applied Open-hole Log Analysis. Contributions in

Petroleum Geology and Engineering, 2. Gulf Publishing Com-

pany.

Deutsch, C., 1989. Calculating Effective Absolute Permeability in

Sandstone/Shale Sequences. SPE Formation Evaluation, 4(3):

343-348.

Dewan, J., 1983. Essentials of Modern Open-Hole Log Interpreta-

tion. PennWell Books.

Dubrule, O., 1998. Geostatistics In Petroleum Geology. AAPG

Continuing Education Course Note Series 38. The American

Association of Petroleum Geologists, Tulsa, Oklahoma,

U.S.A., 251 pp.

Ertekin, T., Abou-Kassem, J.H. and King, G.R., 2001. Basic Ap-

plied Reservoir Simulation. Volume 7 de SPE Textbook Se-

ries, Richardson, Texas, 406 pp.

Galli, A. and Beucher, H., 1997. Stochastic models for reservoir

characterization: a user-friendly review, Latin American and

Caribbean Petroleum Engineering Conference. Society of Pe-

troleum Engineers, Rio de Janeiro, RJ, Brazil.

Guardado, L.R., Gamboa, L.A.P. and Lucchesi, C.F., 1989a. Petro-

leum Geology of the Campos Basin, Brazil, a Model for a Pro-

ducing Atlantic Type Basin: PART 1. AAPG Special Volumes,

A132: 33.

Guardado, L.R., Gamboa, L.A.P. and Lucchesi, C.F., 1989b. Petro-

leum Geology of the Campos Basin, Brazil, a Model for a Pro-

ducing Atlantic Type Basin: PART 2. AAPG Special Volumes,

A132: 42.

Guardado, L.R., Spadini, A.R., Brandao, J.S.L. and Mello, M.R.,

2000. Petroleum System of the Campos Basin, Brazil. In: M.

R. Mello and B. J. Katz (Editor). Petroleum Systems of South

Atlantic Margins: AAPG Memoir 73, pp. 317-324.

Hayashi, S. H. D. Valor da Flexibilização e Informação em Desen-

volvimento de Campo por Módulos. 2006. Master Thesis. Fac-

ulty of Mechanical Engineering, State University of Campinas,

138 pp. (in Portuguese).

Hayashi, S.H.D., Ligero, E.L. and Schiozer, D.J., 2010. Risk miti-

gation in petroleum field development by modular implanta-

tion. Journal of Petroleum Science and Engineering, 75(1–2):

105-113.

Hilchie, D.W., 1982. Applied Openhole Log Interpretation for

Geologists and Engineers. D.W. Hilchie.

Johann, P.R.S., 1997. Inversion sismostratigraphique et simula-

tions stochastiques en 3D: reservoir turbidítique, offshore du

Brésil. Doctoral Dissertation, Université Pierre et Marie Curie,

352 pp. (in French).

Kelkar, M., Perez, G. and Chopra, A., 2002. Applied Geostatistics

for Reservoir Characterization. Society of Petroleum Engi-

neers, 264 pp.

Meneses, S.X.d. and Adams, T., 1990. Ocorrências de resistivida-

des anômalas no Campo de Namorado, Bacia de Campos, Rio

de Janeiro, Brasil. (in Portuguese).

Ponte, F.C. and Asmus, H.E., 1978. Geological framework of the

Brazilian continental margin. Geol Rundsch, 67(1): 201-235.

Ravenne, C., Galli, A., Doligez, B., Beucher, H. and Eschard, R.,

2002. Quantification of Facies Relationships via Proportion

Curves. In: M. Armstrong, C. Bettini, N. Champigny, A. Galli

and A. Remacre (Editors), Geostatistics Rio 2000: Proceedings

of the Geostatistics Sessions of the 31st International Geologi-

cal Congress. Quantitative Geology and Geostatistics. Springer

Netherlands, Rio de Janeiro, pp. 19-39.

Rider, M., 1996. The Geological Interpretation of Well Logs.

Rider-French Consulting Ltd.

Seifert, D. and Jensen, J.L., 1999. Using Sequential Indicator

Simulation as a Tool in Reservoir Description: Issues and Un-

certainties. Mathematical Geology, 31(5): 527-550.

Souza Jr., O.G., 1997. Stratigraphie Séquentielle et Modélisation

Probabiliste des Reservoirs d'un Cône Sous-Marin Profond

(Champ de Namorado, Brésil). Integration des Données

Géologiques et Géophysiques. Doctoral Dissertation, Universi-

té Pierre et Marie Curie, 215 pp. (in French).

Suslick, S.B. and Schiozer, D.J., 2004. Risk Analysis Applied to

Petroleum Exploration and Production: an Overview. Journal

of Petroleum Science and Engineering, 44(1-2): 1-9.

Winter, W.R., Jahnert, R.J. and França, A.B., 2007. Bacia de Cam-

pos, Rio de Janeiro, Brasil. (in Portuguese).