BADLEYS TRAPTESTER GeoTechLong

Badleys / TrapTester

Badley Geoscience Ltd, Lincolnshire, UK

www.badleys.co.uk

Application of structural geological methods to E&P problems

Consultancy: Global consultancy presence in all major oil and gas producing areas Expertise in all aspects of structural geology, seismic interpretation, structural basin analysis, fault/fault seal problems, fracture analysis/prediction, 3D model building, impact of structure on reservoir models

Training: In-house and public courses Practical Structural Geology Fault Seal Analysis Software training

Software: TrapTester (and related products) Stretch, FlexDecomp 3rd Party Add-ons e.g. FSA for LGC

TT software clients

• Anadarko • Apache• BP• British Gas• BGIndia • Caltex • ChevronTexaco• ConocoPhillips • CNSPC (Khartoum)• CPC (Taiwan)• ENI/Agip• Encana• GNPC• GUPCO• Norsk Hydro• NIOC

• Dagang GRI• BGP• CNODC• CNOOC• CCSPC• JiangSu• SinoPec

• ONGC• Origin• PDVSA• PEMEX• Petrodar• Pertamina• PetroBras • Petrocanada• QatarPetroleum• Rashpetco• Reliance• Statoil• Shell (incl. Sarawak)• Spirit (incl. Balikpapan)• Woodside• WNPOC

Technical and Commercial support centres

Commercial support centres

UK - Headquarters

Abu Dhabi

New Delhi

Lagos

Beijing

Jakarta

Denver

Villa Hermosa

TrapTester

Interp QC

Structural Analysis

Fault Seal Analysis

3D Stress Analysis

ED Modeling

Framework Builder

Property Model & Viz

2D/3D Seismic Interp

OpenWorks GeoFrame Other ...

Well Interp & Processing

Transmissibility Mapping

Data

• fault interpretation – point sets, sticks, tsurfs

• horizon interpretation – point sets, grids, lines, tsurfs

• interpretation can be sourced from OW (binary), GeoFrame (binary), GoCad or other ascii routes

• interpreted can be edited or generated from scratch in TrapTester

• wells from OW, GeoFrame, ascii

• seismic from OW, GeoFrame, SEGY

• cornerpoint grids ECL (SGrids ... soon)

Different tasks have different data requirements, TT catersfor:

Getting data

Seismic and interpretation Wells, picks and log curves

*

Binary access to wells and seismic from OpenWorks and GeoFrame

TT Stores and accesses multiple concurrent 2D and 3D surveys as (a) direct access, (b) cached data or (c) in BGL format

Simple key concepts to applyduring exploration and prospect generation

Displacement continuity along faults

Displacement conservation at fault linkages

Visualize interpretation in 3D

Effects of structural juxtaposition

Prediction of fault rock properties and seal potential

Displacement contours (lines of equal displacement) are approximately elliptical and decrease in regular fashion from a maxima close to the centre of the fault

Key point is that the abrupt irregularities in the horizon polygon geometry or throw pattern represent areas in the data that should be checked

Idealised displacement distribution on an isolated normal fault

Perspective view of an isolated fault surface. (a) The fault is shaded to show the surface topography and the horizon separations are shown as dark polygons on the fault surface. The separations increase smoothly towards the centre of the fault. (b) Contoured throw on the fault surface. The throw increases systematically from low values (blue) to high values (red). The fault continues below the deepest interpreted seismic horizon.

From: Needham et al. 1996

Using displacement patterns to check fault correction

Map of initial fault interpretation showing fault cuts correlated as a single fault (highlighted in blue). (b) The pattern of throw on the fault distinguished by banded contour pattern. Two maxima are developed suggesting that it is really two separate faults. (c) Fault recorrelated to show two separate structures. (From: Needham et al. 1996)

Use fault throw to QC fault correlation in exploration acreage

1500 ms

300

600

0

900

1200

At the reservoir level, decreases eastwards from a high of about 1500ms TWT at the western end of the fault. A significant decrease in throw (from ca 650 to 200ms TWT) is coincident with the eastern boundary of the prospect (arrowed). Fault viewed looking towards south

East West







Horizon data

2D & 3D seismicsections, time slices

Fault segments

Fault picked onsections, time-slicesand horizons

Visualize, interpret multiple 2D, 3D surveys for fault QC and prospect generation

Client example: Niger Delta

• 70 Interpreters

• 10 3D Cubes

• >25 2D Surveys

• 6400 Fault planes

• 5000 Wells

• Huge problem correlating faults and horizons between surveys

• ALL data loaded into a single TT project• Correlation and QC undertaken using 3D viz in TT• TT now adopted as preferred tool for 2D structural interpretation

Client Recommendation







Fault traps and side sealgeometry or property?

Juxtaposition seal only

Mainly juxtaposition seal; minor contribution from gouge between sands

between sands Seal dominated by gouge

cataclastic def’m bands

shale smear

breccia

gouge

Fault zone properties I

Reduction in grain size by fracturing. Reduction in porosity. Localized mechanical mixing of grain fragments.

Fracturing of lithological units. Mechanical mixing of fragmented lthologies.

?

Predicting fault-zone composition II Shale Gouge Ratio (or SGR)

Outcrop data (metre scale)

Well core data (cm scale)

Slipped interval (T)

Throw, T

Vsh5, z5

Vsh4, z4

Vsh3, z3

Vsh2, z2

Vsh1, z1

Sand

ShaleSGR=(Vsh.z) / T

Photo: G Skerlec

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

O bserved proportion of sha ley gouge in fau lt zone

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Ca

lcu

late

d S

hale

Go

uge

Ra

tio

Expected re lationship

Regression R 2=0.71

Shale Gouge Ratio validation: outcrop data

Outcrops provide ‘ground truth’ for assessing the SGR algorithm.

Fault sampling should be at the appropriate scale.

There is a general correlation between observed shale content of the Moab fault zone and the calculated SGR.

235m

155m

275m

65m

0m

K c mJ n

Transects across the Moab FaultS.E. Utah, Foxford et al 1998

65m

0m

155m

235m

275m

Triangle Plots

Throw = 20m

Increasing throw

Sand C

Sand D

Upthrown

Sand B

Sand B

Sand C

Sand D

Sand B

Sand C

Sand D

Sand B

( c )

Sand A

( b )

Throw = 0

Throw = 10m

Sand B

( a )

Sand A

Triangle

• Identify principal fault seal risks and fault seal opportunities quickly using an industry standard technique.

• Improve exploration team productivity and efficiency.

curve data directly fromOpenWorks or

GeoFrame






Juxtaposition of reservoirs

Detailed interpretation and model building:Development/Appraisal/Production

Aim is to produce a “water tight” horizon and fault framework model

Pass the model into a geocellular package

Detailed validation of critical structure

Assess the seal potential

Predict HC column heights

Assess relative impact of certain faults on flow BEFORE building the geocellular model

...

...

Simple key concepts to applyduring exploration and prospect

generation

• Displacement continuity along faults• Displacement conservation at fault linkages• Effects of structural juxtaposition • Prediction of fault rock properties and seal potential

Raw interpretation

Water-tight model

Calculated fault properties

Goal of framework building

TT Provides:

• Advanced tools for editing fault and horizon surface data

• Advanced, semi automated tools for generating and editing the framework topology

• QC tools

• Advanced interpretation aids (e.g. fault slicing)

• Tools for building infill stratigraphy

• Fast, effective, modelling procedures (does not take years to become expert!)

TT uses an automated modelling procedure that is determined by the fault network

(Incorrectly) unconnected fault planes lead to a sloppy and ungeological framework model

Initial model build

Faults need to be linked where appropriate. The process of branchline completion between the splay and master fault is automated but tools exist to optimize the interpretation by hand.

Automated splay intersection lines can be adjusted according to seismic when displayed on the fault surfaces. This improves the accuracy of the fault model.

Here the seismic slice from the down thrown side of the fault is interpreted as though it were a row or column.

Completed interpretation of the hanging wall splay fault

Fault-attribute mapping to quality check interpretation

Problem: Discrepancy in fault polygon due to anomaly in horizon interpretation that may be due to mis-picks, absence of other faults, etc

Solution: Interpret new structure or edit horizons on sections or in 3D or edit fault polygons directly on fault surface or edit the modelling patches

Local framework completion. Applies to all layers and all faults in the project.

• Identify trap-bounding faults• Assign risk (leaking or sealing)• Estimate potential column heights • Better understanding of fault zone properties • More cost-effective reservoir management• Increased recovery (= dollar savings)

Assessment of fault Seal potentialSome goals

Some Observations ...

• Paucity of fault-seal studies by oil companies

Less to do with available technology but more with the reluctance toincorporate routine fault-seal analysis techniques into primaryworkflows

• Faults are typically regarded as a special problem

It is not sufficient to treat the faults as a ‘special problem’ but ratheras part of an integrated container framework of faults and horizonswhose ‘seal capacity’ varies over the surface of the container

Observations ...

Behaviour of faults is time-dependent

In exploration we are interested in the capillary entry pressureof the fault zone and its ability to support an economic columnheight (static trapping)

In production we are more interested in the permeability; a faultthat admits flow over geological time may become a barrier overproduction time scales

Fault Seal – Towards Allan Diagrams

Allan diagram – shows areas of juxtaposition seal and areas of potential cross-fault leakage

Downthrown Red juxtaposed against Upthrown Yellow

Yellow reservoir zones self-

juxtaposed across fault

Areas of reservoir non-overlap = juxtaposition seal at fault.Grey = non-reservoir sealing lithology on both sides of fault

plane.

up

down

Downthrown Yellow juxtaposed against Upthrown Green

Detail of the pattern of juxtaposition

The most import contacts here, i.e. good reservoir to good reservoir, are coloured dark blue. In the absence of any knowledge of the fault rock properties, these are the locations where we would expect the fault to leak.

Volume properties – three routes

SGR=(Vsh.z) / T

Stratigraphic Infill

Constant:Constant distance below or above a primary horizon

Scaled value:Fraction of the interval between two primary horizons

Absolute Depth:Horizontal at depth value

Constant above II

I

II

III

IV

I

II

III

IV

Absolute depth

Constant below I

Scaled below III

Scaled above IV

0.25

0.3

0.3

0.25

I – IV = primary seismic horizonsDashed = markers created in Well Editor

3500

Well 2

B

A

2500

20003000

Stratigraphy is defined in wells and distributed by thickness rules

Example showing a detail of an infilled 3D model

VShale attribute within the infill layers

Vsh

VShale direct from wells using CurveMapper

0

-1 +1

RAI scale; IESX

TrapTester seismic scale

1

V-shale

Low acoustic impedance, low V-shale, probably a gas-bearing sand

Use of an RAI cube and Slicer to volume properties

High acoustic impedance indicateshigh V-shale

Allan Diagrams- Extra note

Allan Diagrams so far have been produced on a layer by layer basis.

New ways of generating volume propertites lead to new ways of considering Allan diagrams

The geometry of the juxtaposition concept can be generalized to apply to layers, seismic voxels, curve mapped properties or cell-cell connections

Map of SGR on a fault surface. Red = high SGR, Yellow = intermediate SGR and Green = low SGR.

Badleys has now published the calibrations from many fault seal studies that show that “green” indicates a high probability of fault leakage and “red” indicates a high probability of seal over geological time scales.

Using properties to predict fault zone composition

SGR=(Vsh.z) / T

Predicting seal / leaking behaviour

Fault-seal attributes (e.g. SGR, CSP) are estimates of the relative likelihood of clay gouge or smear being developed at the fault surface.

To use the attributes as estimates of seal capacity, the attributes must be calibrated in datasets where the sealing behaviour is documented from well data. The objective is to derive an empirical relationship that can be used to estimate the ‘strength’ of the fault seal

0

10

20

30

40

50

60

70

80

90

100

15-20% SGR

Seal

LeakRange of SGR values at sand-sand juxtapositions

Oil fields in North Sea

Sha

le G

ouge

Rat

io (

%)

Shale Gouge Ratio (SGR) at sand-on-sand reservoir juxtaposition. Green (low SGR) are potential leak points, Red (high SGR) are sealing

Relationship between SGR and pressure data

Lines represent maximum across-fault pressure that can be supported at a specific gouge ratio value (seal failure envelopes).

Plotting all data onto one diagram permits a general trend of increasing SGR value supporting increasing across-fault pressure difference (AFPD) to be established.

Empirical equation defining seal-failure envelopes:

AFPD (bars) = 10 ((SGR/27) – C)

C is 0.5 for burial depths < ca. 10,000 ft

C is 0.25 for burial depths 10,000-11,500 ft

C is 0 for burial depths > ca. 11,500 ftAcross-fault pressure difference (AFPD) is taken to be equivalent to fault-zone threshold pressure

Data collected from a variety of basins worldwide (inc. North Sea, mid-Norway, Grand Banks, Gulf of Mexico, Columbus Basin, Niger Delta, Vietnam, Gulf of Thailand).

All basins are typically mixed clastic environments.

Faults dominated by extensional faulting. No strike-slip or reverse fault data.

Fault-gouge samples: composition control on capillary entry pressure

Using calibrated Shale Gouge Ratio to estimate hydrocarbon column heights

Workflow:

• Derive the fault-rock distribution (e.g. from SGR) from subsurface data.

• Convert SGR to fault-zone capillary threshold pressure. Apply seal-failure envelopes derived from calibration with in-situ pressure data or from lab-derived empirical equations to estimate threshold pressure Pc.

• Incorporate density data for water, oil or gas phases at reservoir conditions to predict column height using the equation:

w = pore water density (kg/m3); h = hydrocarbon density (kg/m3)

g = acceleration due to gravity (9.81 ms-2 or approx. 10ms-2)

Pc = threshold pressure in Pascals (105 Pa = 1 bar)

In a simplistic approach, traps are often assumed to be filled down to the shallowest structural spill point. The fault is considered to be sealing (and able to support the column) over the entire fault surface.

Leakage of hydrocarbons through a [membrane] fault seal takes place when the buoyancy pressure exceeds the pressure required for hydrocarbons to enter and pass through the largest interconnected pore throat in the seal (capillary entry pressure).

Establishing where the buoyancy pressure equals the fault-zone entry pressure provides a method for predicting the column height supported by an SGR value.

Predicting hydrocarbon column heights

Buoyancy Pressure

Depth

Pressure difference between hydrocarbons and water

Pressure

B

Water pressure trend

A

Depth

Hydrocarbon pressure trend

Buoyancy pressure / depth profile:Pore pressure / depth profile:

2-D fault section

What is TrapTester predicting?

Buoyancy pressure = capillary threshold pressure

Depth

Fault-zone composition

Depth

Low SGR High SGR

Buoyancy pressure(HC pressure – water pressure)

Capillary threshold pressure (function of SGR)

Low

Depth

High

Predicted leak

point on fault

Maximum column supported by this

SGR value

• TrapTester predicts the column height that is supported by an SGR value

B o u y a n c y P r e s s u r eP r e s s u r e

D e p t h D e p t h S u p p o r t a b l e p r e s s u r ef r o m S G R v a l u e s

Footwall (Oil)

Hangingwall (Water)

= Maximum column supported by the fault

Estimating column heights: 3D case

The critical part of the reservoir overlap is the point which exhibits the shallowest base of hydrocarbon column (red arrow)

This point defines a potential spill point on the fault surface

SGR-derived spill point may be shallower then structural spill(fill-to-spill) or contacts derived from pore pressure gradients

= Shallowest column derived from SGR

Juxtaposed reservoir sands

How can TrapTester predictions help us?

• TrapTester predictions provide an alternative method for estimating column heights (ie, why some traps are under-filled)

OWC based on structure filled down to spill point at fault tip

Purple: OWC; black: depth contours

Fault-plane diagram:

Solid = footwall sand; Dashed = hangingwall sandYellow = sand-on-sand

Alternative OWC based on shortest column predicted from SGR

Column heights predicted from SGR

Red line = column support by SGR

Framework model vs. cellular modela way to optimize cellular geometries?

(EarthGrid)

Simplified fault geometrySimplified fault connectivitySimplified horizon geometry

Integrated modelGullfaks Public Data Release, courtesy of the licencees of PL050/PL050B and the Norwegian

Petroleum Directorate.

Framework model of faults and surfaces based on seismic

interpretation

Well

Horizon

Structural differences

End of geocellular fault before true end of fault.

Fault clay content too low in geocellular fault representation

Geocellular Fault

Framework Fault

0SGR (%)

100

•The geocellular fault is too transmissive towards its tip, and is too short in the model.

•The geocellular fault throw is forced to be 0 at its lateral boundary, where the true displacement is greater.

End of model

N

• The number of faults included in the model usually depends on criteria imposed by limitations imposed by the model building process (Y-faults, fault size, intersection geometry etc.).

• Shouldn’t we decide on what faults to include based on their likely effect on fluid communication between reservoir layers…

• …and their effect on fluid flow?

Structural omissions

Horizon dip azimuth

Calculation of SGR – towards transmissibility(TXMmapper)

Vsh4, z4Vsh3, z3Vsh2, z2Vsh1, z1

SGR = (Vsh.z) / t x100%

Throw

0

1Vshale

0

100SGR (%)

Vshale

Reservoir simulators usually incorporate fault properties implicitly as transmissibility multipliers - the ratio by which the slab of fault-zone material degrades the transmissibility between juxtaposed cells.

The multiplier depends on the size and permeability of the juxtaposed cells as well as the thickness and permeability of the fault zone.

The transmissibility multiplier is model-dependent

Fault properties in reservoir simulation: transmissibility multipliers

k1 k2

L

t, kfz

A

t = Fault-zone thickness

Kfz = Fault-zone permeability

K1 K2 = Cell permeability

L = Distance between cell centers

A = Area of connection between cells

TM =

Fault-gouge samples: control on permeability

General decrease in permeability with increasing phyllosilicates in gouge

• Clay-smear samples show very low permeability.

• Gouges generated from clean sands have very variable properties that depend on their geological history (depth at time of faulting, maximum burial depth – greater depths give lower permeabilities).

Comparison of permeability measurements on core samples (symbols) and predicted permeability (solid lines).

Zf = Initial burial depth (during faulting)Zmax = Maximum burial depthVf = Clay fraction of fault rock

From Sperrevik et al 2002

Fault-gouge samples: control on permeability

kf = 80000.exp-[19.4Vf + 0.00403zmax + (0.0055zf - 12.5)(1 - Vf)7]

Pf = 31.84.kf-0.3848 where Pf = Hg/air threshold pressure

Examples of transmissibility multiplier calculations

• Gullfaks reservoir model contains >50 faults, 25 zones, 38 rows and 87 columns.

• Our examples come from the Northern part of the model.

Gullfaks reservoir model

An example: the “big fault”

• Big fault tips out to the south (left)

• Towards the centre of the fault the maximum throw corresponds to the juxtaposition of Tarbert Sands against Etive and Rannoch sands.

• Shale Gouge Ratio in this area is higher because of the intervening more shaley Ness Fm.

• The fault should be more transmissive towards the tip where self juxtaposition of of the Tarbert Etive and Rannoch Fms occurs.

0

1Vshale

0

100SGR (%)

0

150

ThrowThrow (m)

Hangingwall

Footwall

SGR

Vshale

NS

Transmissibility

0.0002

0.02

Transmissibility(mDm)

0

1

TMX Transmissibility Multiplier

Faulted

Unfaulted

Transmissibility

•Unfaulted transmissibility is the flow potential across a fault that has no fault rock (such as clay smear, cataclasis or diagenetic alteration).

•Faulted transmissibility implicitly incorporates permeability, using SGR, and a thickness of fault rock products into the calculation of potential flow.

•Transmissibility multiplier (TM) is the ratio between unfaulted and faulted transmissibility.

•Low transmissibilty multipliers occur where the fault impedes flow.

NS

Recent examples of reservoir simulation using geologically-driven transmissibility multipliers:

• Heidrun Field

Fault-zone permeability derived from core analysis & applied to juxtaposition diagrams. Accurate prediction of water breakthrough.

• Snorre Field

Transmissibility multipliers derived from SGR analysis. Excellent history match achieved, compared to using non-geological ‘default’ multipliers.

• Scott Field

Transmissibility multipliers derived from SGR analysis. Excellent history match achievable after 1 day instead of 3 months.

Transmissibility Multipliers: Examples

PRODUCTION DATA

ALL FAULTS CLOSED

SELF JUXTAPOSED OPEN

MODIFIED OPEN (3 months)

SGR METHOD (<1 day)

Time

Simulation Results: Water Production vs.

Time1994 1995 1997 1998 19991996

Cumulative WaterProduction (STB*106)

0

4.0

8.0

12.0

20.0

24.0 Scott Field - Block Ib

ALL FAULTS CLOSED (all Tm = 0)

SELF JUXTAPOSED OPEN (Tm = 1; non-juxtaposed Tm = 0)

MODIFIED OPEN (manual input to modify cell-by-cell multipliers)

TMX key benefits

Fault reactivation risk

likelyhood of faults being active under present day stress conditions

Main geomechanical relationships in TrapTester

Ratio of shear to normal stress

Risk of slip increases as the ratio approaches the coeff. friction (~0.6)

Slip Tendency Fracture Stability

P(P)

Pore pressure increase required to induce failure

Assumes fault rock has mechanical strength

Fracture stability Slip tendency

Fault-plane diagrams: geomechanical attributes

Green: small increase in pore pressure required to induce failure (~ high risk of reactivation)

Blue: large increase in pore pressure (~ low risk of reactivation)

Red: high slip tendencyYellow: low slip tendency

Regions on the fault with low fracture stability (green) coincide with high slip tendency (red)

Fracture prediction - FaultED

Introduction to Elastic Dislocation methodology

A ‘fault panel’ is a rectangular dislocation with uniform slip, embedded in an elastic medium. Using the equations of Okada (1992), the resulting displacement and strain tensor can be computed at any observation point in the medium.

The corresponding stress tensor and failure mode (if any) at the observation point can then be computed using appropriate material properties.

Mapped faults in the subsurface can be approximated by an array of rectangular fault panels, each of uniform slip.

Define ‘observation grid’ upon which strains, displacements & stresses are calculated.

FaultED modeling: Workflow

3: Include regional strain estimates. Forward model deformation to match

observed horizon geometry

1: Build faulted framework model

4: View model properties

2: Panel faults (rectangular fault panels)

1. Thrust fault with displacement pattern (blue: 0, red:700m slip).

2. Observation grid set up around thrust fault (node spacing 200m).

3. ED forward model deforms the observation grid to mimic the hangingwall anticline.

Example: Thrust anticline

Comparison of example reservoir horizon and modelled observation grid

modelhorizon

Model does not include regional tilting, effects of other faults, or local diapirism

Elastic strain tensor

Axes of strain tensor

A ‘pseudo-stress’ tensor is calculated from the strain tensor (incorporating material properties)

Volumetric strain

FaultED modelling workflow: Predictions

Maximum Coulomb Shear Stress (MCSS)

Most-favourable fracture orientations

Stress tensor is used to predict the mode and orientation of likely failure planes at all nodes on the grid

Normal Strike slip

Reverse

small faults mapped on seismic

(Sand 2)

Small faults concentrated in

high-MCSS region at east end of main

thrust

Maximum Coulomb Shear Stress (proximity-to-failure indicator),

map view

main thrust fault

normal faults

reverse faults

strike-slip faults

main thrust fault

How do observed and predicted fracture orientations

compare in detail?…..

Predicted fracture orientations, map view

reverse faults on back limb

tear fault at E end of fold

0o

(perfect) 90o (bad)

angular misfit

Mapped fault-trace azimuth (o)

ED Predicted fault-trace azimuth (o)

Mean angular misfit = 23.6o.Cf 45o for random set,

and 62o for faults // main thrust

Coloured squares show local angular misfit between mapped and predicted fault strikes

Simple key concepts to applyat all stages






Leverage all the above to build defensible 3D Models

To risk effectively seal / leak behaviour

To assess risk of reactivation

To predict fracture densities and orientations

To build TXM maps for fault-flow behaviour during production

BADLEYS TRAPTESTER GeoTechLong

Documents

fault interpretation

fault linkages

isolated fault surface

fault key point

seismic interpretation

interpretation wells

structural basin analysis

tsurfs interpretation