Geoengineering the properties of Carbonate Reservoirs · PDF file• Step 3: Up-scaling and...

Project: DynaCARB ICCR Meeting 10th December 2010 1

DynaCARB

Patrick Corbett (HWU)

Ian Main (EU)

DynaCARB- Calibrating carbonate geological models with dynamic data


Dynamic Calibration

• Incorporating dynamic data:

– Pressure data/MDT

– Production logs/Timelapse

– Well Test Data

– Seismic Responses (tricky in carbonates)

• At the appraisal stage of project

• For calibration prior to Prediction


Build well bore model

• Image logs

• Geomodelling software

Faults

Fractures

Vugs

Layers, etc

Multi-porosity model (M)

Alsafadi, 2009


Project Workflow

Viswasanthi ChandraMSc Reservoir Evaluation and Management

Individual Project 2009/2010


Project Workflow

Core and

Petrophysical

Analysis of Well A

Stage 1


Core analysis

• Braided fluvial environment

– amalgamated channel sands,

sheetflood and floodplain facies

• High net to gross

– flood plain mudstones laterally

discontinuous

• Outcrop Analogue:

– Otter sandstone formation

– permeability, porosity reduced due to

calcretes

Fig. 1 ‘Second order’ facies cycle


Petro-physical Analysis

• Lithology sandstone with significant proportions of calcite, arkosic sandstones

• Petrophysical property models: Vsh- Neutron/Density model

Porosity- Density porosity model

Sw- Indonesian model

• Net Pay Analysis:

Cut-offs Vsh Sw Porosity

Lower (primary) cut-off 0.4 0.5 0.12

Upper (secondary) cut-off 0.0 0.0 0.0

Well A Well B Well C Well D

Top Interval (m) 1602 1621.95 1566.4 1599.4

Base Interval (m) 1680.35 1656 1634.3 1665

Net Pay thickness (m) 51.6 21.3 54.9 35.7

N:G (%) 65.88 62.46 80.85 54.09


Stage 2

Identifying and

Generating Geo-

objects


Identification of Geo-objects

• Step 1: Identifying Geo-

objects for wells with core

– Geomodelling software SBED -

used to integrate core and wireline

data to bridge scale gaps

– Wireline and core scale

heterogeneities considered

simultaneously


Litho-Facies Type CUTOFFS

Shale Vsh > 0.4 and POR < 0.05

High Shale Sand +

Calcrete

Vsh > 0.4 and POR >= 0.05

High Shale Sand 0.25 < Vsh < 0.4

Low Shale Sand +

Calcrete

0.1 < Vsh =< 0.25 and POR <

0.13

Low shale Sand 0.1 < Vsh =< 0.25 and POR

>= 0.13

Cross-bedded Sand Vsh =< 0.1 and POR > 0.18

AND RHOB < = 2.3

Channel Sand+ Calcrete Vsh =< 0.1 and POR < 0.18

Channel Sand Vsh =< 0.1 and POR > 0.18

• Eight main types of

hetero-lithic faicies,

Criteria – shale, calcretes

• Vsh, Porosity and Density

cutoffs evaluated using

Monte-Carlo simulation –

Crystalball Software

Facies and Cut-offs


Facies division scheme


Identification of Geo-objects cont..

• Step 2: Geo-object Quality

Check:– ‘Geo-object’ log generated for all

wells with core

– Quality checked with available core

logs

• Step 3: Generating Geo-

objects for Wells Without

Core:– Geo-object logs generated for wells

without core in Petrel

– Wireline logs used for this


Near Wellbore Modeling• Step 1: Bedding structure

Modelling:

– Bedding structure templates

– Input parameters: • Geometry

• Roughness

• Migration

• Depositional features

– Input for calcrete modelling• Shape

• Size

• Spatial distribution

– Dimensions of 3D grid cell: DX = DY = 1 cm

• Step 2: Property Modelling:– Vsh, Porosity and Permeability values

assigned to templates

– Quality checked with core-plug measurements


Near Wellbore Modeling cont…• Step 3: Up-scaling and Exporting to Petrel:

– Porosity, permeability and net to gross up-scaled in SBED

– Representative isochore interval from 1620 to 1630 m used for up-scaling and

exporting


Stage 4

Field Scale

Modelling,

Sensitivity

Analysis


Field scale Modeling

• Seismic top surface used to create top

and bottom surfaces

• Pillar gridding :

– Cell dimensions - dX x dY = 100 m x 100

m

– I- direction reflects the direction of

deposition

– East-West orientation

• Well log Scale up:

– Number of layers: 20


SBED Porosity Model (Number of cells ~

2 million)

SBED Permeability Model

•Grid Dimensions:

X=Y=60 ‘

Z= 10 m

•Cell Dimensions:

dX=dY= 2’

•DZ depends on the

bedding structures in

the model

(Initial models were as

small as dX=dY= 1cm)

Top View

Top View

NWM Property Models for Well A


Two sets of realisations generated


Porosity Up-scaling into Regular Grids

Original Porosity Model (Number of cells ~ 2

million)

(Attempts to obtain optimum upscale scenario)dZ = Dimension of each cell in Z- direction


NWM after up-scaling in Z-Direction


Local Grid Refinement

Sector Model for

Well A

Each SBED property grid plugged

into Field Model through

Local Grid Refinement Scenario


Well Test Response

Convention

al

scenario

NWM

scenario

Difference in well test response between conventional and

NWM scenarios


Field scale Modeling

• Facies and Petrophysical

Modelling

– Sequential Indicator Simulation

(SIS) used for Geo-object

modelling

– Sequential Gaussian Simulation

(SGS) used to model porosity

and NTG

• Volumetric and Sensitivity

Analysis

– bulk volume = 127 * 106 m3

– STOIIP and sensitivity analysis

results discussed in next section


Results and Discussion• Identification of Geo-objects:

– The Geo-objects in the field can be

classified into 8 types

– Vsh, Porosity and Density cutoff

values discrete for each type

– The Normal Distribution of the

probability density function Gamma

Ray (GAPI) for Geo-objects:Geo-object P90 P50 P10

Shale 183 189 196High Shale Sand +

Calcrete168.24 174.18 178.86

High Shale Sand 144.48 155.04 165.83Low Shale Sand +

Calcrete101.71 106.48 111.21

Low shale Sand 123.55 136.02 146.74Cross-bedded Sand 136.44 144.15 152.12

Channel Sand+

Calcrete104.19 107.81 111.43

Channel Sand 135.21 144.19 152.48

Litho-Facies Type CUTOFFS

Shale Vsh > 0.4 and POR < 0.05

High Shale Sand +

Calcrete

Vsh > 0.4 and POR >=

0.05

High Shale Sand 0.25 < Vsh < 0.4

Low Shale Sand +

Calcrete

0.1 < Vsh =< 0.25 and

POR < 0.13

Low shale Sand 0.1 < Vsh =< 0.25 and

POR >= 0.13

Cross-bedded Sand Vsh =< 0.1 and POR >

0.18 AND RHOB < = 2.3

Channel Sand+

Calcrete

Vsh =< 0.1 and POR <

0.18

Channel Sand Vsh =< 0.1 and POR >

0.18


Results and Discussion cont…

• Impact on Modelling:

– Almost +10% change in

STOIIP values

– Reduced uncertainty

– Higher non-stationarity

Case

% change of

STOIIP from

conventional

model

A (Upscaled from near wellbore model (NWM)) +9.5

A1 NWM- Calcrete modelled as star shaped

objects- 33.3

A2 NWM- 50:50 mix of star + network shaped

calcrete- 47.6

A3 NWM- Calcrete modelled as network shaped

objects+ 5.2


• Calcrete Flagging: – At high calcrete zones core

porosity lower than wireline

porosity

– Not representative of formation

• Impact on Economy: – Economic value of the reserves

improved by up to 10%

– Exploration costs can be

downsized

Results and Discussion cont…


Build near well bore model

• Image logs


• Production logs/MDT

Triple -porosity model (T)

Simulated PLT, well G2-97

12240

12260

12280

12300

12320

12340

12360

12380

12400

0 500 1000 1500 2000 2500

Tota Flow rate, RB/STB

Dep

th, ft

Normalized Measured Spinner

Reading, well G2-97

12240

12260

12280

12300

12320

12340

12360

12380

12400

0.0 0.2 0.4 0.6 0.8 1.0

Spinner Speed, rps

Dep

th, ft

HFUs Transmissivity Expressed by

Cum. Kh, well G2-97

12240

12260

12280

12300

12320

12340

12360

12380

12400

0.0 0.2 0.4 0.6 0.8 1.0

Transmissivity

Dep

th, ft

HFU1 HFU2

HFU3 HFU4

Perfs.


Example Data Set


0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

0.00 0.50 1.00

Flo

w C

apac

ity

(k*h

)

Storage Capacity (phi*h)

Lorenz Plot - DK90 (Fahahil West - Downflank)

0.00

0.20

0.40

0.60

0.80

1.00

1.20

0.00 0.50 1.00 1.50

Flo

w C

ap

ac

ity

(k

*h)


Lorenz Plot - DK52 (Fahahil West)

0.00

0.20

0.40

0.60

0.80

1.00

1.20

0.00 0.50 1.00 1.50

Flo

w C

ap

acit

y (k*h

)


Lorenz PLot - DK314 (Fahahil Sector)

Arab-C

DK-90

DK-314

DK-52

60% Flow

50% Storage

less flow

unit contrast

78%

Flow

20% Storage

Much greater

contrast between

UAC & LAC

Useful technique to assess the distribution of flow

capacity versus storage capacity for different layers

Lorenz Plots

UAC

UAC

LAC

LAC

Gomes, 2001Has also been applied in BG’s Karachaganak field


Time-lapse PLTTime Lapse PLT Flowrate Profile (Well N6)

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

Fractional Flowrate and Transmissivity (k*h)

Sto

rati

vity

(P

oro

*h)

Poro-Perm (Core/Log)

PLT 1993

PLT 2002 (Pre-perf)

PLT 2002 (Post-perf)

Cortez and Corbett

SPE 94436

• The Lorenz Plots can be used toassess the flowing potential(transmissivity) of the reservoirunits.

• Measured data => Validation of thepredictive model

• Long term benefits => Productionoptimisation strategy

Core data

Core derived

and K

Observed data

Production Logging

Flowing profiles

(measured Q & P)

Predictive tools

Lorenz Plot

Flowing potential

(transmissivity)


Build numerical well test model

• Image logs


• Production logs/MDT

• Pressure Transient

modelling

Dual or Effective porosity model (DE)


Account for complex structures

(e.g. faults and fractures)

Sector-scale model of

fractured carbonate

reservoir in Oman –

original geometries of

seismic structures are

preserved in reservoir

model


Geotipe curves for carbonates

Extension of double matrix porosity catalogue


Deliverables• Improved links between Stratigraphic Modified Lorenz

Plot (SMLP) and Production Logging (PLT) for

calibration of the WRT models for Flow Unit

identification.

• Use of dynamic (time lapse) production profiles for

reservoir characterisation

• Systematic well test curve responses for MTDE

carbonate systems

• Guidance on the collection of well bore data (including

Well Test Design)

• Guidance on near-well bore property modelling

• Upscaling guidance for carbonate systems(M>T>D>E)

Geoengineering the properties of Carbonate Reservoirs · PDF file• Step 3: Up-scaling and...

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Transcript of Geoengineering the properties of Carbonate Reservoirs · PDF file• Step 3: Up-scaling and...