geological modeling

Geological Modeling Lessons Learned

Serdar Kaya,

Please contact for any suggestion, comments or contribution [email protected]

Outline

29 April 2015 X Systems LLC - Confidential 2

Important Data and QC Approach

Structural Framework

Depth Match

Structural calibration

Fault interpretation QC

Grid parameters

Property Modeling

Stationarity Analysis

Porosity Reconciliation

Cloud transformation

Multiple realization of property models

Blind Testing

Uncertainty Analysis

Uncertainty cases

Data Oriented Approach


Lesson : Incomplete Data Set

Solution: Comprehensive Data Analysis

Complete Data Set with Every Available Data

All penetrating and surrounding wells

Shallower wells

QC data consistency

QC results after every step Well data in cross sections

Check average maps, histograms

Compare results with raw data

Structural Modeling


3D Grids

Depth Shift Cored Wells,

Horizontal and Pseudo Wells

Update Input Database

(Well, core, log, seismic)

Sub-zone

picking

Generate

isochore

Adjacent wells

Inconsistency

Generate Sub-

zones

Generate

Layers

Generate Reservoir

Top Horizon

Generate Fault

Model

Lesson: Outliers in data set

Solution: Right workflow

Structural Modeling Parameters


Fault Modeling

Gridding

Horizons

Subzones

Layering

Structure calibrated with markers

Grid orientation, Zig-zag fault, Grid size

Fault Data for modeling

Subzone well picks and isochores

Layer properties

Lesson : Inconsistent 3D Framework Model

Solution: Right Grid Parameters

Core Data Points Shifted Depth Shift of Core Data

Lesson:

Mismatching

flow

characteristics

Solution: Right

depth for core

data

Depth Correction of Adjacent Wells


Lesson :

Inconsistent

Marker Depth

Solution: Depth

Shift Due to LWD Wireline

Uncertainty

Before Depth Shift of Horizontal Well After Depth Shift

Structural Correction for Horizontal Well Trajectory


Lesson :

Inconsistent

Horizontal

Trajectory

Solution:

Structural Control

with Pseudo Wells

Before Calibration

Calibrated Structure

Structural Modeling


Lesson: Inconsistent SubZone Features

Solution: Right Picking

Structural Modeling


ISOCHORE POINTS

Top Structure

Lesson Learned:

Inconsistent

thickness

variation

Solution: QC

markers with

isochore

Structural Modeling


Lesson : Inconsistent thickness variation

Solution: Subzonation Process, Thickness Control

MarkersIsochore map controls at inter well locations

Structural Modeling


Summary of Troubleshooting for Framework Modeling

Top structure calibration

Surrounding wells

Shallower formations

Incorporating the horizontal wells

Depth match to pilot

Structural control along the trajectory

To control thickness use isochore maps

Grid Characteristics

Orthogonal grids

Fixed orientation

Zig Zag faults

Property Modeling


Lesson: Inconsistent property variation

Solution: Capture Variation with Right Trend

Average Porosity at Well Location

Porosity Modeling Workflow

29 April 2015 X Systems LLC - Confidential14

Scale-up porosity to 3D Model

SGS with Collocated

Co-Krigging to

Porosity Map

Porosity Model

(log porosity reconciled to OB corrected core

porosity)

KriggingSGS

SGS with Data

Range Variogram

SGS with 5

Km rangeKrigging with

Data Range

Variogram

1 24

Krigging with

5 km

Variogram

Krigging Model

as 2D Trend

Krigging Model

without trend

Krigging Model

with modified

trend

5

SGS with

30 Km

range

Krigging with

30 km

Variogram

63

Lesson: Limited

Property Models

Solution:

Scenario Based

Modeling

Workflow

Porosity Modeling


SGS with Trend

Krigging 3D Model Trend

Krigging Modified Map Trend

2013 Model

Lesson: Defining Accurate Realization

Solution: Blind Test on Removed Well Data

Porosity RealizationError Mean

Std

SGS with Trend 0.0159 0.127

Krigging Model Trend 0.0146 0.0124

Krigging Modified Trend 0.0135 0.0109

2013 Porosity Model 0.0293 0.0249

Flank to Crest Well Comparison

Permeability Modelling


Lesson: Limited Property Models

Solution: Scenario Based Modeling Workflow

Scale-up permeability to 3D Model

Permeability (10 wells)

(Distribution on Phie-K Cross Plot per sub-zone)

GRFS with Porosity

Model Trend

GRFS without any TrendGRFS with 2D

Porosity Map

Trend

Data range

Variogram

5 Km range

variogram

1

Cloud Transformation

2

30 Km range

variogram

Data range

Variogram5 Km range

variogram

4 5

30 Km range

variogram

Data range

Variogram5 Km range

variogram

7 8

30 Km range

variogram

3

6

9

Layer 4 Subzone C

Edge effect of algorithm

Saturation log problem

Saturation Modeling QC of Log SaturationLesson: Incorrect Log Interpretation

Solution: Generate Kriged Sw Model QC the logs

Saturation log problem

Crest Wells with 100% Water Saturation

Saturation Modeling


Lesson: Accuracy in Saturation Model and Data

Solution: Saturation Model Based on J Function and MICP data

W7

A A`GDT: 8600 ftss

FWL: 8700 ftss

W1

W2

W3H W4H

W5H W6H

Saturation Modeling


Crest Wells Transition Water Zone

Lesson: Accuracy in Saturation Model and Data

Solution: Saturation Model Based on J Functions

Property Modeling


Data reconciliation and consistency

Trend investigation

Multiple realizations

Extensive QC steps, and statistical analysis

Histogram

Cross Plots

Maps

Cross Sections

Blind test by removing well data from data set

Summary of Troubleshooting in Property Modelling

Model Upscaling


47 Layers13 Layers

23 Layers

Lesson: Upscaling Course Grid

Solution: QC with Cross Sections

Upscaled 13 and 23 Layers vs. Geomodel (47 Layers)

Porosity Model Uncertainty


Scale-up porosity to 3D Model

SGS with Collocated

Co-Krigging to Porosity

Map

Log Porosity (316 wells)

(reconciled to OB corrected core porosity)

Porosity Model by KriggingPorosity Model by SGS

SGS with Data

Range Variogram

SGS with 5

Km range

Krigging with Data

Range Variogram

1 2 4

Krigging with 5

km Variogram

Krigging Model

as 2D Trend

Krigging Model

without trend

Krigging Model

with modified

trend

5

SGS with

30 Km rangeKrigging with 30

km Variogram

63

Lesson: Missing Uncertainty Steps

Solution: Scenario Embedded Workflow

Scenario Based

Uncertainty

Krigging versus SGS

Trend

Variogram

Seed number

Correlation

coefficient

Porosity Model Uncertainty


Final porosity model will be obtained by history match process

Uncertainty cases are generated on following parameters

Modeling algorithm: SGS versus Krigging

Trend

Variogram

Seed number

Bulk shift due to core porosity log porosity difference



Permeability Modeling Uncertainty




Scale-up permeability to 3D Model

Permeability (10 wells)

(Distribution on Phie-K Cross Plot per sub-zone)

GRFS with Porosity

Model Trend

GRFS without any

TrendGRFS with 2D

Porosity Map

Trend

Data range Variogram 5 and 30 Km

range variogram

2

Cloud

Transformation

1 3 21 3

Scenario Based

Uncertainty

Cloud definition

Trend

Variogram

Seed

number

Permeability Model Uncertainty


Lesson: History Match with Multipliers

Solution: Scenario with Cloud Transformation

Density of the pointsExtended limits

Upper Reservoir (R1) Porous 1.2 Sub-zone

Extended Cloud Case

Permeability

Reduced Cloud Case

Permeability

Extended Cloud Case

Permeability

Reduced Cloud Case

Permeability

Data Points

Model Points

Permeability Modeling Uncertainty


Final permeability model will be decided after history match

Uncertainty cases are generated on following parameters

Modeling algorithm: SGS with various trends

Cloud definition

Variogram

Seed number

Bulk shift due to uncertainty in measurements



CONCLUSION


Data oriented workflow

Correction and calibration in early stage

Consistent data set

Comprehensive QC steps

Scenario based uncertainty analysis

Flexible workflow to use for data integration

geological modeling

Documents

systems llc confidential

right depth

important data

available data

grid sizefault data

horizontal wells depth

incomplete data setsolution

depth shift of core