rapid model update-libre

8/19/2019 Rapid Model Update-libre

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Static Modeling Workflows for

Rapid Model Update and

Integration with

Reservoir Simulation

Serdar Kaya, Grenergy LLC


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http://www.grenergyllc.com

Outline

Addressing the Challenge

Why automated processes?

Solutions Workflows

http://www.grenergyllc.com/http://www.grenergyllc.com/


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Marginal Fields

Heavy Operations and large

amount of data collection

Market and Administrative

Pressures

Challenges



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Challenging Fields



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Prognosis and frequent amendments

Geosteering

Frequent Volumetric Evaluation

More Frequent Model Update

Data Management

Operational Challenges



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FieldOperations

Vendor

Geologist Petrophysicist RE

Specialist

Report

Database Update

Real Time or Same DaySame Day

5-10 Days

1-7 Days

30-60 Days

Time Delay in Data Flow




Wells Data

Data

II

III

I

Log Data

SeismicData

Outcrops

Production Tests




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?? ?

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Why Integration ?

Due to sub-surface uncertainty &multi disciplinary approach




Integration Approach

Core Plug

Whole Core

Well Log

Well Tests

Borehole

Geophysics

Outcrop

StudiesSeismic

Geologist’s Knowledge

Production

Data

Engineer’s

Knowledge

Geophysicist’s

Knowledge




Conditional PropertyModeling

Rocktype

Amplitude

orAttributes

Saturation




Conditional Modeling



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Running Petrel Workflows



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Maps From Isochore



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Statistics of PropertyRealizations



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Generating Average Property

Maps

Processing Surfaces



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Statistics Per Sub-Zone



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Multiple PermeabilityRealizations



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Monte Carlo Model: STOIIP

The basic formulas used for volume calculations are:

STOIIP = Net Pore Volume * So / Bo

Net Pore Volume = Bulk Volume * Net/Gross * Porosity

INPUT PARAMETERS:

• GRV = Bulk volume

• Porosity = Average porosity in oil pool

• So = Oil saturation within oil pool

• Bo = Formation Volume Factor

• Net/Gross = Ration of non-porous rock type



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Running Monte Carlo inPetrel

Process manager

Range of parameters and distributionare input

Results are listed in output sheet

Output is transferred back as point datawith attribute



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Monte Carlo Workflow

Output sheet header

Input Data

Output result andparameters ofeach calculation

Number of Run

Calculation

Input parameters:

GRV ( ft3)Sw, Poro (fraction)Bo (Res.ft3/St ft3)

STOIIP (bbl)



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• Purpose of Tornado chart• To show the effect of the input parameters without correlation• To show impact of assumptions and/or decision variables one

at a time• To measure the absolute change in an output value due to a

change in the input parameter• To determine where to begin adding assumptions to your

model

• To perform “sensitivity” analysis before defining assumptions

Tornado Chart

What does the Tornado chart do? Manipulates one input variableat a time, measuring the impacton the output Display the results on tornadoand spider charts



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Cross-Plotting 3 Parameters



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Filtering 3rd Parameter



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3D Model With Images



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Permeability Simulated(SGS) Independently

70,000 Point from reservoir cells 7,000 Cells



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Quantification of Uncertainty

Precision:

Accuracy : Systematic shifts due to environmental

correction or measurement tools/techniques

Difference between Core porosity and Phie

Variation in Modeling steps

Difference between well log porosity and scaled-

up porosity

Difference between scaled-up porosity and

modeled porosity

Difference between realizations on seed number

Difference between variogram range



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Statistics for Core Porosity

Name Type Min Max Delta N Mean Std Var Sum

Scaled-up Cont. 2.35 27.24 24.89 227 18.08 5.292 28.003 4104.0497

Well logs Cont. 2.35 28.62 26.26 592 18.855 5.157 26.59 11162.171

Statistics for Log Porosity

Scaled-up Cont. 0.68 29.91 29.23 441 17.8 6.37 40.57 7850.9

Well logs Cont. 0 30.72 30.72 2706 18.41 6.39 40.84 49812.24

Core Porosity Log Porosity

Porosity Measurement Statistic



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Statistics of Porosity Realizations OilAccumulation

Min Max Mean

Realization #1 2.06 28.79 14.54

Realization #2 1.23 29.07 14.6

Realization #3 0.83 27.48 14.63Realization #4 1.16 27.61 14.33

Realization #5 0.74 27.54 14.42

Realization #6 1.2 28.3 14.52

Realization #7 1.33 27.38 14.46

Realization #8 0.69 27.65 14.99

Realization #9 1.13 28.13 14.5

R8 has highest mean 14.99R4 has minimum mean 14.33

Difference is 0.66

Uncertainty onStochastic Modeling



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PhieCpor

One Well Porosity Comparison

Porosity Statistics

Phie Cpor

Min 2.79 9.212

Max 29.34 28.616

Delta 26.54 19.404Number of DefinedValues 296 92

Mean 18.36 20.41

Std. dev. 6.44 4.1544

Variance 41.54 17.259

Sum 5434.2 1877.7

Difference inmean is 2.05 PU

Q



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Quantification of Uncertaintyon Porosity Modeling

Log Porosity Core Porosity

Log Scaled

-Up Model Log Scaled

-Up

N 592 227 2706 441

Mean 18.86 18.08 17.78 18.41 17.80Std 5.16 5.29 6.23 6.39 6.37

Difference 0.78 1.08 0.61

Precision Error 0.21 0.35 0.12 0.30

Porosity Values

HC Porosity Uncertainty 2.06

Mean porosity 18.36

LC Porosity uncertainty -2.06



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Conclusion

Reservoir integration reduces

uncertainty significantly

Workflow both helps rapid modelingand data integration

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