2011 what's new at cmg event in perth - automated history-matching & optimization using cmost

8/12/2019 2011 What's New at CMG Event in Perth - Automated History-Matching & Optimization Using CMOST

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CMOST

CMGs Assisted History-Matching& Optimization Tool

Presentation first c reated by CMGs Chaodong Yang, Long Nghiem, Colin Card & Rob Eastick

For CMG Technical Symposium July 7-9 2010 - Calgary

Perth December 6, 2011


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CMOST Functions

CMOST can be used to perform:

Sensit ivity Analysis (SA)

History Matching (HM)

Optimization (OP)

Uncertainty Assessment (UA)

CMOST works with CMGs reservoir simulators


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CMOST History Matching

Field Case Study

C. Yang, L. Nghiem, C. Card, CMG

M. Bremeier, Wintershall

SPE109825, 2007 SPE Annual Technical Conference and Exhibition


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Model Overview

G1-97 G10-97G19-97

G2-97

-G6-97

G7-97

G9-97

G1-97 G10-97

G14-97G15-97G16-97

G17-97

G18-97 G19-97

G2-97

G4-97G6-97

G7-97

G9-97

,

000 551,000 553,000 555,000 557,000 559,000 561,000 563,000 565,000 567,000

3,

193,0

00

3,1

95,0

00

3,1

97,

000

3,

1

0.00 1.50 3.00 miles

0.00 2.50 5.00 km

0

Permeabili ty I, K Layer: 7


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Model Overview

1,219,617 blocks (290,649 active)

Highly heterogeneous perm distribution

55 faults with unknown transmissibi lities

12 production wells with 10 years of production history

All wells hydraulically fractured

Wells that were diff icult to match manually: G1, G3, G7, G10

IMEX used for this black oil history match


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CMOST History Match Parameterization

Parameters selected for variation during HM

1 critical gas saturation

5 fault transmissibility multipliers

11 well productivity index multipliers 28 permeability multipliers

1 volcanic rock boundary for well G3

1 well perforation file for well G9

A total of 47 parameters

Search space > 1020


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CMOST History Matching Results

550 total IMEX runs4 simultaneous 2-way parallel configuration

Runs made on 8-core Xeon 5400 3.2 Ghz PC

Total calendar time = < 3 days to reduce GlobalHM error from 20% to < 7%


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Base Case Results (Well G1-97)G1-97

Time (Date)

OilRateSC(m3/day)

WaterCutSC-%


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G1-97

Time(Date)

OilRateSC(m3/day)

WaterCutSC-%


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Base Case Results (Well G3-97)G3-97

Time (Date)

OilRateSC(m3/day)

WaterCutSC-%


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CMOST History Match Conclusions

CMOST can handle a large number of parameters &manage a large number of runs relatively quickly

47 parameters (> 1020 combinations)

550 runs in 3 days with one 8-core 3.2 GHz Xeon 5400 PC

Successful history match workflow demonstrated

Reservoir engineer: correct parameterization and

objective function definition

CMOST: effective and effic ient opt imization algorithm Hardware: suff icient computing power

3 days versus 4 months


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Optimization of 6-Well-Pair SAGD Model

onWell locationsOperating Conditions


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200 300 400 500 600 700 800

-

-190

-180

0.00 315.00 630.00 feet

0.00 100.00 200.00 meters

0

Model Overview

SAGD Model Cross Section

Pair1Pair2Pair3Pair4Pair5Pair6


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CMOST SAGD Optimization

Objective function

Bitumen pr ice = $30/bbl

Steam cost = $6/bbl

Interest rate = 10% yearly

Capital = $5,000,000 per well pair

NPVfield = NPVW1 + NPVW2 + NPVW3 + NPVW4 + NPVW5 + NPVW6

Two competing objectives Produce more oil

Reduce steam injection


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Base Case Results

Base case Steam injection pressure: 2600 kPa

Steam injection temperature: 226 C

Steam injected (Mm3) 13.97

Oil produced (Mm3) 3.58

Steam-oil ratio 3.90

Field NPV (M$) 119

Summary of base case simulation results


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CMOST Optimization Parameters

Well location

Well depth (16 parameters)

Well horizontal location (16 parameters)

Well operating conditions Injector max steam pressure (46 parameters)

4 time intervals

Steam temperature needs to be varied accordingly

Producer max steam rate (16 parameters)

Total number of parameters: 42


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CMOST SAGD Optimization Results


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200 300 400 500 600 700 800

-

-190

-180

0.00 315.00 630.00 feet

0.00 100.00 200.00 meters

0

CMOST Optimal Well Locations

Optimal Case

Pair1Pair2Pair3Pair4Pair5Pair6

Base Case

CMOST O ti l O ti C diti


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CMOST Optimal Operating ConditionsWell Pair 1

Time (Date)

WellBottom-holePres


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Summary of CMOST Optimization Results

Item Base case CMOST optimization Change

Steam injected

(Mm3)13.97 11.89 -15%

Oil Produced

(Mm3) 3.58 3.99 +11%

Steam-oil ratio 3.90 2.98 -24%

Field NPV (M$) 119 216 +81%

Number of runs 1 565

Computer time

(Xeon 5400)

1 hour with

4 cores

14 days with

2 x 4 cores


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Advanced Topics Using JScript

With dynamic JScript code execution, users cancustomize and extend CMOST via custom code

JScript code can appear in

Parameters

Objective functions

Constraints and penalty functions

Examples

Use Corey equation to create relative permeabil itytables

Link to Excel spreadsheet for NPV calculation

Read simulation log to obtain material balance error


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Advanced Topics Integration

CMOST wil l run the user-specified

executable as a final pre-processing

step when building dataset

BUILDER can be run silently

This makes it possible to link CMOST

with any application that supports

batch processing, such as geological

modelling tools like GOCAD and

JewelSuite


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Recent CMOST Applications

CO2 sequestration optimization

Flex well optimization

ASP history matching & optimization

Dynagrid tuning

Numerical tuning

Robust Optimization under Geologic

Uncertainty (SPE 141676)

90 100 110 120

10


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Robust Optimization of SAGDOperations under Geological

Uncertainties

Chaodong Yang, Colin Card, Long Nghiem, andEugene Fedutenko

Computer Modelling Group Ltd.


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Outline

Introduction

Nominal and robust optimization

Challenges of robust optimization

Proposed Method

Workflow

Robust optimization workflow

SAGD Performance Index

Optimization algorithm

Case Study

Conclusions and Ways Forward


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Nominal and Robust Optimization

Nominal optimization

Based on a single realization (but is i t right?)

Ignores geological uncertainties

The validity of optimum solut ions is often challenged

Robust optimization

Account for geological uncertainty

Seeks an optimal risk weighted solut ion that is mostlikely to give good performance for any realization of

the uncertainty


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Challenges of Robust Optimization

For each set of parameter values, 100 simulation runs arerequired. So the computation cost is 100 times higherthannominal optimization.

One set ofparameter values

Run for realization 100





Calculate robustobjective


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Challenges of Robust Optimization

Few cases will allow 100 x increase in simulation

CMGs Solution:

Classify like realizations to reduce total geomodels to

investigate (SAGD Performance Index or SPI)

Improve Optimization Engine

DECE Method combined with Proxy Modelling


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Proposed Realization Ranking

Realization 100

Realization 001

Realization 002

Realization 003

Realization 099

Rank allrealizationswith CMG

SPI

Realization R1

Pick NR

realizationsfor robustoptimization

Realization R3

Realization R9

Realization R4

Realization R8

Realization R7

Realization R6

Realization R5

Realization R2


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Classify like realizations

Expected productivity considering well connectivity and tortuosity

Pick a cell

Figures out a path from the injector to this cel l, then from this cel l to producer

Calculates the harmonic average permeabili ty of this path

Calculate harmonic average assuming clean sand along

the same path


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Connectivity equals the ratio of average permeability for therealization to the average permeabil ity for the clean sand case

The largest connectivity of a cell represents the optimum

path from the injector to producer

Certain rules are appl ied in the search for opt imum path

Steam tends to r ise Oil tends to drain downwards

Steam/Oil will take path of least resistance

In order to reach the destination, the steam and oil can occasionally break

these 3 rules (such as oil above shale layer may travel horizontal ly f irst)


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The SAGD Performance Index is the average

optimum connectivity of the cells in the

realization

If there are multiple well pairs, the above

formula will be applied to all the well pairs


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Optimization Using Proxy

Run simulations using the design

Get init ial set of training data

Build a proxy model using training data

Find possible optimum solutions using proxy

Run simulations using these possible solutions

Satisfy stop criteria?

Stop

Add validatedsolutions totraining data

Generate init ial Latin hypercube design

Polynomial

Ordinary kriging

Yes

No


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Optimization Examples

Kriging 30 LHD Runs Kriging 60 LHD Runs

Kriging 180 LHD Runs DECE

C S d


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Case Study

Where do we place our SAGD Well Pairs?

How do we operate these Well Pairs?

How do I ensure my design is optimal for all

possible geologic scenarios?

R i M d l


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Reservoir Model

Obj ti F ti


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Objective Function

NPV Steam cost

Steam rateBitumen rate

Bitumen price

Well capital

Discount rateTime interval

O ti i ti P t


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Optimization Parameters

Location of Each Well Pair

Depth

Injector/Producer Spacing

Well Pair Spacing

Injection Pressure

Can vary with Time

2400-3000 kPa

Injection Rate Can Vary with Time

Producer Sub-Cool (steamtrap) Temperature

R ki f R li ti


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Ranking of Realizations

R P


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Run Progress

O ti l W ll O ti


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Optimal Well Operation

O ti l W ll Pl t


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Optimal Well Placement

NPV Hi t


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NPV Histograms

NPV C l ti P b bilit


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NPV Cumulative Probability

Computational Cost


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Computational Cost

9 realizations is sti ll probably excessive,but a minimum of 3 is crit ical.

Conclusions


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Conclusions

Robust optimization is able to find an optimal risk weightedsolution that gives good performance for any plausiblegeologic realization

To make robust opt imization practical for SAGD process,

we need a workflow that can Account for the impact of geological uncertainty on optimization

Significantly reduce computational time

Key technique of the workflow presented

Rank the entire set of geological realizations Select a small set of representative realizations for robust

optimization

CMOST


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CMOST

Thank you for your time!

Any Questions?

2011 what's new at cmg event in perth - automated history-matching & optimization using cmost

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