2014 CMOST Presentation

7/26/2019 2014 CMOST Presentation

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What this course is not

A treatise on optimizat ion theory & methods A course on the math behind CMOST

An in-depth discourse on applied historymatching and optimization

We can provide some guidance based onour experience but we do not claim to beexperts in the application area

3

Agenda

CMOST overview

CMOST functionality and Tutor ials

Sensitivity Analysis

History Matching

CMOST functionality and Tutor ials

Optimization

Uncertainty Assessment

4


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Overview

What is CMOST?

CMOST is CMG software that works in conjunctionwith CMG reservoir simulators to perform thefollowing tasks:

6


Better understanding of a simulation model Identify important parameters

History Matching

Calibrate simulation model with field data Obtain multiple history-matched models

Optimization

Improve NPV, Recovery, Reduce cost

Uncertainty Analysis

Quantify uncertainty Understand and reduce risk


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Typical Workflow for a Brown Field

7

Reservoir model Field history f ile

Matched model

Forecast model

Optimal modelOptimal operating

conditions

Uncertainty

quantification

History matching

Parameter

Histograms

Optimization

Uncertainty

assessment

Parameter

Sensitivities

Sensitivity analysis

CMOST View of Simulation Models

Simulation Model

y1=f1(x1, x2, , xn)y2=f1(x1, x2, , xn)

ym=f1(x1, x2, , xn)

Parameters

x1, x2, , xn

Objective Functions

y1, y2, , yn


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CMOST Process

Selectcombination of

parameter values

Substituteparameter valuesinto simulation

dataset

Run simulation

Analyze results

9

Parameterization

Objective

Functions &

Proxy Analysis

Experimental

Design

& Optimization

Algorithms

CMOST User Interface


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How Input Data is Organized

Define what data to be extracted from simulation(Plots and Formulas)

Define how the simulation model isparameterized(Inputs)

Define objective functions to be calculated(Outputs)

General Settings


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General Properties

All input fi les are entered on the generalproperties page:

Base Dataset (required)

Master Dataset (required)

Results template files

Measured Data

Field History Files

Log Files

Fundamental Data

Fundamental data defines which 2D curves of simulation

results need to be viewed

Original Time Series 2D plots similar to Results Graph

X-axis: time

User-defined Time Series Custom formula based 2D plots

X-axis: time

Property vs. Distance Series

X-axis: Distance Fluid Contact Depth Series

Depth of Fluid Contact (based on fluid saturation) vs. Time


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Parameterization

Parameterization

Parameters are variables in the simulationmodel that will be adjusted when creating newdatasets

- E.g. Porosity, permeability, etc.

To determine the location in the dataset tosubstitute values, a master dataset must becreated (.cmm)

A master dataset is almost identical to a normal

simulation dataset except CMOST keywordshave been added to identify where a parametershould be added

- Acts as a template for creating new datasets


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Master Dataset

Master Dataset

A master dataset can be created in multipleways:

CMOST Editor

Builder

Text editor (Notepad, Textpad, etc.)


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Parameterization of Simulation Model (Builder)

List of

ParametersSelect

Dataset

Section

Select

Parameter from

Section

Export Master Dataset (Template

file)

Parameterization of Simulation Model (Text)

Dataset Template

Complementary to Builder

Create CMOST parameters

Better syntax highlighting Highlight CMOST parameters

Fold no-need-to-see sections

Easy navigation Different sections of the dataset

Navigate CMOST parameters

Handle include files Create/extract include files

View include files

Parameterize include files


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Master Dataset Syntax

Original Dataset:

PORCON0.20

Master Dataset:

PORCONthis[0.20]=Porosity

Simulator

Keywords

CMOST

Start

Original

(Default)

Value in

Dataset

Variable

NameCMOST

End

No Spaces in

CMOST Portion

Variable Names

Case Sensitive


Formulas can also be used wi th 1 or more variables:

PORCON0.20*PorosityMultiplier

Simulator

Keywords

CMOST

Start

Formula CMOST

End

Default value

optional


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Values in regions of the reservoir can be modif ied using

MOD simulation keywords

PORCON0.20

MOD

1:52:81:10*this[1]=PorosityMultiplier1

6:102:81:10*this[1]=PorosityMultiplier2

Block RangesI:IJ:JK:K

Parameter Definition

Continuous parameter

Lower and upper limit define the sampling rangeused by study engines

Discrete parameter

Real, Integer, and Text

Each discrete text value also requires anumerical value

Formula

Value based off of other parameters values


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Dependent Parameters Using Formula

Syntax highlighting

Shows what variables are available to beused to create formulas

Test and check the formula anytime

Hard Constraints

Criteria that must be satisf ied for a datasetto be created

Used to eliminate unrealistic datasets

E.g. Horizontal permeability should begreater than vertical permeability


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Pre-Simulation Commands

Passes dataset to separate appl icationbefore submitting to simulator

Run Builder Silently

Run GOCAD Command Silently

Run User Defined

Can be used to create new geostatist icalrealizations, recalculate formulas in Builder,

recalculate rel. perm. curves, etc.

Coupling with Geological Software

H Pair

Geological model Simulation model

Simulation model


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Objective Functions

Objective Functions

An Object ive Funct ion (OF) is something (anexpression or a single quantity) for which

you wish to achieve some goal

Usually this goal is to achieve a minimum ormaximum value

In the case of History Matching, oneusually wishes to minimize an error

between field data and simulation In the case of Optimization, one usually

wishes to maximize something like NPV


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Basic Simulation Results

Values directly taken from simulation resultswith no modification

History match error

Percentage relative error

Perfect match: 0%

Net Present Value

Simplified NPV calculation

Can be used to construct user-definedobjective functions which utilize simulationresults discounted by time as variables

Objective Functions

Characteristic Date Times

Specific Dates

Date where maximum or minimum value isfound

Date when value surpasses a specified criteria

Advanced Objective Funct ions

User defined objective function based on

formula or code (jscript or python) Soft Constraints

Re-evaluates objective functions based onsimulation results

Objective Functions


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What are Dynamic Date Times

Characteristic date times, within certain timeseries range, that meet certain criteria

Examples:

First time oil rate is higher than 100 bbl/d;

Last time SOR is bigger or equal to 6;

First time oil rate is higher than 100 bbl/d , andSOR is smaller than 4

Why Need Dynamic Date Times

Fixed date time (e.g. simulation stop time, or2014-8-15) is fixed for all jobs. Objective functionare calculated on fixed date times for all jobs.

In some cases, we want to get information on datetime that some specified condition are meet (e.g.oil rate>100). However, these times are not the

same for all simulation jobs.Thus, they are dynamic.


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Dynamic Date Times: Maximize Peak NPV

Peak NPV

Dynamic Date Times: Plateau

Optimization

Plateau period

Oil produced at plateauAverage oil rate at plateau


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Characteristic Date Times Page

Start & Stop Time

To name certain

data time

Data times that

meet certain

criteria

Define Dynamic Date Time

Criteria Time Series

Criteria User defined time series


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Characteristic Time Durations

Defined in Basic Simulation Result Page,then can be used as objective functions.

User-defined Objective Functions

Use Excel spreadsheet

Map CMOST parameter values to cells

Map simulation results to cells

Use Jscript or Python code

Use executable provided by user (e.g.MATLAB)

Preview calculation result using base case


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Use Excel Spreadsheet

Af ter each simulation is done:

CMOST write Parameter and simulation resultsto Excel cells;

Excel calculate objective function using formulaor VBA code;

then CMOST read value back into CMOST, anduse it the objective function value.



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Sensitivity Analysis Goals

Determine which parameters have an effecton results

E.g. I expect that rock compressibility isbetween values A and B. Does thisuncertainty impact my results?

Determine how much of an effectparameters have on results

E.g. If permeability is increased by 50mD,

how much will cumulative oil increase?

Sensitivity Analysis Process

Select parameters to analyze

E.g. porosity

Select range of values to analyze

E.g. between 20-30% porosity

Select results (Objective Functions) toanalyze

E.g. Cumulative Oil


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Sensitivity Analysis Methodology

One Parameter at a Time (OPAAT) Each parameter is analyzed independently

while remaining parameters are set to theirreference value

Response Surface Methodology

Multiple parameters are adjusted togetherthen results are analyzed by fitting aresponse surface (Polynomial equation) to

results

One Parameter at a Time (OPAAT)

This method analyzes each parameterindependently

While analyzing one parameter, the methodfreezes the other parameters at their referencevalues (Median or Default)

This measures the effect of each parameter onthe objective function while removing the effects

of the other parameters.


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Benefits Simple to use

Results easy to understand

Results not complicated by effects of otherparameters

Drawbacks

Results are focused around the referencevalues

Results can change dramatically ifreference values change

Configure OPAAT Engine

Select reference

values to be used

Non-monotonic

variables require many

levels of parameter

values to be tested

Parameter

Obje

ctive

Function


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Porosity = 0.2CumOil = 33004bbl

Porosity = 0.3CumOil = 44176bbl

Porosity = 0.25(reference value)CumOil = 40416bbl

Note: Min and max objective function values donot always correspond with Min and Maxparameter valuesCheck cross plots to verify


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Length of bar gives themaximum change in theobjective function over theparameter range

Response Surface Method (RSM)

Correlation betweenresponse andparameters

NPV = f(x1, x2, ,xn)

The response surfaceis a proxy for thereservoir simulator thatallows fast estimationof the response

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Multiple Parameters values are changedsimultaneously

The combination of parameter values that arechosen are based off of an experimentaldesign

Response sur face (polynomial equation) is f it tosimulation results

Linear

Linear + Quadratic

Linear + Quadratic + Interaction terms

Response Surface Proxy Models

Linear Model

Linear + Quadratic Model

Linear + Quadratic + Interaction

Statistically insignificant terms are automatically removed

Model type is automatically chosen but can be changed if necessary

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SA Using RSM Engine

Handles problematic

simulation runs

Specify desired

accuracy, the engine

will c reate and run

experiments as

needed.



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For comparing effect between differentparameters, parameter ranges are

normalized between -1 and 1

In the resulting tornado plot, 2*Coefficient ofthe normalized polynomial relation is g iven

Represents average change going frommin to max parameter value when looking

at linear effects Similar to bar length from OPAAT method



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Increasing PERMH_L1(permeability) from2625mD to 4375mDresults in an increase inCumulative Oil of 12,461STB on average


If a parameters has a non-linear relation withthe objective functions, a quadratic term mayalso be given (x2)

If modifying 2 parameters at the same time hasan effect stronger than the sum of theirindividual linear or quadratic effects, a crossterm may be given (x*y)


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Quadratic and Linear Effect Table

X-axis: Parameter

Y-axis: Objective Function

Any Questions?

Now Hands on Work


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Control Centre

Engine Settings

Defines task type

Task type can be modified from whatwas originally selected when creatingthe study

Any other options related to the enginecan be modified from this page


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Engine Settings

Study TypeEngine

Simulator Settings

Simulation related settings:

Schedulers

Simulator version

Number of CPUs per job

Maximum simulation run time

Job record and file management

Data I/O Cleanup


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Submit Simulations to Compute Cluster

CMG Scheduler Microsoft HPC IBM Platform LSF Oracle Grid Engine Portable Batch System (PBS/TORGUE)

Optimize Dataset I/O Section

Switches to reduce output file size

Disable restart records writing Disable grid records writing in OUT

Disable grid records wri ting in SR2


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Optimize Dataset I/O Section

What does CMOST do?

Benefits

Remove I/O bottleneck Reduce occurrence of strange problems

Reduce support troubleshooting time

Experiments Table

List of Experiments

Parameter values used

Objective function results

Able to sort and filter results

Open in Builder and Results

Add additional experiments

User defined Predefined experimental designs(fractional factorial, latin hypercube, etc.)


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Simulation Jobs

List of Simulations Scheduler information

Start Time

End Time

Scheduler Name

Status

File information

Name and location

Normal/Abnormal termination

Any Questions?

Now Hands on Work


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Results & Analyses

Results & Analyses

Al l result objects are dynamically created onthe fly using the data stored in experiment

table

Al l types of result objects are available forany study type

HM & OP will automatically have sensitivityand proxy result if there are enough

experiments.


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Results & Analyses

Parameters Run progress

Parameter value vs. experiment number

Histogram

Frequency that range of parameter valueswere chosen

Cross plot

Plot parameter values vs. other data

Current parameter always on y-axis

Results & Analyses

Time Series and Property vs. Distance Plots of simulation results as defined in the Fundamental Data section


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Results & Analyses

Objective Functions

Run progress

Objective function value vs. experiment number

Histogram

Frequency that range of objective function values occurred

Cross plot

Plot objective function values vs. other data

Current objective function always on y-axis

OPAAT Analysis

Results from One Parameter At A Time sensitivity analysis

Proxy Analysis

Proxy verification Response Surface sensitivity analysis results

Monte Carlo Simulation uncertainty assessment results

Sensitivity Analysis -

Validating Results


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Proxy Analysis for Objective Functions

Polynomial proxy Linear/Quadratic/Interaction

RBF neural network

Statistics

Response Surface Verification

When using the response surfacemethodology, one should verify that the

response surface provides a valid match to

the simulation data

This can be verified through

Response Surface Verification Plot

Summary of Fit Table Analysis of Variance Table

Effect Screening


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Gives a visual overview of how the proxy modelfits the actual simulation results

Distance from each point to the 45 degree lineis the error/residual for that point

Points that fall on the 45 degree line are thosethat are perfectly predicted



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Summary of Fit Table

R2 is a measure of the amount of reduction in thevariability of the response obtained by using theregressor variables in the model.

R2 of 1 occurs when there is a perfect fit (the errors areall zero).

R2 of 0 means that the model predicts the response nobetter than the overall response mean.


R2 can be adjusted to make it comparable overmodels with different numbers of regressors byusing the degrees of freedom in its computation.

Here n is the number of observations (trainingjobs) and p is the number of terms in the

response model (including the intercept). When R2 and R2adjusted differ dramatically, there is

good chance that non-significant terms havebeen included in the model.


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R

2

adjusted

(Example)

Linear:

R2=0.9683

Quadratic:

R2=0.9715

4 Samples

n=4

R 1

4 1

4 2 1 0.9683 .

R 1

4 1

4 3 1 0.9715 .


PRESS is the prediction error sum of squares.

R2prediction gives some indication of the predictivecapability of the regression model.

For example, we could expect a model withR2prediction=0.95 to explain about 95% of the

variability in predicting new observations.


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To calculate PRESS, Select an observation i.

Fit the regression model to the remaining n-1observations and use this equation to predict thewithheld observation yi.

Denoting this predicted value by y(i), we can find theprediction error for point i as e i=yi-y(i).

The prediction error is often called the ith PRESSresidual. This procedure is repeated for eachobservation i=1,2,3,,n producing a set of n PRESSresiduals: e1, e2, e3en.

The PRESS statistic is then defined as the sum of

squares of the n PRESS residuals:

R

2prediction

Example

x y y(i) ei ei2

1.000 1.900 1.657 0.243 0.059

2.000 2.450 2.484 -0.034 0.001

3.000 2.950 3.194 -0.244 0.060

4.000 3.890 3.483 0.407 0.165

PRESS: 0.285

SS

(Total):

2.143

R2prediction 0.867


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Mean of Response

The overall mean of the response values. It isimportant as a base model for predictionbecause all other models are compared to it.

Standard Error

Estimates the standard deviation of the randomerror. It is the square root of the mean squarefor Error in the correspondingAnalysis ofVariance table. Standard error is commonlydenoted as .

Analysis of Variance Table

Degrees of Freedom Total = Number of Samples 1

Model = Number of coefficients for the response surface (notincluding intercept)

Error = Total Model

Sum of Squares Total: Sum of Squared distances of each response from the

sample mean

Error: Sum of squared differences between the fitted (RS) values

and the actual simulated values Model = Total Error

Mean Square (Sum of Squares)/(Degrees of Freedom)

Converts sum of squares to an average


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Analysis of Variance Table

F Ratio

Model mean square divided by the Error mean square

Tests the hypothesis that all the regression parameters(except the intercept) are zero (have no effect on theobjective function)

Prob > F

The probability of obtaining a greater F-value by chancealone if the specified model fits no better than the overallresponse mean.

Significance probabilities of 0.05 or less are oftenconsidered evidence that there is at least one significantregression factor in the model

Summary of Fit and Analysis of

Variance Table


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Response Surface Model Checklist

Check Predicted vs. Actual Is there a good fit?

Any outliers?

Check Summary of Fit

Is R-Square adjusted and R-Square predictionlarge enough (>0.5)?

Check Analysis of Variance For a decent model, Prob>F should be very

small ( |t|

Probability of getting an even greater t-statistic (in absolute value),given the hypothesis that the parameter (coefficient) is zero.

Probabilities less than 0.1 are often considered as significantevidence that the parameter (coefficient) is not zero.

Used to filter statistically insignificant terms


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Effect Screening using Normalized

Parameters (-1, +1)

VIF (Variance Inflation Factor) Measure of multi-collinearity problem

Multi-collinearity refers to one or more near-lineardependences among the regressor variables due to poorsampling of the design space

Multi-collinearity can have serious effects on the estimatesof the model coefficients and on the general applicability ofthe final model

The larger the variance inflation factor, the more severe themulti-collinearity.

It is suggested that the variance inflation factors should notexceed 4 or 5.

If the design matrix is perfectly orthogonal, the varianceinflation factor for all terms will be equal to 1.

Effect Screening using Normalized

Parameters (-1, +1)


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Increasing PERMH_L1(permeability) from2625mD to 4375mDresults in an increase inCumulative Oil of 12,461STB on average

Proxy Dashboard

See quick estimation of effects ofparameters on resul ts at all simulation times

See results immediately without needing towait for additional simulation

Can assist in manual history matching oroptimization

CMOST can create dataset and run

simulation to verify proxy results


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Proxy Dashboard

Build ProxyModel

SelectComparison Case

What-if Scenario:Choose ParameterInput

What-if Scenario:Visualize Estimate ofCurve

What-if Scenario:

Estimate ObjectiveFunctions

Proxy Dashboard Method

Curve divided into 100 points

Response surface model created for eachpoint based on simulation results

Polynomial

RBF neural network

When parameter values are adjusted, eachpoint along the curve is recalculated using

the response surface


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Any Questions?

Now Hands on Work

History Matching and

Optimization


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History Matching Goals

In history matching, we are trying to reduce theerror between the simulation results and field

measured data

By matching the simulation model to the historical

behaviour, we have more confidence that the model

will be able to predict future behavior

When creating a simulation model, there may be

uncertainty in the input parameters. These will bethe parameters that should be adjusted when

history matching

History Matching Process

Select parameters to analyze

E.g. porosity, permeability


E.g. between 20-30% porosity

Select results (Objective Functions) tomatch

E.g. Cumulative Oil

CMOST will search for the best combinationof parameter values that will give the lowest

history match error


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Objective Function Hierarchy

A hierarchy is used when optimizing the objectivefunction

Upper terms are calculated as a weightedaverage of the lower terms

GlobalObjectiveFunction

LocalObjective

Function 1

Term 1 Term 2 Term 3

LocalObjectiveFunction 2

Term 1 Term 2

LocalObjectiveFunction 3

Term 1 Term 2 Term 3

Objective Function Hierarchy

Typically common items are grouped together

E.g. The local objective functions mightrepresent the error for a well and the termsmight represent the measured data for thatwell

Total Error

Well 1 Error

OilProduction

Error

WaterProduction

Error

GasProduction

Error

Well 2 Error

Bottom-holePressure

Error

OilProduction

Error

Well 3 Error

OilProduction

Error

WaterProduction

Error

GasProduction

Error


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Calculating History Match Error

Nt

)Y(YNt

1t

2m

t

s

t

simulated measured

For each measured data point, calculatedifferent between simulation and measuredresult

Square terms to make them positive Sum up all of the points at all times

Divide by the number of measurements to getaverage square

Square root to get average error

Number of measurements


To compare error of terms with dif ferent units, errormust be normalized

This is done by dividing by the maximum difference in

measured values Measurement error can also be included

Merr represents 1 standard deviation from the mean

Value of 4 is used to include 2 standard deviations on each side of themean (95% confidence)

j

m

j

Nt(j)

1t

2m

j,t

s

j,t

jMerr4Y

Nt(j)

)Y(Y

TermError

maximum di fferencemeasurement error


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Each Local Objective Function is made up ofa weighted arithmetic average of each of theTerms

N(i)

1j

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N(i)

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Calculating Global History Match Error

The Global Objective Function is made up of aweighted arithmetic average of each of theLocal Objective Functions

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w

N

1i

i

N

1i

ii

global

w

Qw

Q

Field Data Weighting

Able to weight each measured data pointindividually

Remove or reduce weight of outliers


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Optimization Methods

CMG DECE (Designed Evolution, ControlledExploration)

Particle Swarm Optimization

Latin Hypercube plus Proxy Optimization

Random Brute Force Search

Optimization Philosophy

Mathematical optimization

Mathematicians are particularly interested in finding the trueabsolute optimum.

Optimum 0.000001 is much better than 0.01 even though it maytake 20 extra days to achieve the former.

Engineering optimization

Engineers are more interested in quickly finding optima that areclose to the true optimum.

Optimum 0.01 is much better than 0.000001 if it takes 20 less

days to achieve the former.CMOST Optimization Philosophy

Engineering optimization

Not intended to solve pure mathematical problems

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CMG DECE Optimization Algorithm

Run simulations using the design

Get initial set of training data

Run simulations

Satisfy stop criteria?

Stop

Add new

solutions to

training data

Generate initial Latin hypercube design

Yes

No

Exploration

(get more information)

Exploitation (find optimum)

Success?

Yes

No

CMG DECE Optimization Algorithm


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DECE Characteristics

Handles continuous & discrete parameters Handles hard constraints

Asynchronous complete utilization of distributed computingpower

Fast and stable convergence

PSO Optimization Algorithm

118

A populat ion based stochast ic optimization techniquedeveloped in 1995 by James Kennedy and Russell Eberhart.

Let particles move towards the best position in search space,remembering each local (particles) best known position and

global (swarms) best known position.


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119

Local

Area


120

Next

Case


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Algorithm

Run simulations using the design

Get initial 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 vali dated

solutions to

training data

Generate initial Latin hypercube design

Polynomial

RFB Neural

Network

Yes

No


Algorithm


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Proxy Optimization

Latin hypercube design

Optimization using proxy

Random Search

Parameter value combinations randomly untilthe max number of simulator calls has beenreached

No trend to results (scatter)

Only use if search space is small


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Any Questions?

Now Hands on Work

Optimization Goals History matching and opt imization are very similar in that ineach one would l ike to find the maximum or minimum of an

objective function

In history matching, we are trying to reduce the error betweenthe simulation results and field measured data

With optimization, we are trying to improve an objectivefunction

Find maximum NPV

Find maximum recovery Etc.

Typically w ith optimization, the parameters that will beadjusted are operational parameters as opposed to reservoir

parameters when history matching


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Optimization Process

Select parameters to analyze E.g. Injection rate, well spacing


E.g. between 200-500bbl/day injection rate

Select results (Objective Functions) to improve

E.g. NPV, recovery factor

CMOST will search for the best combination ofparameter values that wil l maximize your objective

function

In some cases we may want to minimize anobjective function such as when looking at runtimes during numerical tuning

Calculating Net Present Value (NPV)

Net Present Value (NPV) is often usedas an economic indicator to evaluate thevalue of a project

A discount rate (I) is used to incorporatethe time value of money

Money now is worth more than money later


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Calculating Net Present Value (NPV)

In CMOST, the cash f low is always calculatedbased on the period that has been selected.

Daily Monthly Quarterly Yearly

Yearly discount rate will be converted to theperiod of interest

E.g. Daily:

Common OP Engine Settings

OP

DECE

LHD Plus Proxy

PSO

Random Brute Force

All methods use the

same stop criterion

Which objective

function to

optimization

Maximize or minimize


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Multi-objective PSO (SPE 170024)

Multiple-objective optimization: Optimizingmultiple, maybe conflicting, objective functions

simultaneously

Two approaches:

1. Optimize an aggregated global objective function

2. Pareto Optimization, e.g. Multiple-objective PSO

Multi-Objective PSO

Select the engine

How many simulations?

First objective function

Maximize or minimize?

2nd objective function

Maximize or minimize?


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How MO-PSO Works

Domination: Better in every objective functionLeader:A non-dominated solution

Pareto front: The ensemble of leaders

Leader

Dominated solution

Pareto front

f1

f2

a

b a dominates b

Leader Selection

Leader is randomly selected for each particle

Least crowded leaders are given high priority,trying to find an adequately spread Pareto front

f1

f2

a

b

c

d

ef

Priority Leader

1 a, f

2 e

3 d4 c

5 b


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Case Study: Numerical Tuning

Numerical tuning of a SAGD model for a realfield

Full model takes >42d to run

After cleanup & tuning: 7d to finish (SPE165511)

Sub model: 4 slices (4x600x50) takes ~1h torun the first 6 months

Colin Card et.al. A New and Practical Workflow of Large Multi-Pad SAGD Simulation- A

Corner Oil Sands Case Study, SPE HOCC, SPE 165511, June 2013.

Single Objective PSO Optimization

Conflicting objective functions:

1. Run time

2. Material Balance Error

3. Solver Failure Percent

GlobalObj =(50,000*MaterialBalanceError+1*RunTime+1,000*SolverFailurePercent)/51,001

Use PSO to minimize GlobalObj


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MO-PSO Optimization

Same set of parameters

Minimize two conflicting objective functions:MaterialBalanceError& RunTime

GlobalObj & SolverFailurePercent are alsocalculated, just for comparison

Pareto front @500 MOPSO & 1000

SPSO Experiments

2500

2600

2700

2800

2900

3000

3100

3200

3300

3400

3500

0 0.005 0.01 0.015 0.02 0.025 0.03 0.035 0.04

MOPSO

SPSO

RunTime(s)

Material Balance Error (%)


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Any Questions?

Now Hands on Work



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Uncertainty Assessment (UA)

Once an opt imum operating strategy has beendeveloped for one or more history match models the

question remaining is:

Given residual uncertainties in the HM (or other)variables, what impact will those uncertainties haveon the NPV of the optimum cases(s)?

How does this work?

Even with simple geological models we are still likelyto have more than one set of geological parametervalues that give an acceptable HM, thus indicating

some uncertainty in these values

141


How does this work?

If we have more than one geological realization thatgives an acceptable HM we have by definition anuncertainty as to which realization best reflects reality

Thus we need to see how the NPV of our optimumcases is impacted by the uncertainty in theserealizations

It is important to recognize that the HM processdevelops alternative realizations and that parameterswhich are part of these realizations cannot have theirvalues changed independently and arbitrarily


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Monte Carlo Simulation

Input probabilitydistributions derived fromexperience

Pick random values (thatfollow input distribution)and calculate NPV

Repeat for thousands ofiterations

12 13 14 15 16 17 18 19 20 21 22 23 24 25 26

Net present value (M$)

DWOC

8281 83

p

DWOC

DWOC

8281 83

p

DWOC

SORG

0.300.25 0.35

p

SORG

SORG

0.300.25 0.35

p

SORG

SORW

0.300.25 0.35

p

SORW

SORW

0.300.25 0.35

p

SORW

Probability distribution for HM parameters

NPV=F(DWOC, SORG, SORW, )

Prior Probability Density Functions

Defines l ikelihood of a parameter value beingselected in Monte Carlo Simulation for continuous

parameters

Uniform

Triangle

Normal

Log Normal

Custom

Probability for each value defined for discreteparameter types


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Probability Distributions in CMOST

Normal Lognormal Triangle

Uniform Custom Discrete

Parameter Correlations

Some parameters may be related to eachother

E.g. porosity and permeability maycorrelate with each other

Correlation coefficient defines how closelyrelated parameters are to each other

Value of 1 means parameters are directly

related Value of 0 means that parameters have no

relation with each other


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POR PERMHPERMV

POR

PERMH

POR

PERMV PERMV

PERMH

Parameter POR PERMH PERMV

POR 1 0 0

PERMH 0 1 0

PERMV 0 0 1

Sampling of Correlated Parameters

Calculated Values

Desired Values

POR

PERMH

POR

PERMV PERMV

PERMH


POR 1 0.6 0.4

PERMH 0.6 1 0.8

PERMV 0.4 0.8 1


POR 1 0.582 0.385

PERMH 0.582 1 0.79

PERMV 0.385 0.79 1

Sampling of Correlated Parameters


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Parameter Correlations

0.0 0.25

0.50 0.75

Uncertainty Assessment (UA) Objective functions can be evaluated using 2 methodsfor Monte Carlo Simulation

Response surface as a proxy to reservoir simulation

Running reservoir simulation

Running reservoir simulation is often too slow toevaluate the response so the proxy method is of ten

preferred

Situations to run reservoir simulation for Monte Carlosimulation:

You want to validate MCS-proxy result

Building proxy is not feasible. For example,

Multiple Geostatistical realizations are used

Multiple HM models are used


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How do we do this? The RS is generated by creating an

experimental design and providing and astatistical distribution for each uncertainparameter

The process is the same as polynomialresponse surface modelling for sensitivityanalysis

RBF neural network type proxy models alsoavailable for Uncertainty Assessment

A Monte Carlo simulation is then run todetermine the NPV distribution

UA Using MCS-Proxy Engine

Handles problematic

simulation runs

Specify desiredaccuracy, the engine

will c reate and run

experiments as

needed.


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User Defined Study Type

User-defined Study Type

User

Defined

Manual Engine

External Engine

All ows the use of user s

own optimizationalgorithm.

All exper imen ts are to be

created by the user

explicitly through:

Classical experimentaldesign

Latin hypercubedesign

Manual


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When to Use Manual Engine

Use classical experimental design for SA andUA

Precise control on the number of Latinhypercube experiments

Run additional experiments after aSA/UA/HM/OP run is complete.

Create new experiments using usersoptimization algorithm

Working With CMOST


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Main CMOST Components

Base Files To begin a CMOST project, a completed

simulation dataset (.dat) along with itsSimulation Results (SR2: .mrf, .irf) files arerequired

CMOST Project

A CMOST Project is the main CMOST file thatcan contain multiple related studies

CMOST 2013 File System

Project

Name:

SAGD_2D_UA

Project

File:

SAGD_2D_UA.cmp

Project

Folder: SAGD_2D_UA.cmpdBest

practice: All

files

related

to

the

project

should

be

stored

in

the

project

folder.


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Main CMOST Components

CMOST Study A CMOST study contains all of the input

information for CMOST to run a particular

type of task

Information can be copied between studies

Study types can be easily switched

The new study type will use as muchinformation from the previous study type as

possible

CMOST 2013 File System (contd 1)

StudyName:BoxBen

Study File:

BoxBen.cms

Study

File

Auto

Backup:

BoxBen.bak

StudyFolder: BoxBen.cmsd

Dont

modify/delete

files

in

the

study

folder

unless

you

know

what

youre

doing.


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CMOST 2013 File System (contd 2)

Vector

data

repository

file:

*.vdr

VDR

stores

compressed

simulation

data

required for

objective

function

calculations

SubsetofSR2results

Never

modify

or

delete

vdr

files

manually

Reusing and Restarting a Study

After you run an engine, you can go back to change input data.Experiments inside a study will be automatically reused.

If new parameters are added, you need to resolve reuse pendingexperiments.

After you finish the changes, click start engine to restart.

AutoSynchronization

Auto/ManualReprocessing


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Reusing Data from Another Study

Licensing Multiplier

CMOST uses only partial licenses whenrunning simulations

E.g. Run 2 STARS simulations while usingonly 1 STARS license

Applies to other license types (Parallel,Dynagrid, etc.)

IMEX 4:1 GEM 2:1

STARS 2:1

x 4

x 2

x 2


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CMOST Input Data Features

Quality Data

Quality Result

Further Assistance

Email: [email protected]

Zip an entire project or selected studies

Email or ftp the zip file to CMG


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Diagnostic Zip for Support Request

Diagnostic Zip for Support Request


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Any Questions?

Now Hands on Work

2014 CMOST Presentation

Documents

Transcript of 2014 CMOST Presentation