7/27/2019 A Novel Framework for History Matching Incorporating Geo-Engineering Knowledge- Teal South Case Study

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BACKGROUND

Arash Mirzabozorg, University of Calgary, Long Nghiem, Computer Modelling Group Ltd., Zhangxing Chen, University of Calgary

A Novel Framework for History Matching Incorporating Geo-Engineering

Knowledge: Teal South Case Study

METHODOLOGY

PURPOSE

RESULTS

In order to improve the speed and solution diversity in assisted history

matching and optimization workflows, recent research has focused on

using population-based sampling algorithms, which have shown a track

record of success in both academic and real-world problems. However,

they are mainly criticized for being a black-box tool, as there is no

capability for guiding the sampling algorithms to a specific region in

the search space. They are also blamed for not handling the engineering

constraints, which have resulted in many realizations that have perfect

match quality but failed to honour the relationships that exist among the

parameters in a constrained system. This means that they are good from

the match quality point of view but are inconsistent with the physical

and geological knowledge of a field.

Fuzzy Logic (FL) has shown to be an effective way to handle the

challenges associated with vague parameter descriptions and helps

engineers to incorporate their knowledge and belief of the realistic

conditions of a field under study during the history matching and

optimization problems.

A framework is introduced to incorporate engineering knowledge and

handle constraints in assisted history matching and optimization tasks

by integration of a rule-based fuzzy system with Differential Evolution

(DE) as a population-based sampling algorithm.

Mamdani Approach

Fuzzification

Fuzzy Rule Evaluation

Obtain Rule Conclusion

Rule Output Aggregation

Defuzzification

P5(KH5)

P2(KH2)

DE

P5(KH5)

P2(KH2)

A Fuzzy Inference System (FIS) is a way of mapping an input

space to the output domain using FL. A FIS attempts to

formalize the reasoning process of human language by means

of building fuzzy IF-THEN rules. Crisp inputs are fuzzified

using input membership functions and are then processed in

the FIS. The FIS drives its judgment based on the rule-based

section of the workflow and produces fuzzy outputs. These

outputs are then defuzzified to obtain crisp values for the

output parameters. The fuzzy inference engine reflects how

engineers make decisions regarding the definition of

parameter values/relationships and constraint-violation

thresholds. Its objective is to reduce or eliminate the need for

higher-level human control during history matching and

optimizations workflows.

The concept of Fuzzy Logic (FL) was introduced by Lotfi Zadeh

(1965). He stated that As complexity rises, precise statements lose

meaning and meaningful statements lose precision. It works based on

reasoning rather than fixed and exact, which means it is an approach

that deals with degrees of truth rather than the conventional crisp

logics like true/false, yes/no and high/low. Zadehs theory provides a

solution for problems that contains vague, noisy, or imprecise system

descriptions.

SPE 163636

The proposed framework is applied to history match the oil and

water rates in a black oil model using IMEX. In the first

scenario, FIS+DE is applied to show how engineering

knowledge can guide the sampling algorithm to converge to the

desired regions in the solution space. In the second scenario, we

only perform history matching with DE without FIS.

Teal South reservoir is

located in block 354 of

Eugene Island in the Gulf

of Mexico.

605 corner grid cells

5 geological layers

3.5 years production

History matching is an inverse problem in which the model

parameters will be tuned to fit production data. Ensemble of HM

models are required to reduce the uncertainty in the future forecast.

The engineer believes that there is a relationship between

permeabilities in layers 2 (P2) and 5 (P5). That is, if P2 is

medium, then P5 should be high. we use the FIS + DE to

honour this constraint during the history matching problem.

FIS+DE

In order to figure out the effect of incorporating the engineering knowledge on

the uncertainty in the future performance of the reservoir, we divide the

synthetic production data into two sections in which the first part is used during

history matching and the second part will be used in the forecast stage.

Water Rate

Oil Rate

FIS+DE

DE