Application of Fuzzy Expert Systems for EOR Project Risk Analysis

Chung, Ting-Horng; Carroll, H.B.; Lindsey, Rhonda P.

doi:10.2118/30741-ms

Cited by 19 publications

(6 citation statements)

References 10 publications

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“…These applications include petrophysics (31,32) reservoir characterization (33) , en-Fig. 2 Fuzzy Sets Representing the Price of Oil (41) hanced recovery (34,35) , infill drilling (36) , decision making analysis (37) , and well stimulation ( 38,39) . In this section we review an application that incorporates fuzzy logic in a hybrid manner in concert with neural networks and genetic algorithms.…”

Section: Application Of Fl In Petroleum Industrymentioning

confidence: 99%

Artificial Intelligence: Excellent Key for Developing E&P Oil Industry in Iraq

Al-Mudhafer¹,

Shaheed²

2021

Journal of Petroleum Research and Studies

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A Multidisciplinary study for increasing oil recovery has been made in the present paper. This work has been adopted in the Upper Sandstone member/Zubair formation in South Rumaila Oil Field. The work was achieved by using optimization techniques for determining the optimal future reservoir performance regarding to infill drilling. Two different methods of Genetic Algorithm used to optimize the number and locations of infill wells. The first method is simple adaptive genetic algorithm and the second one is the breeder adaptive. The main parameters depended in this study is the cumulative oil production obtained from the output of reservoir simulation software. These two methods of GA depend on using Net Present Value (NPV) as economic analysis as objective function. The optimal number of infill wells is three wells which have maximum cumulative oil production and maximum value of NPV. The same results from two GA methods have been obtained. The locations of these optimal infill wells located in the crest of the oil field.

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Section: Application Of Fl In Petroleum Industrymentioning

confidence: 99%

Artificial Intelligence: Excellent Key for Developing E&P Oil Industry in Iraq

Al-Mudhafer¹,

Shaheed²

2021

Journal of Petroleum Research and Studies

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“…Subsequently, several works have been published to improve the quality and accuracy of the models. These models are based on fuzzy-logic (FL) and expert system approach [10,11] artificial neural network (ANN) [12] least square support vector machine (LSSVM), and very recently, the combination of both fuzzy-logic (FL) and neurofuzzy (NF) [13,14]. These works and others recently published in literature on screening techniques are summarised in [14].…”

Section: Introductionmentioning

confidence: 99%

Screening selection of enhanced oil recovery methods based on analytics of worldwide oilfield data with reference to offshore oil fields in Vietnam

Hien¹,

Long²,

Ngoc³

2021

PVJ

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Selecting a proper enhanced oil recovery (EOR) method for a prospective reservoir is a key factor for successful application of EOR techniques. Reservoir engineers usually refer to screening guidelines to identify potential EOR processes for a given reservoir. However, these guidelines are often too general. In this study, we develop an advanced EOR screening technique based on the statistical analyses with boxplot in combination with some initial deep learning analyses to select the most suitable EOR method for a given mature oil field. At first, a database and the screening guidelines were established by compiling the information of 1,098 EOR projects from various publications in different languages, including Oil and Gas Journal (OGJ) biannual EOR surveys, SPE publications, DOE reports, and Chinese publications, etc. Boxplots were used to detect the special cases for each reservoir/fluid property and to present the graphical screening results. A case study was used to demonstrate that with a simple input of reservoir/fluid information, the proposed procedure could effectively give recommendations for EOR method selection. With the inputs (reservoir and fluid properties) from Vietnam offshore oil fields, the EOR methods recommended by this study are mostly chemical, including polymer and surfactant injection.

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“…In [11] expert system has been suggested with possibility of changing and editing the parameters of EOR methods. Fuzzy expert system for EOR project risk analysis was presented in [12]. Ontology driven decision support system was presented in [13].…”

Section: Introductionmentioning

confidence: 99%

Knowledge Representation Model and Decision Support System for Enhanced Oil Recovery Methods

2014

International Conference on Intelligent Systems, Data Mining and Information Technology (ICIDIT’2014) April 21-22, 2014 Bangkok

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In this paper we develop decision support system for enhanced oil recovery methods selection. Fuzzy production rules were suggested to describe expert's knowledge. We use various types of constraints in fuzzy production rules, and expert estimates an importance rate of these constraints. We suggest fuzzy inference method for decision support system with the proposed fuzzy production rules. Fuzzy inference method is capable to work in conditions of gaps in the source data. We used the decision support system to select enhanced oil recovery methods on some oil fields.

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Application of Fuzzy Expert Systems for EOR Project Risk Analysis

Cited by 19 publications

References 10 publications

Artificial Intelligence: Excellent Key for Developing E&P Oil Industry in Iraq

Artificial Intelligence: Excellent Key for Developing E&P Oil Industry in Iraq

Screening selection of enhanced oil recovery methods based on analytics of worldwide oilfield data with reference to offshore oil fields in Vietnam

Knowledge Representation Model and Decision Support System for Enhanced Oil Recovery Methods

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