Application of Fuzzy Expert Systems for EOR Project Risk Analysis

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

doi:10.2523/30741-ms

Cited by 4 publications

(4 citation statements)

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“…It is developed to deal with the concept of partial or incomplete truth whereby the values fall between the whole truth (one) and absolute false (zeroes). 33 The application of the fuzzy logic in the petroleum industry can be seen in several cases, namely, in controlling the pressure of fracturing fluid in its characterization facility, 34 risk analysis for enhanced oil recovery, 35 and the petroleum separation process. 36 Zamani et al 37 used the ANFIS method without using trend analysis to predict the R s at the bubble point pressure (SCF/STB) based on the bubble point pressure (P b ), γ g , API, and T using 157 datasets from Iranian fields.…”

Section: Introductionmentioning

confidence: 99%

Determination of the Gas–Oil Ratio below the Bubble Point Pressure Using the Adaptive Neuro-Fuzzy Inference System (ANFIS)

et al. 2022

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Determining the solution gas–oil ratio ( R s ) below the bubble point is a vital requirement that aids in multiple production engineering and reservoir analysis issues. Currently, there are some models available for the determination of the solution gas–oil ratio under the bubble point. However, they still may prove unreliable due to the applied assumptions and their specification to operate only under a particular range of data. In this study, the neuro-fuzzy, i.e., the adaptive neuro-fuzzy inference system (ANFIS) approach, is utilized to develop an accurate and dependable model for determining the R s below the bubble point pressure. A total of 376 pressure–volume–temperature datasets from Sudanese oil fields were used to establish the proposed ANFIS model. The trend analysis was applied to affirm the proper relationships between the inputs and outputs. Furthermore, using different statistical error analyses, the developed model was benchmarked against widely used empirical methods to evaluate the proposed method’s performance in predicting the R s at pressures below the bubble point. The proposed ANFIS model performs with an average absolute percent relative error of 10.60% and a correlation coefficient of 99.04%, surpassing the previously studied correlations.

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Section: Introductionmentioning

confidence: 99%

Determination of the Gas–Oil Ratio below the Bubble Point Pressure Using the Adaptive Neuro-Fuzzy Inference System (ANFIS)

et al. 2022

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“…This is particularly the case since these parameters may not necessarily be directly dependent on each other. Several methods have been developed and published for screening oil reservoirs such as data analysis by using tables and graphs [5][6][7][8] and artificial intelligence (AI) [3,[9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

“…Fuzzy-neural is an important approach in the area of reservoir characterisation in which knowledge of reservoir performance forecast from well logs can be derived [9]. Chung et al [10] developed a fuzzy expert system for EOR risk analysis incorporating preliminary screening EOR methods, the field performance estimation and economic analysis. The system reduces the requirements of massive laboratory experiments and field data input.…”

Section: Introductionmentioning

confidence: 99%

Data Analysis and Neuro-Fuzzy Technique for EOR Screening: Application in Angolan Oilfields

Ramos

Akanji

2017

Energies

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In this work, a neuro-fuzzy (NF) simulation study was conducted in order to screen candidate reservoirs for enhanced oil recovery (EOR) projects in Angolan oilfields. First, a knowledge pattern is extracted by combining both the searching potential of fuzzy-logic (FL) and the learning capability of neural network (NN) to make a priori decisions. The extracted knowledge pattern is validated against rock and fluid data trained from successful EOR projects around the world. Then, data from Block K offshore Angolan oilfields are then mined and analysed using box-plot technique for the investigation of the degree of suitability for EOR projects. The trained and validated model is then tested on the Angolan field data (Block K) where EOR application is yet to be fully established. The results from the NF simulation technique applied in this investigation show that polymer, hydrocarbon gas, and combustion are the suitable EOR techniques.

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“…[39][40][41][42][43][44] In the recent paper by Gharbi 39 , an expert system was developed that can perform three consultations: (1) select an appropriate EOR process on the basis of the reservoir characteristics, (2) prepare appropriate input data sets to design the selected EOR process using the existing numerical simulators, and (3) make optimization studies on several key parameters (selected by the user) in order to optimize the oil recovery from the selected EOR process. The optimization part of the expert system in step (3) was done using an iterative approach based on the oil recovery curve as the decision-making variable.…”

Section: Introductionmentioning

confidence: 99%

A Knowledge-Based System for Optimal Economic Design of Improved Recovery Processes

Gharbi¹

2001

Proceedings of SPE Asia Pacific Oil and Gas Conference and Exhibition

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The main challenge of Enhanced Oil Recovery (EOR) processes at the prevailing low oil prices is to reduce the cost. An optimization methodology, combined with an economic model, is implemented into an expert system for optimizing the net present value of full field development with an EOR process. The approach is automated and combines an economic package and existing numerical reservoir simulators to optimize the design of a selected EOR process using sensitivity analysis. The EOR expert system includes three stages of consultations: (1) select an appropriate EOR process on the basis of the reservoir characteristics, (2) prepare appropriate input data sets to design the selected EOR process using existing numerical simulators, and (3) apply the discounted-cash-flow methods to the optimization of the selected EOR process to find out under what conditions at current oil prices this EOR process might be profitable. The project profitability measures were used as the decision-making variables in an iterative approach to optimize the design of the EOR process. The economic analysis is based on the estimated recovery, residual oil in-place, oil price, and operating costs. Two case studies are presented for two reservoirs that have already been produced to their economic limits and are potential candidates for surfactant/polymer flooding, and carbon-dioxide flooding, respectively, or otherwise subject to abandonment. The effect of several design parameters on the project profitability of these EOR processes was investigated.

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Application of Fuzzy Expert Systems for EOR Project Risk Analysis

Cited by 4 publications

References 0 publications

Determination of the Gas–Oil Ratio below the Bubble Point Pressure Using the Adaptive Neuro-Fuzzy Inference System (ANFIS)

Determination of the Gas–Oil Ratio below the Bubble Point Pressure Using the Adaptive Neuro-Fuzzy Inference System (ANFIS)

Data Analysis and Neuro-Fuzzy Technique for EOR Screening: Application in Angolan Oilfields

A Knowledge-Based System for Optimal Economic Design of Improved Recovery Processes

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