Application of a Hybrid System of Genetic Algorithm &amp; Fuzzy Logic as Optimization Techniques for Improving Oil Recovery in a Sandstone Reservoirs in Iraq

Al-Mudhafer, Wathiq Jassim Mohammed; Alabbas, Maytham

doi:10.2118/149982-ms

Cited by 8 publications

(3 citation statements)

References 21 publications

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“…The fuzzy logic system comprises three phases: fuzzifier, fuzzy inference system, and defuzzifier. 50 In particular, the mechanism of the fuzzy logic system can be described as follows: in the fuzzifier stage, the raw inputs to the system form fuzzy inputs. Later, these fuzzy inputs are to be populated into the inference environment or system in which the real calculations are accomplished.…”

Section: Fuzzy Logic-genetic Algorithmmentioning

confidence: 99%

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Rapid evaluation and optimization of carbon dioxide‐enhanced oil recovery using reduced‐physics proxy models

Al‐Mudhafar

Rao

Srinivasan

et al. 2022

Energy Science & Engineering

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The carbon dioxide (CO2) injection in oil reservoirs is a promising enhanced oil recovery method to reduce carbon emissions into the atmosphere. The CO2 injection simulation and optimization require a large computation time, especially in real large‐scale oil reservoirs. This paper integrates experimental design and machine learning to construct a reduced‐physics surrogate model alternative to the complex reservoir simulator. This surrogate model was used to evaluate and optimize the CO2‐assisted gravity drainage (CO2‐GAGD) process, applied to a clastic reservoir in the Rumaila oil field. In the GAGD process optimization, five operational decision parameters controlling the production and injection activities were manipulated to attain optimal future oil production. Hundreds of simulation runs were created by Latin Hypercube Sampling to build a proxy‐based optimization. The optimal scenario increased the cumulative oil production by 416 MMSTB at the end of 10‐year prediction period. Finally, four machine learning (ML) algorithms were built as proxy surrogates alternative to the complex compositional reservoir simulations: Quadratic Equation (QM), FUzzy logic‐GEnetic algorithm (FUzzy‐GEnetic), Multivariate Additive Regression Splines (MARS), and Generalized Boosted Modeling (GBM). To show how robust using the ML approaches for the fast CO2‐GAGD process optimization, the random subsampling cross‐validation was adopted to conclude the optimum proxy metamodel that provides the lowest mismatch between the proxy‐ and simulator‐based cumulative oil production. The GBM algorithm achieved the highest adjusted‐R2 (0.9973) and lowest root mean square prediction error MathClass-open( 3.704 × 1 0 6 MathClass-close) $(3.704\times 1{0}^{6})$ to produce the most accurate metamodel. The resulting accurate GBM proxy metamodel can be used for fast optimization of the CO2‐GAGD process. Specifically, GBM‐metamodel should lead to achieving higher optimal reservoir flow responses, especially with running a large number of simulation runs. Consequently, this proposed trained GBM‐ML proxy metamodel could be used for gas injection optimization and uncertainty assessment with far‐less computational effort than with the conventional approaches, which use a complex flow simulator.

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Section: Fuzzy Logic-genetic Algorithmmentioning

confidence: 99%

“…Fuzzy Logic is a convenient way to build a fuzzy input and output data model. The fuzzy logic system comprises three phases: fuzzifier, fuzzy inference system, and defuzzifier 50 . In particular, the mechanism of the fuzzy logic system can be described as follows: in the fuzzifier stage, the raw inputs to the system form fuzzy inputs.…”

Section: Proxy Modelingmentioning

confidence: 99%

Rapid evaluation and optimization of carbon dioxide‐enhanced oil recovery using reduced‐physics proxy models

Al‐Mudhafar

Rao

Srinivasan

et al. 2022

Energy Science & Engineering

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“…This research focuses on the use of predictive capabilities of Particle Swarm Optimization with capabilities learning of Neural Network (PSONN) combination[18]. Jing-Ru Zhang introduced a hybrid algorithm combining particle swarm optimization (PSO) algorithm with back-propagation (NN) algorithm to train the weights of feedforward neural network (FNN)[19]. Al-Mudhafer and Abbas developed hybrid System of Genetic Algorithm and Fuzzy Logic as Optimization Techniques for determining the optimal future reservoir performance regarding to infill drilling[20].…”

mentioning

confidence: 99%

Hybrid Artificial Intelligent Approach for Determination of Water Saturation using Archie’s Formula in Carbonate Reservoirs

GM¹,

AA²

2015

J Pet Environ Biotechnol

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The challenge to determine the accurate water saturation is still faced Petroleum Engineering. The difficulty of this problem will be increase if we deal with carbonate rocks. There are some available techniques used to determine the water saturation. However, the accuracy of those techniques has become unable to find the best results. Several available techniques have been used to estimate water saturation such as conventional, CAPE (a, m, n), CAPE (1, m, n) and 3D methods. Currently, the achievements of Artificial Intelligent (AI) techniques alone open the door to use the hybrid system such as (PSONN). In this model, the Particle Swarm Optimization (PSO) technique is employed to search for optimal connection weighs and thresholds for the neural networks (NN), then the back-propagation learning rule and training algorithm is used to adjust the final weights. A total of about 383 data points obtained from the laboratory measurements of electrical properties from carbonate core plugs of Middle East reservoir were used for the implementation of the proposed technique. Statistical analysis and comparative study show that the performance of PSONN model is the best one with lower root mean square error (0.092) and higher accuracy of correlation coefficient (0.95) than those obtained with previous methods. Results showed that the new hybrid PSONN model outperforms some available methods and overcome the weakness if we use AI alone. From error analysis, it is found that CAPE and 3-D and PSONN methods ensure minimum error of water saturation values.

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References

2022

Artificial Intelligence and Data Analytics for Energy Exploration and Production

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Application of a Hybrid System of Genetic Algorithm & Fuzzy Logic as Optimization Techniques for Improving Oil Recovery in a Sandstone Reservoirs in Iraq

Cited by 8 publications

References 21 publications

Rapid evaluation and optimization of carbon dioxide‐enhanced oil recovery using reduced‐physics proxy models

Rapid evaluation and optimization of carbon dioxide‐enhanced oil recovery using reduced‐physics proxy models

Hybrid Artificial Intelligent Approach for Determination of Water Saturation using Archie’s Formula in Carbonate Reservoirs

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