Development of an evolutionary artificial neural network-based tool for selecting suitable enhanced oil recovery methods

Prudencio, Guillermo; Celis, César; Armacanqui, J. Samuel; Sinchitullo, Joseph

doi:10.1007/s40430-022-03403-3

Cited by 4 publications

(3 citation statements)

References 45 publications

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“…For polymer flooding projects, Keil et al built a proxy model based on the adaptive training process of neural networks to efficiently obtain suitable polymer flooding methods [20]. Prudencio et al developed a tool to determine the EOR method with the highest probability of successful implementation using an artificial evolutionary neural network-based approach based on seven reservoir fluid parameters such as porosity and permeability [21]. Koray et al used an artificial neural network to predict oil production and applied it to reservoir production optimization [22].…”

Section: Introductionmentioning

confidence: 99%

Investigation and Optimization of EOR Screening by Implementing Machine Learning Algorithms

Su,

Zhang,

Wang

et al. 2023

Applied Sciences

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Enhanced oil recovery (EOR) is a complex process which has high investment cost and involves multiple disciplines including reservoir engineering, chemical engineering, geological engineering, etc. Finding the most suitable EOR technique for the candidate reservoir is time consuming and critical for reservoir engineers. The objective of this research is to propose a new methodology to assist engineers to make fast and scientific decisions on the EOR selection process by implementing machine learning algorithms to worldwide EOR projects. First, worldwide EOR project information were collected from oil companies, the extensive literature, and reports. Then, exploratory data analysis methods were employed to reveal the distribution and relationships among different reservoir/fluid parameters. Random forest, artificial neural networks, naïve Bayes, support vector machines, and decision trees were applied to the dataset to establish classification models, and five-fold cross-validation was performed to fully apply the dataset and ensure the performance of the model. Utilizing random search, we optimized the model’s hyper parameters to achieve optimal classification results. The results show that the random forest classification model has the highest accuracy and the accuracy of the test set increased from 88.54% to 91.15% without or with the optimization process, achieving an accuracy improvement of 2.61%. The prediction accuracy in the three categories of thermal flooding, gas injection, and chemical flooding were 100%, 96.51%, and 88.46%, respectively. The results also show that the established RF classification model has good capability to make recommendations of the EOR technique for a new candidate oil reservoir.

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Section: Introductionmentioning

confidence: 99%

Investigation and Optimization of EOR Screening by Implementing Machine Learning Algorithms

Su,

Zhang,

Wang

et al. 2023

Applied Sciences

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“…As compared to any numerical/experimental approach, ANN offers the following advantages: (1) only correct pair of data (input-output) is required; that is, exact relation between the output and input is not needed, (2) a trained ANN model can give the result in much less time, that is, time taken to solve any similar problem is drastically less as compared to other means, (3) both the experimental and/or numerical data can be used to develop an ANN model, and (4) inverse problem can be solved easily. [7][8][9][10][11] Various advantages of the ANN approach make it a useful tool to solve scientific problems in different fields, like, manufacturing, 12,13 oil exploration, 14 biofuels, 15,16 solar, 17 lubrication, 18 automobile, 19 power plants, 20 and so on. Various thermal and fluid problems solved by employing ANN and various optimization tools are listed in Table 1.…”

Section: Introductionmentioning

confidence: 99%

“…Various advantages of the ANN approach make it a useful tool to solve scientific problems in different fields, like, manufacturing, 12,13 oil exploration, 14 biofuels, 15,16 solar, 17 lubrication, 18 automobile, 19 power plants, 20 and so on. Various thermal and fluid problems solved by employing ANN and various optimization tools are listed in Table 1.…”

Section: Introductionmentioning

confidence: 99%

Retrieval of parameters in micromixer with obstacle by cascade‐forward‐type artificial neural network: Comparison of four training algorithms under noisy data

et al. 2023

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Two parameters are retrieved in a passive Y‐type micromixer with circular obstacle by cascade‐forward‐type artificial neural network (CFANN). The governing equations are solved by the finite volume method, under specific boundary conditions. The numerical model is then used to compute velocity profile and mixing efficiency, for different values of the Reynolds number. Thus, the velocity profiles along with Reynolds number (Re) and mixing efficiency (η) constitute the input–output pair of data. These data are used to train CFANN, and the network is monitored through different means, like, histograms, performance curves, and so forth. For inverse analysis, the trained CFANN model is fed with a new velocity profile as input, and corresponding values of Reynolds number and mixing efficiency are obtained as output. In an attempt to construct the optimum CFANN model, various combinations were explored, like, (1) different numbers of neurons in the hidden layer, (2) different noise levels in input data, and (3) different algorithms in the training stage. Finally, the CFANN with 10 hidden layer neurons with Levenberg–Marquardt (LM) algorithm was found to give retrieved values with up to 0.96% absolute error for all levels of noise in the input data. Also, the CFANN model with the LM algorithm has a very high value of regression coefficient of greater than 0.998, under all the noise values. Scaled conjugate gradient algorithm gives good results for the no‐noise case, but fails poorly with the rise of noise. Other algorithms, like, Bayesian regularization and resilient backpropagation, perform poorly even in the no‐noise case. The present approach is highly simple, accurate, and time efficient for applying inverse analysis in micromixers.

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Artificial neural network framework for the selection of deep eutectic solvents promoted enhanced oil recovery by interfacial tension reduction mechanism

Prasad,

Raj,

Kundu

2024

Energy

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Development of an evolutionary artificial neural network-based tool for selecting suitable enhanced oil recovery methods

Cited by 4 publications

References 45 publications

Investigation and Optimization of EOR Screening by Implementing Machine Learning Algorithms

Investigation and Optimization of EOR Screening by Implementing Machine Learning Algorithms

Retrieval of parameters in micromixer with obstacle by cascade‐forward‐type artificial neural network: Comparison of four training algorithms under noisy data

Artificial neural network framework for the selection of deep eutectic solvents promoted enhanced oil recovery by interfacial tension reduction mechanism

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