Application of adaptive neuro-fuzzy inference system for prediction of minimum miscibility pressure

Shahrabi, Mohammadjavad Ameri; Kivi, Iman Rahimzadeh; Akbari, Mohammadreza; Safiabadi, Anoush

doi:10.1504/ijogct.2014.057796

Cited by 7 publications

(3 citation statements)

References 22 publications

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“…Both of the suggested intelligent-based paradigms exhibited interesting prediction performance. Shahrabi et al 191 and Ahmadi and Ebadi 192 220 illustrated the application of RF, ANN, and SVR for predicting the MMP of pure and impure CO 2 − oil systems. The considered database in their study covered 170 data points.…”

Section: Energy and Fuels Pubsacsorg/ef Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Utilizing Artificial Intelligence Techniques for Modeling Minimum Miscibility Pressure in Carbon Capture and Utilization Processes: A Comprehensive Review and Applications

Amar,

Djema,

Ourabah

et al. 2024

Energy Fuels

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The carbon dioxide (CO 2 ) based enhanced oil recovery methods (EORs) are considered among the promising techniques for increasing the recovery factor from mature oil reservoirs and reducing the amount of CO 2 emission in the atmosphere. Determining the minimum miscibility pressure (MMP) of CO 2 − oil systems is a crucial step for successfully implementing CO 2 − EOR processes. Therefore, various approaches have been proposed for determining this key parameter. However, the laboratory tests are expensive and timeconsuming, while most available correlations present moderate accuracy. To address these shortcomings, various studies have applied artificial intelligence (AI) techniques to model the MMP of the CO 2 − oil systems. In this study, we reviewed the published works for predicting the MMP of the CO 2 − oil systems using AI-based models. Our analyses revealed the robustness of these techniques in modeling MMP. In this context, it was noticed that more than 70 AI-based paradigms have been utilized for estimating the MMP of CO 2 − oil systems. Among the applications, the hybrid schemes combining machine learning and nature-inspired algorithms (ML-NIA) take the top spot, accounting for 27% of applications. Additionally, the investigation demonstrated that the artificial neural network (ANN) is the most utilized ML method in the modeling phase, while the genetic algorithm (GA) is the most widely applied NIA for improving ML performance. In the second part of this study, we suggest an updated correlation based on gene expression programming (GEP) for the accurate prediction of MMP of CO 2 − oil systems. Our proposed explicit correlation yielded excellent predictive performance, achieving overall root-mean-square error and determination coefficient (R 2 ) values of 0.9253 and 09713, respectively. These promising statistical metrics enabled the newly updated GEP-based correlation to outperform the preexisting models. Besides, the physical validity and interpretability of the proposed correlation were proven using the trend analysis and the Shape Dependence Analysis plot, respectively. Lastly, the findings of this study provide a dual benefit, the review and the illustration of the published studies on applying AI techniques for modeling the MMP of the CO 2 − oil systems; and second, the newly implemented GEP-based correlation can significantly enhance the effective prediction of the MMP in CO 2 -oil systems, thus, facilitating the simulation of various CO 2 − based EOR processes.

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Section: Energy and Fuels Pubsacsorg/ef Reviewmentioning

confidence: 99%

“…Both of the suggested intelligent-based paradigms exhibited interesting prediction performance. Shahrabi et al . and Ahmadi and Ebadi presented two ANFIS models with reliable prediction performance.…”

Section: Progress On Modeling the Mmp Of The Co2 – Oil Systems Using ...mentioning

confidence: 99%

Utilizing Artificial Intelligence Techniques for Modeling Minimum Miscibility Pressure in Carbon Capture and Utilization Processes: A Comprehensive Review and Applications

Amar,

Djema,

Ourabah

et al. 2024

Energy Fuels

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“…Many engineering problems are resolved through the application of adaptive neuro-fuzzy inference system (ANFIS). Accurate prediction models are developed using complicated and nonlinear algorithms of ANFIS which are structured between input and output parameters [31][32][33] .…”

Section: Modelling Approaches: Ann Anfis and Fn Artificial Neural Net...mentioning

confidence: 99%

Machine learning accelerated approach to infer nuclear magnetic resonance porosity for a middle eastern carbonate reservoir

Mustafa

Tariq²,

Mahmoud³

et al. 2023

Sci Rep

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Carbonate rocks present a complicated pore system owing to the existence of intra-particle and interparticle porosities. Therefore, characterization of carbonate rocks using petrophysical data is a challenging task. Conventional neutron, sonic, and neutron-density porosities are proven to be less accurate as compared to the NMR porosity. This study aims to predict the NMR porosity by implementing three different machine learning (ML) algorithms using conventional well logs including neutron-porosity, sonic, resistivity, gamma ray, and photoelectric factor. Data, comprising 3500 data points, was acquired from a vast carbonate petroleum reservoir in the Middle East. The input parameters were selected based on their relative importance with respect to output parameter. Three ML techniques such as adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN), and functional network (FN) were implemented for the development of prediction models. The model’s accuracy was evaluated by correlation coefficient (R), root mean square error (RMSE), and average absolute percentage error (AAPE). The results demonstrated that all three prediction models are reliable and consistent exhibiting low errors and high ‘R’ values for both training and testing prediction when related to actual dataset. However, the performance of ANN model was better as compared to other two studied ML techniques based on minimum AAPE and RMSE errors (5.12 and 0.39) and highest R (0.95) for testing and validation outcome. The AAPE and RMSE for the testing and validation results were found to be 5.38 and 0.41 for ANFIS and 6.06 and 0.48 for FN model, respectively. The ANFIS and FN models exhibited ‘R’ 0.937 and 0.942, for testing and validation dataset, respectively. Based on testing and validation results, ANFIS and FN models have been ranked second and third after ANN. Further, optimized ANN and FN models were used to extract explicit correlations to compute the NMR porosity. Hence, this study reveals the successful applications of ML techniques for the accurate prediction of NMR porosity.

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Application of intelligent models in reservoir and production engineering

Hemmati‐Sarapardeh¹,

Larestani²,

Amar³

et al. 2020

Applications of Artificial Intelligence Techniques in the Petroleum Industry

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Application of adaptive neuro-fuzzy inference system for prediction of minimum miscibility pressure

Cited by 7 publications

References 22 publications

Utilizing Artificial Intelligence Techniques for Modeling Minimum Miscibility Pressure in Carbon Capture and Utilization Processes: A Comprehensive Review and Applications

Utilizing Artificial Intelligence Techniques for Modeling Minimum Miscibility Pressure in Carbon Capture and Utilization Processes: A Comprehensive Review and Applications

Machine learning accelerated approach to infer nuclear magnetic resonance porosity for a middle eastern carbonate reservoir

Application of intelligent models in reservoir and production engineering

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