Supervised deep learning-based paradigm to screen the enhanced oil recovery scenarios

Pandey, Rakesh; Gandomkar, Asghar; Vaferi, Behzad; Kumar, Anil; Torabi, Farshid

doi:10.1038/s41598-023-32187-2

Cited by 21 publications

(3 citation statements)

References 46 publications

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“…Evaluation parameters: Conduct extensive market research: Market research is a thorough process of directly engaging with customers to determine the reliability of a new service or product. It involves finding out about a company's target market [26]. gathering feedback from consumers and other stakeholders, and getting opinions from professionals in the field.…”

Section: Methodsmentioning

confidence: 99%

Developing Business Services Using IBM SPSS Statistics

Ramachandran²,

Vidhya³

et al. 2023

jbab

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Developing Business Services. This study examines business development services for entrepreneurs, which should be offered in various phases. Non-financial services and products are defined as business services. “Business services” is a common term used to describe supportive but firm objects and non-productive work. Information technology (IT) is an important supporting service in many businesses, such as shipping and finance. A good business service aligns with the company’s IT assets, employees, and customers’ requirements, supports business goals, and facilitates company profitability. The IT sector provides business documenting the value of infrastructure processes, IT service audit, IT service inventory creation or renewal, and/or delivery to improve communication, including an employee self-service portal. More information about this source text is required for additional translation; please send feedback using the side panels. Business services are a support business but produce a solid product. Information technology (IT) is an important business support service in shipping, procurement, and various businesses like finance. Business development is about promoting development in your company to increase revenue strategies and opportunities through the process of implementation. Pursuing opportunities, identifying new opportunities, and converting more customers, including commercial services, are activities that help businesses but do not provide tangible substance. For example, information technology in shipping, procurement, and various businesses like finance supports these services. To help your business grow, you need to pursue opportunities and develop strategies to increase revenue. This involves conducting extensive market research, raising visibility and awareness, promoting thought leadership, conducting outreach, generating quality leads, providing exemplary customer service, and developing sales content from success stories. SPSS statistics is a data management, advanced analytics, multivariate analytics, business intelligence, and criminal investigation developed by IBM for a statistical software package. A long time, spa Inc. was created by IBM, which purchased it in 2009. The brand name for the most recent versions is IBM SPSS statistics. The Cronbach’s alpha reliability result showed that the overall Cronbach’s alpha value for the model is .490, indicating 50% reliability. From the literature review, the above cronbach’s alpha value of 46% can be considered to analyze the model.

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Section: Methodsmentioning

confidence: 99%

Developing Business Services Using IBM SPSS Statistics

Ramachandran²,

Vidhya³

et al. 2023

jbab

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“…Consequently, AEORSs have been extensively used in the last decade to establish a rigorous relationship between the influential parameters and suitable EOR techniques for different types of reservoir models. Machine learning (ML) methods, such as multi-layer perceptron 14 , fuzzy inference 15 , Support Vector Machines (SVMs) 10 , etc., are some examples of AEORS techniques that are widely used for EOR screening. Khazali et al 16 employed a fuzzy decision tree (DT) trained on 548 successful EOR experiences related to ten different EOR techniques.…”

Section: Literature Reviewmentioning

confidence: 99%

Application of power-law committee machine to combine five machine learning algorithms for enhanced oil recovery screening

Yousefzadeh,

Kazemi,

Al-Maamari

2024

Sci Rep

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One of the main challenges in screening of enhanced oil recovery (EOR) techniques is the class imbalance problem, where the number of different EOR techniques is not equal. This problem hinders the generalization of the data-driven methods used to predict suitable EOR techniques for candidate reservoirs. The main purpose of this paper is to propose a novel approach to overcome the above challenge by taking advantage of the Power-Law Committee Machine (PLCM) technique optimized by Particle Swam Optimization (PSO) to combine the output of five cutting-edge machine learning methods with different types of learning algorithms. The PLCM method has not been used in previous studies for EOR screening. The machine learning models include the Artificial Neural Network (ANN), CatBoost, Random Forest (RF), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). The CatBoost is used for the first time in this work for screening of EOR methods. The role of the PSO is to find the optimal values for the coefficients and exponents of the power-law model. In this study, a bigger dataset than those in previous studies, including 2563 successful worldwide EOR experiences, was gathered. A bigger dataset improves the generalization of the data-driven methods and prevents overfitting. The hyperparameters of the individual machine-learning models were tuned using the fivefold cross-validation technique. The results showed that all the individual methods could predict the suitable EOR method for unseen cases with an average score of 0.868. Among the machine learning models, the KNN and SVM had the highest scores with a value of 0.894 and 0.892, respectively. Nonetheless, after combining the output of the models using the PLCM method, the score of the predictions improved to 0.963, which was a substantial increase. Finally, a feature importance analysis was conducted to find out the most influential parameters on the output. The novelty of this work is having shown the ability of the PLCM technique to construct an accurate model to overcome the class-imbalance issue in EOR screening by utilizing different types of data-driven models. According to feature importance analysis, oil gravity and formation porosity were recognized as the most influential parameters on EOR screening.

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“…Indeed, this relationship is explained by the significant increase in the emissions of greenhouse gases (GHGs), which are resulted mainly as waste gases from utilization of fuel energy. , Carbon dioxide (CO 2 ), which is recognized as the most famous GHG, is considered among the biggest contributors to global warming as its level in the atmosphere is increasingly rising due to the use of fossil fuels. , Owing to this fact, various fields of science and technology have witnessed the deployment of new strategies that aims at develop new tools and processes which can help to reduce CO 2 levels in the atmosphere. In the context of the explored pathways, carbon capture and storage (CCS) in geological formations is currently recognized as the most attractive option to mitigate the challenge of reducing the emissions of CO 2 . − CCS can be implemented in underground porous mediums, such as depleted hydrocarbon reservoirs, coal seams, and saline aquifers. , Besides, enhanced oil recovery (EOR) based on the injection of CO 2 (CO 2 – EOR) in mature oil reservoirs during the medium to late production stages has also gained fresh prominence and is considered one of the utmost underground utilizations of CO 2 as it increases the oil recovery factor (ORF). − …”

Section: Introductionmentioning

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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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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Supervised deep learning-based paradigm to screen the enhanced oil recovery scenarios

Cited by 21 publications

References 46 publications

Developing Business Services Using IBM SPSS Statistics

Developing Business Services Using IBM SPSS Statistics

Application of power-law committee machine to combine five machine learning algorithms for enhanced oil recovery screening

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

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