Smart shale gas production performance analysis using machine learning applications
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TLDR
This review paper encompasses the literature published in the recent years and narrated the recent development made by researchers especially in the field of production performance estimation of shale gas by developing machine learning-based models.About:
This article is published in Petroleum Research.The article was published on 2021-06-29 and is currently open access. It has received 27 citations till now. The article focuses on the topics: Oil shale & Unconventional oil.read more
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Laboratory to field scale assessment for EOR applicability in tight oil reservoirs
TL;DR: In this article , a detailed discussion on laboratory-based experimental achievements at micro-scale including fundamental concepts under confinement environment, physics-based numerical studies, and recent actual field piloting experiences based on the U.S. unconventional plays is presented.
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AI/ML assisted shale gas production performance evaluation
TL;DR: In this paper, a systematic literature review is presented focused on the AI and ML applications for the shale gas production performance evaluation and their modeling, which can be utilized through supervised and unsupervised methods in addition to artificial neural networks (ANN), other ML approaches include random forest (RF), SVM, boosting technique, clustering methods, and artificial network-based architecture, etc.
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CO2 EOR Performance Evaluation in an Unconventional Reservoir through Mechanistic Constrained Proxy Modeling
Fahad I. Syed,Mariana de Oliveira Eberhard,Temoor Muther,Amirmasoud K. Dahaghi,Shahin Neghabhan +4 more
TL;DR: In this paper, a smart unconventional EOR performance evaluation tool is presented utilizing intelligent modeling strategies through proxy modeling approach with mechanistic constraints, which incorporates physics-based numerical simulation dataset.
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Geothermal 4.0: AI-enabled geothermal reservoir development- current status, potentials, limitations, and ways forward
Temoor Muther,Fahad I. Syed,Austin Lancaster,Farah D. Salsabila,Amirmasoud Kalantari-Dahaghi,Shahin Negahban +5 more
TL;DR: In this article , the authors highlight the integration of advanced technology on geothermal reservoir performance optimization, which plays a crucial role in the creation of optimum operating conditions at minimized costs, which leads to a more sustainable energy transition towards other energy sources besides petroleum.
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Analysis of environmental factors using AI and ML methods
Mohd Anul Haq,Ahsan Ahmed,Ilyas Khan,Jayadev Gyani,Abdullah Mohamed,El-Awady Attia,Pandian Mangan,D.Guru Pandi +7 more
TL;DR: In this paper , the authors applied a deep neural network model for time series forecasting of environmental variables, such as snow cover, temperature, and normalized difference vegetation index (NDVI).
References
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Corinna Cortes,Vladimir Vapnik +1 more
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TL;DR: Some results achieved by carrying out the classification tasks of equipment integrating the most common features of the ECG analysis: arrhythmia, myocardial ischemia, chronic alterations are presented.
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Optimal Parametric Design for Water-Alternating-Gas (WAG) Process in a CO2-Miscible Flooding Reservoir
TL;DR: In this article, a pragmatic method has been developed to efficiently design the production-injection parameters to optimize the water-alternating-gas (WAG) performance in a field-scale CO 2 -miscible flooding project.