Application of ML & AI to model petrophysical and geomechanical properties of shale reservoirs – A systematic literature review
TLDR
In this paper , the authors provide a comprehensive literature review in the area of AI and ML applications to model Petrophysical and Geomechanical properties using different approaches and algorithms, and a systematic publication counts in each field of subject study per year in different literature databases are presented that reflect the trending interest in this subject.About:
This article is published in Petroleum.The article was published on 2022-06-01 and is currently open access. It has received 19 citations till now. The article focuses on the topics: Petrophysics & Oil shale.read more
Citations
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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).
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Reproduction of reservoir pressure by machine learning methods and study of its influence on the cracks formation process in hydraulic fracturing
TL;DR: In this paper , the authors investigated the influence of the formation pressure on the patterns of fracturing and concluded that the influence should be taken into account when planning hydraulic fracturing in the considered conditions, which is confirmed by high convergence with the actual (historical) reservoir pressures obtained during hydrodynamic studies of wells.
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A Review of Proxy Modeling Highlighting Applications for Reservoir Engineering
TL;DR: The introduced guideline in this review provides a more comprehensive guideline on comparing and developing a proxy model compared to the existing literature and highlights the superiority of SPM over traditional statistical/AI-based proxy models.
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Application of machine learning algorithms in classification the flow units of the Kazhdumi reservoir in one of the oil fields in southwest of Iran
Ali Ranjbar,Reza Keshavarz +1 more
TL;DR: In this article , the hydraulic flow units (HFUs) in the reservoir rock and examining the distribution of porosity and permeability variables, it is possible to identify areas with suitable reservoir quality.
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Perspectives on Microfluidics for the Study of Asphaltenes in Upstream Hydrocarbon Production: A Minireview
TL;DR: In this article , the authors highlight the crucial aspects of microfluidics that have been used to understand physicochemical behavior and dynamics of asphaltene deposition and highlight the importance of micro-systems for advancing knowledge in hydrocarbon production and processing.
References
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An Insight into Extreme Learning Machines: Random Neurons, Random Features and Kernels
TL;DR: An insight into ELMs in three aspects, viz: random neurons, random features and kernels is provided and it is shown that in theory ELMs (with the same kernels) tend to outperform support vector machine and its variants in both regression and classification applications with much easier implementation.
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Cornucopia or curse? Reviewing the costs and benefits of shale gas hydraulic fracturing (fracking)
TL;DR: The authors assesses the overall technical, economic, environmental, and social costs and benefits of hydraulic fracturing (fracking) of natural gas and concludes that done poorly production can contribute to accidents and leakage, contribute to environmental degradation, induce earthquakes, and, when externalities are accounted for, produce more net economic losses than profits.
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Predicting the need for vehicle compressor repairs using maintenance records and logged vehicle data
TL;DR: The machine learning based features outperform the human expert features, which supports the idea to use data mining to improve maintenance operations in this domain.
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Applications of machine learning for facies and fracture prediction using Bayesian Network Theory and Random Forest: Case studies from the Appalachian basin, USA
TL;DR: Application of Bayesian Network theory and Random Forest shows that both facies and fractures can be predicted with high accuracy using limited common well logs.
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Machine learning technique for the prediction of shear wave velocity using petrophysical logs
TL;DR: The method employed in this study could be used as an efficient means to accurately estimate Vs and provided more reliable and accurate results compared to those of empirical and regression models, and also LSSVM-PSO and L SSVM-GA algorithms.
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