Artificial Intelligence Methods in Hydraulic System Design

Filo, Grzegorz

doi:10.3390/en16083320

Cited by 6 publications

(1 citation statement)

References 82 publications

(79 reference statements)

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“…Ryan [25] screened more than 100 predictive variables for more than 3900 acidizing operations in more than 500 wells in the Wilmington Oilfield in southern California, and logistic regression (LR), support vector machine (SVM), and random forest (RF) in the open-source R-4.3.2 statistical learning software were selected for training and utilized for decision-making in acidizing procedures, achieving an impressive predictive accuracy of 77%. Additionally, the use of artificial neural networks (ANNs) played a crucial role in selecting optimal hydraulic fluid systems, addressing a significant challenge in the design of acidizing strategies [27].…”

Section: Design Optimizationmentioning

confidence: 99%

Machine Learning in Reservoir Engineering: A Review

Zhou,

Liu,

Liu

et al. 2024

Processes

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With the rapid progress of big data and artificial intelligence, machine learning technologies such as learning and adaptive control have emerged as a research focus in petroleum engineering. They have various applications in oilfield development, such as parameter prediction, optimization scheme deployment, and performance evaluation. This paper provides a comprehensive review of these applications in three key scenarios of petroleum engineering, namely hydraulic fracturing and acidizing, chemical flooding and gas flooding, and water injection. This article first introduces the steps and methods of machine learning processing in these scenarios, then discusses the advantages, disadvantages, existing challenges, and future prospects of these machine learning methods. Furthermore, this article compares and contrasts the strengths and weaknesses of these machine learning methods, aiming to help researchers select and improve their methods. Finally, this paper identifies some potential development trends and research directions of machine learning in petroleum engineering based on the current issues.

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Section: Design Optimizationmentioning

confidence: 99%