Version 1
: Received: 25 April 2024 / Approved: 29 April 2024 / Online: 30 April 2024 (08:43:08 CEST)
How to cite:
Alake, A.; Oyedeji, E. Systematic Analysis of Novel Machine Learning Techniques for Hydraulic Fracturing Optimization. Preprints2024, 2024041945. https://doi.org/10.20944/preprints202404.1945.v1
Alake, A.; Oyedeji, E. Systematic Analysis of Novel Machine Learning Techniques for Hydraulic Fracturing Optimization. Preprints 2024, 2024041945. https://doi.org/10.20944/preprints202404.1945.v1
Alake, A.; Oyedeji, E. Systematic Analysis of Novel Machine Learning Techniques for Hydraulic Fracturing Optimization. Preprints2024, 2024041945. https://doi.org/10.20944/preprints202404.1945.v1
APA Style
Alake, A., & Oyedeji, E. (2024). Systematic Analysis of Novel Machine Learning Techniques for Hydraulic Fracturing Optimization. Preprints. https://doi.org/10.20944/preprints202404.1945.v1
Chicago/Turabian Style
Alake, A. and Emmanuel Oyedeji. 2024 "Systematic Analysis of Novel Machine Learning Techniques for Hydraulic Fracturing Optimization" Preprints. https://doi.org/10.20944/preprints202404.1945.v1
Abstract
Over the past decade, the volume and quality of data in the oil and gas industry have exploded, breeding exciting opportunities to implement machine learning for better data- driven decisions. One critical example is hydraulic fracturing (HF), given our ever-growing reliance on HF to meet global hydrocarbon demand. This paper systematically explores the work of several researchers to apply ML techniques (including linear regression, neural networks, support vector machine, decision trees, and more) to HF-related operations, from production forecast to HF design. Furthermore, this study examines how optimization algorithms (including gradient-free, differential evolution, and surrogate-based optimizations) are incorporated with different ML techniques for selecting optimal HF parameters (like fracture number, proppant concentration, and more) during the planning process. The paper aims to provide a comprehensive overview without delving too deeply into technical intricacies to ensure accessibility for a broader audience. Additionally, it introduces innovative techniques currently being integrated into the industry and offers a clear understanding of the processes involved. Ultimately, the analysis concludes that machine learning is an accurate and cost-effective alternative for hydraulic fracturing optimization.
Environmental and Earth Sciences, Geochemistry and Petrology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
