2024
DOI: 10.20944/preprints202404.1945.v1
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Systematic Analysis of Novel Machine Learning Techniques for Hydraulic Fracturing Optimization

Adedamola Alake,
Emmanuel Oyedeji

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-relat… Show more

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