Abstract
With the ongoing evolution of both the quality and quantity of data in the oil and gas fields, the optimal implementation of machine learning becomes of great benefit. The repetitive iterations process of approaching problem-solving mechanisms can yield effective and functional outputs for the gains of the industry. Notably, this paper aims to examine the imminent methods of the machine learning techniques that are associated with hydraulic fracturing operations, especially in shale gas reservoirs. This paper mainly focuses on the design, interpretation, real-time prediction, and re-frac selection of hydraulic fracturing. The discussion of the findings is aimed to be broad with less focus on depth to avoid complexities and help readers acquire a general sense. Also, it introduces the novel techniques that are being implemented in the industry and portrays a clear depiction of the processes involved. The analysis of the discussion concludes machine learning as an accurate method to deal with a large amount of fracturing data for designing, prediction, and real-time analysis.
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Sprunger, C., Muther, T., Syed, F.I. et al. State of the art progress in hydraulic fracture modeling using AI/ML techniques. Model. Earth Syst. Environ. 8, 1–13 (2022). https://doi.org/10.1007/s40808-021-01111-w
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DOI: https://doi.org/10.1007/s40808-021-01111-w