Predictive Analytics Performance on Oil and Gas: A Significant Review

Putri Azmira R Azmi; Marina Yusoff; Mohamad Taufik Mohd Salledud-din

doi:10.20944/preprints202405.0423.v1

Preprint Review Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 6 May 2024 / Approved: 7 May 2024 / Online: 8 May 2024 (09:05:24 CEST)

Enhancing the management and monitoring of oil and gas processes demands developing precise predictive analytics techniques. Over the past two years, oil and its prediction have advanced significantly using conventional and modern Machine Learning techniques. Several review articles detail the developments in predictive maintenance and technical and non-technical aspects of influencing the uptake of big data. The absence of references for machine learning techniques impacts the effective optimization of predictive analytics in the oil and gas sector. This review paper offers readers thorough information on the latest machine learning methods utilized in this industry's predictive analytical modelling. The review covers forms of Machine Learning techniques used in predictive analytic modelling from 2021 to 2023 (91 articles). It provides an overview of the details of the papers that were reviewed, comprising of the model’s categories, the data's temporality, field, and name, the dataset's type, predictive analytics (classification or clustering or prediction), the models' input and output parameters, performance metrics, optimal model, and benefits and its drawbacks. Additionally, suggestions for future research directions are provided to raise the potential of the associated knowledge and increase the accuracy of oil and gas predictive analytics models.

classification; clustering; machine learning; oil and gas; predictive analytics

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

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Predictive Analytics Performance on Oil and Gas: A Significant Review

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