An In-Depth Review of Predictive Methods for Oil and Gas Applications

Abstract

The oil and gas industry aims to optimize production, reduce costs, and increase efficiency. Predictive models have gained popularity as potential solutions in recent years. In predictive models, machine learning algorithms analyse oil and gas operations data and forecast future performance. This paper examines the current state of predictive modelling in the oil and gas industry with the objective to systematically review and analyse current research on the predictive modelling in the oil and gas industry. The paper begins by highlighting the sub-fields and datasets in the oil and gas industry that used recent machine learning methods for predictive modelling. Additionally, literature from the Scopus and Web of Science indexes was reviewed. This study assessed recent approaches for oil and gas industry in predictive applications for papers published up until December 2022. The findings identify several advantages and disadvantages that can be used as guidelines to effectively implement predictive modelling in the oil and gas industry. It includes challenges on the requirement for accurate and reliable data, the development of appropriate algorithms, and the integration of predictive models into existing workflows. In addition, the finding highlights the growing application of deep learning algorithms for various tasks as one of the major trends. From the analysis of the state-of-the-art in predictive techniques, it is necessary first to survey the landscape of existing predictive analytics approaches and their methods to employ in oil and gas prediction.