Data Science Adoption and Operationalization in the O&G Industry: Challenges and Solutions

Abstract

Abstract The Oil and Gas (O&G) industry is used to cycles of lows and highs due to different challenging economic and political situations. Yet the challenges caused by the sanitary crisis due to the covid-19 pandemic are certainly like no others. The shutdown of a large number of social activities had a direct impact on energy consumption. Many studies [1], [2] and [3] have been published at the beginning of the covid-19 pandemic to predict impacts of the restrictions imposed on a global scale: decline in global oil demand, saturation of storage capacities and delay of exploration and production projects. Companies facing this unprecedented crisis had no option but to adopt innovative ways of driving costs lower and maximizing operational efficiency. As a consequence, the pace at which Data Science (DS) is finding its way to O&G applications has been noticeably accelerated although the O&G industry is one of the latecomers to digitalization [4]. The adoption of DS and data-driven solutions has moved from gaining acceptance in the industry to becoming a necessity to many companies. According to a Gartner survey [5], the O&G industry commitment to investment in digital transformation in general had become the first priority in 2021 while it was third-highest priority in 2019 and not even funded in 2014. This involves investments in data acquisition techniques through innovative sensing technologies but also investments in advanced data aggregation and analytics platforms. AI/ML/analytics are listed in the same survey [5] as "top game-changing technologies in 2021". The 2021 survey also states that 50% of the O&G companies have plans to increase their investments in AI/ML and related fields such as cloud-computing. But adoption and operationalization of DS does not come with no challenges. Acceptance and reliance on data-driven models need a favorable cultural and technical environment that is not necessarily compatible with the conventional corporate-like outlook of O&G companies: Data privacy and ownership regulations can diminish DS efforts. Security restrictions can prevent deployment of ML models to end users. All of these challenges are accentuated by the absence of a clear process model to implement and manage DS projects. In this paper, we survey the actual challenges the O&G industry is facing and present a number of corresponding solutions. The paper is structured as follows. The first section explores the state of the art of data-driven models in the O&G industry. The second section lists the challenges DS is facing within the O&G industry and proposes a classification of these challenges into three main classes, namely: human, data and infrastructure related challenges. The paper also proposes an O&G specific framework for DS projects to overcome these identified challenges.