The amount of change expected in the next few years in the Oil and Gas industry is relentless, fast-paced, and quite daunting. Currently, the energy sector heavily relies on the Oil and Gas industry as a source of energy. Oil and Gas resources are undoubtedly finite. We are bound to be affected by clean energy transitions, so every part of the industry needs to consider how to respond. At the core of this evolution is the implications of the digital transformation journey.

Technological innovations are fundamental to increasing efficiency in our day-to-day operations and mitigating unforeseen dangers through predictions. How well positioned are we to adapt to changes in energy demand dynamics? In recent times, machine learning (ML) and data science have found prominence in solving complex problems in many industries using only data. The industry we are in is perceived to be a generator of huge amounts of data (Big data) from acquisition, processing, interpretation, integration with other datasets, and production.

To transform the dataset into purposeful and logical information, data science approaches assimilate visualization and statistical techniques with ML and trend recognition patterns. The increase in computing power, information processing, internet accessibility, open-source libraries, and cloud technology have also largely contributed to the advancements in the digital world.

I will introduce a conceptual framework that will enable people to understand the sort of problems that can be solved with these techniques, ranging from data management to a more technical geoscience predictive workflow. As a geoscientist who has had the privilege of diving into analytics, I look forward to giving an understanding of what can be realistically done with existing tools.

Machine learning has been around for decades, but somehow has not gained traction in our operations as Kenyan geoscientists and as people who constantly interact with data. With this research opportunity, we hope to build a community passionate about programming, hoping to gain nuanced insights from their data and lead to decisions that are more data-driven.

Keywords: Data Science, Machine learning, Big data

References The Challenge of Big Data and Data Science, Henry E. Brady Annual Review of Political Science 2019 22:1, 297-323