Managing data lineage of O&G machine learning models: The sweet spot for shale use case

Abstract

Machine Learning has increased its role in several industries, becoming an essential tool, and competitive advantage. However, questions around training data lineage, or provenance, e.g., “where did the data used to train this model came from?”; the introduction of several new data protection legislation; and, the need for data governance requirements, has hindered the adoption of machine learning models in the real world. In this paper, we discuss how data lineage can be leveraged to benefit the Machine Learning (ML) lifecycle to build ML models to discover sweet-spots for shale oil and gas production, a major application for the Oil and Gas (O&G) Industry.