  Machine Learning (ML) has increased its role, becoming essential in several
industries. However, questions around training data lineage, such as "where has
the dataset used to train this model come from?"; the introduction of several
new data protection legislation; and, the need for data governance
requirements, have hindered the adoption of ML models in the real world. In
this paper, we discuss how data lineage can be leveraged to benefit the ML
lifecycle to build ML models to discover sweet-spots for shale oil and gas
production, a major application in the Oil and Gas O&G Industry.
