Predictive Modeling Workflow for E&P Engineers

This guide presents a predictive modeling workflow specifically designed for exploration and production (E&P) engineers in the oil and gas industry. It outlines how to utilize advanced data science and machine learning techniques to identify key performance drivers and optimize production strategies. The document details the importance of good data, emphasizing that Analytics Explorer's algorithms are built from extensive datasets and tested to address E&P challenges. It explains the process of building predictive models, including the evaluation of controllable and non-controllable factors affecting production. The guide also covers the significance of feature importance ranking in assessing well data and the necessity of validating models against actual production data. Furthermore, it discusses how to use predictive models to develop effective completions strategies, optimize production systems, and understand the relationships between various well types. Overall, the guide serves as a comprehensive resource for E&P engineers aiming to enhance decision-making through data-driven approaches.