DSDE: In Theory

Sustainability data management is a relatively new discipline that requires a unique set of tools, processes, and procedures to meet corporate demands.

The authors of this paper propose an automated approach to sand prediction and control monitoring that improved operational efficiency by reducing time spent on manual analysis and the decision-making process in a Myanmar field.

This paper focuses on characterization of fracture hits in the Eagle Ford, methods to predict their effects on production, and mitigation techniques.

This paper explores electrical submersible generator design considerations, theoretical underpinnings, and potential future applications.

Machine learning has been shown to have a promising role in oil and gas explorations in recent years. Among the applications, determining a proper location for injection and production wells along with their optimal operating conditions is a complex problem.

The authors of this paper describe a technology built on a causation-based artificial intelligence framework designed to forewarn complex, hard-to-detect state changes in chemical, biological, and geological systems.

This paper presents a family of machine-learning-based reduced-order models trained on rigorous first-principle thermodynamic simulation results to extract physicochemical properties.

In this paper, example machine-learning models were trained using geologic, completion, and spacing parameters to predict production across the primary developed formations within the Midland Basin.

This article presents the application of a reinforcement learning control framework based on the Deep Deterministic Policy Gradient. The crack propagation process is simulated in Abaqus, which is integrated with a reinforcement learning environment to control crack propagation in brittle material. The real-world deployment of the proposed control framework is also dis…

This paper describes a work flow that integrates data analysis, machine learning, and artificial intelligence to unlock the potential of large relative permeability databases.