DSDE: In Theory

In this paper, the authors present data analyses to comprehensively evaluate the performance of a steady-state multiphase-flow point model in predicting high-pressure, near-horizontal data from independent experiments.

The authors of this paper describe a model-driven work flow developed for hydraulic fracturing design and execution that could be a resource for other shale plays with similar challenges worldwide.

This paper presents a case study of integrated geomechanical and reservoir simulation with a developed fracture conductivity calculation work flow to evaluate well spacing and completions design.

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

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

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.

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.

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.

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.