data analytics

The authors of this paper describe a project aimed at automating the task of cuttings descriptions with machine-learning and artificial-intelligence techniques, in terms of both lithology identification and quantitative estimation of lithology abundances.

The combined firm aims to create a stronger and more diversified geophysical company and energy data provider.

This paper presents the concept of a supervisory and advisory system dedicated to support the detection of abnormal events and to provide guidelines for fluid treatment.

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

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 partnership plans to develop an overlying software suite, Data Mesh, to consolidate data from various sources and increase data-access efficiency.

This article explains what deep learning is and how it works and presents an example use case from the energy industry.

This paper summarizes techniques for production allocation using geochemical methods and describes a best practice for a specialized approach.

Understanding the subsurface is crucial to the success of carbon capture and storage, and digital solutions are essential for an accurate analysis of the subsurface being considered.

Health, safety, and environment operations can be greatly enhanced by using artificial intelligence (AI) techniques on HSE data. One important aspect inherent in this process is the need to establish trust in the AI system among the users.