Data & Analytics

In this paper, the authors propose a regression machine-learning model to predict stick/slip severity index using sequences of surface measurements.

From optimizing drilling performance to enhancing worker safety, computer vision can change how the industry works.

Located 230 km south of Abu Dhabi, the onshore Shah field produces around 70,000 B/D of crude.

A new report from GlobalData provides an overview of the digitalization efforts within the industry and their potential to transform operations.

A recent survey conducted by Rackspace Technology reveals new attitudes about using the cloud, including a change from using the public cloud to using private, on-site clouds or a hybrid of the two.

This study examines the implementation of a predictive maintenance method using artificial intelligence and machine learning for offshore rotating production-critical equipment. Conducted over 2 years at Murphy Oil’s deepwater platforms in the Gulf of Mexico, the project aimed to detect equipment issues early, reduce downtime, and streamline maintenance processes.

Moving from use cases to enterprisewide AI is more than a technology challenge. It requires anchoring on value, feedback, and innovation.

The objective of this study is to develop an explainable data-driven method using five different methods to create a model using a multidimensional data set with more than 700 rows of data for predicting minimum miscibility pressure.

The authors present an open-source framework for the development and evaluation of machine-learning-assisted data-driven models of CO₂ enhanced oil recovery processes to predict oil production and CO₂ retention.

The authors of this paper propose hybrid models, combining machine learning and a physics-based approach, for rapid production forecasting and reservoir-connectivity characterization using routine injection or production and pressure data.