modeling

The authors of this paper apply a deep-learning model for multivariate forecasting of oil production and carbon-dioxide-sequestration efficiency across a range of water-alternating-gas scenarios using field data from six legacy carbon-dioxide enhanced-oil-recovery projects.

This paper assesses the technical feasibility of geological carbon storage in the operator’s Brazilian brownfields, focusing on mature oil fields and associated saline aquifers.

This paper explores the evolving role of the digital petroleum engineer, examines the core technologies they use, assesses the challenges they face, and projects future industry trends.

This paper describes an auto-adaptive workflow that leverages a complex interplay between machine learning, physics of fluid flow, and a gradient-free algorithm to enhance the solution of well-placement problems.

This paper details a data-driven methodology applied in Indonesia to enhance flare-emission visibility and enable targeted reduction strategies by integrating real-time process data with engineering models.

The research facility said it plans to add multiphase-flow-testing capabilities for heavy oil and different viscosities.

Data and impartial viewpoints can help de-risk exploration portfolios and keep resource estimates in check.

In this study, a method was developed to analyze the effects of drilling through transitions on bit-cutting structures and construct an ideal drilling strategy using a detailed drilling model.

This paper describes a machine-learning approach to accurately flag abnormal pressure losses and identify their root causes.

This research aims to develop a fluid-advisory system that provides recommendations for optimal amounts of chemical additives needed to maintain desired fluid properties in various drilling-fluid systems.