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The objective of this study was to establish an efficient optimization work flow to improve vertical and areal sweep in a sour-gas injection operation, thereby maximizing recovery under operation constraints.

The objective of this paper is to present a fundamentals-based model of three-phase flow consistent with observation that avoids the pitfalls of conventional models such as Stone II or Baker’s three-phase permeability models.

The authors of this paper describe a solution using machine-learning techniques to predict sandstone distribution and, to some extent, automate the process of optimizing well placement.

This paper presents an intelligent tube solution that combines data retrieved by the sensors with the actual resistance of each pipe in the well to allow adjustment of production parameters while ensuring installation safety.

This paper introduces a method using a Bayesian network to aggregate trends detected in time-series data with events identified by natural language processing to improve the accuracy and robustness of kick and lost-circulation detection.

This study catalogs global gas/oil ratio data to identify currently produced light crude oils that could be rendered carbon neutral through the direct-air-capture mechanism.

A new program offers an affordable way to figure out if salt precipitation could be behind underperforming gas wells and suggests a path to higher production.

Benriach well finds subeconomic natural gas accumulation.

The supermajor redefined its Mad Dog II development in three key ways: a blank-sheet redesign to cut costs, altering the way it works as a result of the COVID-19 pandemic, and a digital twin offering unique access to the asset from anywhere in the world.

Leading energy technology companies unite to unlock efficiencies and increase reserve recoveries using digital advances.