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Because of inherent complexities, understanding the characteristics of perforations in downhole environments is a significant challenge. Perforation-flow laboratories have been used to provide insight into cleanup and productivity mechanisms around perforation tunnels.

A new geostatistics modeling methodology that connects geostatistics and machine-learning methodologies, uses nonlinear topological mapping to reduce the original high-dimensional data space, and uses unsupervised-learning algorithms to bypass problems with supervised-learning algorithms.

Examples of evolution in well construction during the past several years include new horizontal-well applications, breakthroughs in bit and directional-drilling technology, synthetic fluids, fracture-stimulation advancements, managed-pressure drilling, and the digital revolution.

Time spent on bottom drilling is only approximately 10% of the time consumed by drilling operations, but this still provides a significant opportunity for savings if it can be reduced.

Artificial intelligence has come to the oil patch, accelerating a technical change that is transforming the conditions for the oil and gas industry’s 150,000 US workers.

Testing will be performed at DNV’s labs in Columbus, Ohio, and Norway and overseen by experts in fatigue of subsea equipment, bolting connections, cathodic protection, and instrumented tests.

Many leaders of petroleum engineering schools from around the world have never met but SPE’s technical director for academia would like to change that.

An increasingly buzzy term tossed around at industry events, “digital twin” is leveraging data analytics, machine learning, and artificial intelligence to improve efficiencies from design to decommissioning.

To test your knowledge, answer these 10 questions from Jim Crompton, who teaches petroleum data analytics at the Colorado School of Mines.

Superior, Colorado, trustees approved a 6-month drilling moratorium ahead of plans to explore the town's oil and gas regulatory options.