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This article examines how clusters with different properties are produced by different clustering algorithms. In particular, it provides an overview of three clustering methods: k-Means clustering, hierarchical clustering, and DBSCAN.

As oil prices break through multiyear highs, the heartland of US onshore activity is nearing the record levels of output reached before the pandemic.

In the authors’ study, a machine-learning predictive model—boosted decision tree regression—is trained, tested, and evaluated in predicting liquid holdup in multiphase flows in oil and gas wells.

This case history explores a multiwell sectional development in the Delaware Basin by a small operator who reduced drilling and completion costs, along with lease operating expenses, by turning undesirable produced water into an asset.

A produced-water management framework is presented, forming part of an upstream-effluent management policy, to address the minimization and ultimate elimination of treated and untreated produced-water discharge.

This paper presents a framework that advances 3D printing in the oil and gas industry, evaluating the challenges this technology is facing in a conservative industry and realizing its potential benefits.

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This paper presents a methodology that aims to allows the anticipation of problems such as mechanically stuck pipe or lockup situations when running casing or completion strings in hole.

As the oil and gas industry evolves to become more socially responsible with using natural resources such as water, economics constraints are an ever-present concern. The three highlighted papers share different approaches regarding how produced water could be managed economically.

The papers selected this year highlight some complex considerations and the importance of using all the tools provided by the industry resource classification systems to navigate projects through to viable and sustainable development reasonably and transparently for all stakeholders.