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Oil price visibility, projects competing for funds, and regional market softness are all factors resulting in muted demand for deepwater rigs this year.

Virtual reality and related visualization technologies are helping reshape how the industry views 3D data, makes decisions, and trains personnel.

This month’s column offers a review of the perceived quality of SPE's publications and how this perceived quality could affect the value and impact of SPE's generative AI deliverables. Quality can be subjective, so this column focuses on key publication indicators before assessing remedial options in a future column.

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.

This paper focuses on the vital task of identifying bypassed oil and locating the remaining oil in mature fields, emphasizing the significance of these activities in sustaining efficient oilfield exploitation.

This paper tests several commercial large language models for information-retrieval tasks for drilling data using zero-shot, in-context learning.

In this study, artificial-intelligence techniques are used to estimate and predict well status in offshore areas using a combination of surface and subsurface parameters.

Geothermal energy in the US has historically been concentrated in the West due to favorable geology, but emerging technologies have expanded the possibilities.

The authors of this paper describe how deployment of dual-casing cement-bond-logging technology has provided critical insights in real time for decision-making on remedial jobs.

Given the diversity of coiled tubing well-intervention data, many acquisition labels are often missing or inaccurate. The authors of this paper present a multimodal framework that automatically identifies job type and technologies used during an acquisition.