AI/machine learning
In the past year, publications on CO2, natural gas, and hydrogen storage have increasingly focused on the design, evaluation, and optimization of storage plans. These efforts encompass a broad spectrum of challenges and innovations, including the expansion of storage reservoirs from depleted gas fields and saline aquifers to stratified carbonate formations and heavy-o…
This paper introduces an agentic artificial-intelligence framework designed for offshore production surveillance and intervention.
Reaching further than dashboards and data lakes, the agentic oil field envisions artificial intelligence systems that reason, act, and optimize.
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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.
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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.
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The grant was awarded by the Scottish Funding Council in partnership with Scottish Enterprise to assist in developing an AI demonstrator to optimize subsea decommissioning.
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Undocumented orphaned wells pose hazards to both the environment and the climate. Scientists are building modern tools to help locate, assess, and pave the way for ultimately plugging these forgotten relics.
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As we turn the page on our 75th anniversary, JPT’s recent visit to the UAE offers a front-row seat of what some of the industry’s biggest players see coming.
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The USGS has said up to 19 million tons of lithium resource is contained in the briny waters of the Smackover formation in Arkansas.
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Subject-matter experts from industry and academia advanced distributed fiber-optic sensing technologies and their implementation in flow measurement during a special session.
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Technology uptake aimed at optimizing resources, delivering consistency, and augmenting what humans can do.
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This paper investigates the use of machine-learning techniques to forecast drilling-fluid gel strength.
