Reservoir
This case study describes how edge computing and industrial internet of things platforms were deployed to automate and optimize production operations across four distinct basins.
Output is rising fast in the South American shale play and putting Argentina on a course to soon reach 1 million B/D.
This case study presents a procedure in which the operator compared production from wells with adjusted wettability to a control group, finding that the adjustments resulted in significant improvements in production and reductions in produced water.
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Technical papers reviewed for this feature are laden with novel technology borne of the quest to understand and solve complex geological structures and features that ultimately will improve our collective effort toward fostering efficient energy production. The three papers presented here are focused on innovative approaches to handling such complexities. -
A Shell partnership with YPF marks a significant milestone for the Argentina LNG export facility, raising new questions about the nation’s potential to unlock the economic power of its vast shale reserves.
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Diversified Energy announces its largest deal yet to buy private equity-owned Maverick Natural Resources.
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A numerical simulation study based on experimental data of 2D and 3D models is presented to examine immiscible fingering during field-scale polymer-enhanced oil recovery.
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This selection of cutting-edge articles spotlights how experimental concepts are now driving cost-saving strategies in unconventional development. It’s a reminder that innovation often comes from creative thinking, not just new tools or tech partnerships.
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Rystad Energy and Wood Mackenzie highlight key factors shaping the balancing act in the upstream oil market.
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CO₂ enhanced oil recovery (EOR) provides an attractive and commercially established technique to store CO₂ underground. EOR modeling is crucial because complex simulation is required to predict the behavior of CO₂ and its interaction with the oil and reservoir rock.
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Virtual reality and related visualization technologies are helping reshape how the industry views 3D data, makes decisions, and trains personnel.
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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.
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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.
