Reservoir
Three papers are highlighted as the primary contributions because of their broad industry relevance. They focus on improving formation particle-size characterization, expanding the application of openhole gravel packs in depleted and compartmentalized reservoirs, and advancing the understanding of capillary pressure in sand production.
This paper aims to establish a set of best practices for generating particle-size-distribution data from core samples.
This paper discusses the successful execution of two openhole gravel-pack completions in two Gulf of Mexico fields with depleted reservoirs.
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This study introduces a cleanup- and flowback-testing approach incorporating advanced solids-separation technology, a portable solution, equipment automation, improved metallurgy, and enhanced safety standards. -
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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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.
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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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Virtual reality and related visualization technologies are helping reshape how the industry views 3D data, makes decisions, and trains personnel.
