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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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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.
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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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Train 1 of the Corpus Christi LNG Stage 3 project is expected to reach substantial completion during the first quarter of 2025.
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The Scarborough Energy Project is on track to start delivering liquefied natural gas in 2026.
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Australia’s Ichthys LNG facility will provide feedstock, along with production from legacy Japanese gas fields that will also serve as repositories for CO2 storage.
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The formal design and construction contract award comes after an LOI for the work was signed this past summer.
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Canadian operator expands its Deep Basin gas footprint in Alberta, adding 700 new drilling locations.
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This polymer retention study examines temperature effect on an ATBS-based polymer, using single- and two-phase retention studies and different analytical methods in the presence and absence of oil.
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After being a non-factor in the energy industry for years, the country is now one of the fastest growing regions.
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