Reservoir characterization
This paper presents a novel methodology for assessing the rapid mineral carbonation of carbon dioxide through geochemical interactions with carbon-, magnesium-, and iron-rich minerals abundant in geological formations.
This study integrates physics-based constraints into machine-learning models, thereby improving their predictive accuracy and robustness.
This paper introduces a machine-learning approach that integrates well-logging data to enhance depth selection, thereby increasing the likelihood of obtaining accurate and valuable formation-pressure results.
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SponsoredIn a partnership with the American Institute of Formation Evaluation, TGS is now the only US data vendor offering report-sourced drillstem tests, available through TGS Well Data Analytics. -
This paper describes natural fractures and their effect on hydrocarbon productivity in the Vaca Muerta shale formation.
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The authors of this paper describe a solution using machine-learning techniques to predict sandstone distribution and, to some extent, automate the process of optimizing well placement.
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The seismic and well data investigation tool integrates work flows to provide a consistent analysis of reservoirs.
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In Part 2 of a series of articles, the author focuses on generating a connection between the problem types and various methods of trying to solve these problems.
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Artificial intelligence (AI) tools have been used in geological survey methods for many years. Gaining insight into the scale and trends of this implementation could assist surveyors in making informed decisions about buying or developing new technologies.
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This paper describes the building of a geomechanical model for an offshore field that integrated drilling, geology, petrophysics, and reservoir data to play a major role in the drillability and deliverability of the reservoir.
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The authors of this paper present an approach integrating characterization of paleo zones, parameterization of paleo-zone conductivity, and application of flow profiles in a history-matching study of a dual-porosity/dual-permeability model.
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The coupled geomechanical and dynamic flow simulation work flow described in this paper relies on a multidisciplinary approach to meet future peak gas demands and support clean-energy initiatives.
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In Part 1 of a series of articles, the author highlights candidate selection and problem clarification. This section describes how the success rate of solving conformance problems is improved by reducing assumptions and improving your problem understanding prior to executing a solution.
