Reservoir characterization
An openhole advanced formation-evaluation approach is presented that enables assessment of tight-matrix and natural-fracture systems at a level not previously accomplished.
The authors develop an innovative machine-learning method to determine salt structures directly from gravity data.
An appropriate work flow of combining suitable advanced technologies can help to overcome the long-standing challenges of sub-basalt imaging.
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Most of today’s equipment and interpretation methods are indeed not new. After all, well testing has been around for nearly a century, resulting in a legacy that may not always look cutting-edge, but these tried-and-true tools were so technologically remarkable that they became staples. -
The paper outlines examples from tight gas reservoirs in two fields in which openhole data was unavailable and petrophysical analysis was undertaken using cased-hole data.
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The papers explore innovative approaches developed by authors of SPE conference papers toward avoiding data bias, working around the absence of openhole data, and reducing the uncertainty of mass-transport complexes.
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The work and the provided methodology provide a significant improvement in facies classification.
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The authors introduce and compare two quality-control approaches based on two different signal-processing practices.
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Innovators at the Norwegian oil company have developed a machine-learning model that analyzes mud-gas data to predict the gas/oil ratio of wells as they are drilled—something that the industry has worked for decades to accomplish.
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Eni and IBM developed a cognitive engine exploiting a deep-learning approach to scan documents, searching for basin geology concepts and extracting information about petroleum system elements.
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SponsoredThermo Scientific e-Core Software is a unique, high-performance computing platform for the characterization of complex porous media. It focuses on the three essential components of Digital Rock Analysis: parallel computing, multiscale modeling, and process-based reconstruction of 3D volumes.
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The UK offshore Lancaster field was to prove that complex basement formations could be profitably developed. Instead, it is a reminder of how a long-term production test can drastically alter a reservoir model built upon years of exploration work.
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Advances during the past decade in using convolutional neural networks for visual recognition of discriminately different objects means that now object recognition can be achieved to a significant extent.
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