Reservoir characterization
This work investigates the root cause of strong oil/water emulsion and if sludge formation is occurring within the reservoir using a robust integrated approach.
In this work, a perturbed-chain statistical associating fluid theory equation of state has been developed to characterize heavy-oil-associated systems containing polar components and nonpolar components with respect to phase behavior and physical properties.
The paper describes a parameter inversion of reservoirs based on featured points, using a semi-iterative well-test-curve-matching approach that addresses problems of imbalanced inversion accuracy and efficiency.
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Deciding whether to develop a new discovery is often about the data. Without the correct understanding of the fluids in the reservoir, projects are unlikely to turn out as planned. -
The authors develop an innovative machine-learning method to determine salt structures directly from gravity data.
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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.
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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.
