Reservoir characterization
The objective of this study is to numerically investigate system behavior when storing H2/natural gas (CH4) mixtures in aquifer-related underground gas storage, and the effect of gas composition and salinity on energy-recovery efficiency.
The authors describe the effectiveness of an electromagnetic look-ahead service while drilling in terms of providing accurate formation profiles ahead of the bit to optimize geostopping efficiency.
In the past year, publications on CO2, natural gas, and hydrogen storage have increasingly focused on the design, evaluation, and optimization of storage plans. These efforts encompass a broad spectrum of challenges and innovations, including the expansion of storage reservoirs from depleted gas fields and saline aquifers to stratified carbonate formations and heavy-o…
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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 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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Automated image-processing algorithms can improve the quality and speed in classifying the morphology of heterogeneous carbonate rock. Several commercial products have produced petrophysical properties from 2D images and, to a lesser extent, from 3D images.
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The complete paper presents a technical discussion of a new microsampling technique for LWD and a corresponding wellsite technique to provide compositional interpretation, contamination assessment, reservoir-fluid compositional grading, and reservoir compartmentalization assessment.
