DSDE: In Theory
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This paper highlights a new online system for monitoring drilling fluids, enabling intelligent control of drilling-fluid performance.
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This paper presents a workflow that combines probabilistic modeling and deep-learning models trained on an ensemble of physics models to improve scalability and reliability for shale and tight-reservoir forecasting.
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This paper describes a new application that leverages advanced machine-learning techniques in conjunction with metocean forecasts to predict vessel motions and thruster loads.
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This paper proposes a holistic, automatic, and real-time characterization of cuttings/cavings, including their volume, size distribution, and shape/morphology, while integrating 3D data with high-resolution images to pursue this objective for use in the real-time assessment of hole cleaning sufficiency and wellbore stability and, consequently, for the prediction, prev…
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This paper describes a new integrated workflow for automated well monitoring using pressure and rate measurements obtained with permanent gauges and flowmeters.
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The goal of the project described in this paper was to test the added value of shear-wave velocity interpreted from underwater multichannel analysis surface waves, particularly as a propagation tool of geotechnical information.
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This paper assesses the potential of augmented depletion development in four US plays: Bakken, Eagle Ford, Midland, and the Anadarko Basin.
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This paper presents a clear and consistent method for determining dead and live crude EACNs using a single reliable method, highlighting a graphical way to determine the optimal salinity and its uncertainties using real data.
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Chevron looks into using remotely operated vehicles to scrub marine growth from subsea structures.
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This paper describes a data-driven approach for liquid-loading detection and prediction that harnesses high-frequency gas-rate and tubinghead-pressure measurements to identify the onset of liquid loading and correct critical rates computed by empirical methods.
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