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This paper presents a novel modeling framework for predicting residual oil saturation in carbonate rocks. The proposed framework uses supervised machine learning models trained on data generated by pore-scale simulations and aims to supplement conventional coreflooding tests or serve as a tool for rapid residual oil saturation evaluation of a reservoir. -
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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This paper presents a unique investigation into determining the sufficient concentration of hardness ions required to significantly reduce the adsorption of acrylamide-tertiary-butyl-sulfonate-based polymer with a focus on mitigating polymer retention in carbonate formations using softened brine.
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A comprehensive study looks at the sealing efficiency of eutectic bismuth-tin alloy plugs in wells slated for plugging and abandonment through laboratory testing, microscopy analyses, and numerical simulations.
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his paper presents research and application of a sustainable, low-density geopolymer alternative to Portland cement for cementing applications in low-temperature wells.
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Fundamental research conducted to derive a transport model for ideal and partitioning tracers in porous media with two-phase flow that will allow fast and efficient characterization and selection of the correct tracer to be used in field applications.
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A self-updating and customizable data-driven strategy for real-time monitoring and management of screenout, integrated with proppant filling index and safest fracturing pump rate, is proposed to improve operational safety and efficiency at field scale.
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A seismic prediction model is developed and presented in a case study to simulate the magnitude and timing of triggered seismic events with the intent to manage and mitigate environmental impacts resulting from induced seismicity during subsurface development activities.
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A universal, automated approach to condition-based maintenance of drilling rig mud pumps is developed using acoustic emission sensors and deep learning models for early detection of pump failures to help mitigate and reduce costs and nonproductive time generally associated with catastrophic pump failures.
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An economic analysis of a wellbore methodology in natural gas fields that uses gasification of methane within the wellbore (not within the reservoir) for hydrogen production while incorporating simultaneous sequestration of carbon. This new methodology offers significant energy and cost savings in addition to zero carbon being produced to the surface.
