Enhanced recovery
This paper provides an account of the design, implementation, and operational insights from an enhanced geothermal system proppant stimulation targeting a volcanic, dry rock setting with an approximately 330°C bottomhole temperature.
This paper reviews the simultaneous supercritical CO2/brine aquifer injection and water-alternating-gas methods for geologic carbon sequestration and proposes a novel integration with saltwater-disposal wells.
This paper introduces an agentic artificial-intelligence framework designed for offshore production surveillance and intervention.
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This paper describes modeling work performed to design fracturing treatment and spacing for wells in the Project Cape enhanced geothermal system in Utah. -
The award comes as a contract release purchase order under a long-term agreement that simplifies ongoing efforts to maintain mature field production in Saudi Arabia.
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Operators are turning to new gas-lift and nanoparticle-fluid technologies to drive up production rates.
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This paper addresses the difficulty in adjusting late-stage production in waterflooded reservoirs and proposes an integrated well-network-design mode for carbon-dioxide enhanced oil recovery and storage.
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This work presents the development of fast predictive models and optimization methodologies to evaluate the potential of carbon-dioxide EOR and storage operations quickly in mature oil fields.
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The authors of this paper apply a deep-learning model for multivariate forecasting of oil production and carbon-dioxide-sequestration efficiency across a range of water-alternating-gas scenarios using field data from six legacy carbon-dioxide enhanced-oil-recovery projects.
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As the industry accelerates carbon capture, use, and storage initiatives, modeling innovations for carbon-dioxide injection and enhanced oil recovery have become critical for optimizing recovery and ensuring secure storage. Recent studies highlight a shift toward data-driven and hybrid approaches that combine computational efficiency with operational practicality.
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Entrepreneurial mindsets with the motivation to explore new materials, not limited to focusing on traditional hydrocarbon gas, carbon dioxide, and chemicals such as polymer and surfactant, are becoming more important for broadening prospects beyond the conventional EOR scene.
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This paper discusses a comprehensive hybrid approach that combines machine learning with a physics-based risk-prediction model to detect and prevent the formation of hydrates in flowlines and separators.
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The objective of this microfluidic investigation is to identify and test two novel applications for magnetic fluids in porous media for subsurface oilfield applications.
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