Reservoir
This paper provides insight into existing tools, methodologies, and processes that can help any capital project or organization improve predictability of outcome while mitigating risks and exploiting opportunities effectively.
This study presents a novel hybrid approach to enhance fraud detection in scanned financial documents.
This paper presents a study of trapped-torque incidents during drillpipe connection, highlighting contributing factors and root causes and proposing prevention and mitigation measures for these transient events.
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This paper describes development of a high-temperature water-based reservoir drill-in fluid using a novel synthetic polymer and customized with optimal chemical concentrations and sized calcium carbonate. -
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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This paper explains that the discovery of specific pressure trends, combined with an unconventional approach for analyzing gas compositional data, enables the detection and prediction of paraffin deposition at pad level and in the gathering system.
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The aim of this study is to incorporate detailed geological, petrophysical, and hydraulic fracturing models to better predict and mitigate the effects of interbench interactions.
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This paper describes a study to design and implement an enhanced oil recovery project via huff ’n’ puff using Y-grade injectant.
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The objective of this paper is to apply a developed workflow to determine the propped hydraulic fracture geometry in a horizontal multistage fractured well, incorporating production, pressure, and strain data.
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This paper presents an approach to subsea hydrate-risk management based on the understanding that some crudes have induction properties that delay hydrate formation even when the pressure and temperature conditions reach the hydrate thermodynamic region.
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This study compares water-based chemicals including surfactants, nanoparticles, and ketones that can be used for enhancing the oil recovery of shale-oil reservoirs.
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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.
