Reservoir
Sustainability in reservoir management emerges not from standalone initiatives but from integrated, data-driven workflows, where shared models, closed-loop processes, and AI-enabled insights reduce fragmentation and make sustainable performance a natural outcome.
The $100-billion project is widely considered the largest unconventional development outside of the US and is noted by Aramco as the largest nonassociated gas development in the kingdom.
Data and impartial viewpoints can help de-risk exploration portfolios and keep resource estimates in check.
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The merger creates one of the largest oil-focused independents in the US with output of 526,000 BOE/D. -
BPX Energy will retain operatorship of the assets after the sale of its interests to private investment firm Sixth Street is complete.
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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.
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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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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 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.
