Reservoir
Coreflood experiments reveal that alkali significantly reduces viscosity loss in polyacrylamide flooding systems by minimizing adsorption and improving polymer stability under shear stress. The findings position viscosity retention as a key screening criterion for successful alkali-polymer enhanced-oil-recovery applications.
This SPE technical section editorial synthesizes six technical themes shaping CO2 transport and storage and interprets them through a petroleum engineering capability lens.
The UAE project is expected to produce up to 345 MMscf/D by 2029, with expansion potential to 1 Bcf/D.
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The new frontier of production improvement combines surveillance techniques and analysis to determine which variables boost output. -
The KRI has become a significant player in the oil and gas industry. The authors use production data from key fields to explore the factors influencing both individual fields and overall production. An overview of the challenges and milestones in the region’s oil and gas sector from 2014 to 2023 enhances understanding of its evolution, current status, and future oppor…
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High prices for untapped drilling locations in the Permian Basin have sparked some new trends in the tight oil dealmaking space.
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Aramco’s investment pivot to gas aims to propel Saudi Arabia into the top tier of gas producers and LNG players globally by 2030.
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The Integrated Reservoir Management Technical Section is committed to unite a community of technical professionals and academia driven to enhance reservoir performance by harnessing technological innovations and creating a collaborative space for strategic discussions and sustainable practices.
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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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The deal significantly expands the company’s position in the Bakken Shale play of North Dakota.
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The authors of this paper review the advantages of machine learning in complex compositional reservoir simulations to determine fluid properties such as critical temperature and saturation pressure.
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This paper details how the reservoir modeling workflow can be accelerated, and uncertainty reduced, even for challenging greenfield prospects by constructing multiple small fit-for-purpose integrated adaptive models.
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This paper highlights an approach of using active hydrogen to stimulate hard-to-recover formations from candidate-well selection through pilot execution and evaluation.
