Reservoir
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.
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.
The deal significantly expands the company’s position in the Bakken Shale play of North Dakota.
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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. -
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 work presents an integrated multiphase flow model for downhole pressure predictions that produces relatively more-accurate downhole pressure predictions under wide flowing conditions while maintaining a simple form.
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This study describes a technique combining borehole acoustic reflection imaging with production logging in a horizontal well experiencing high water cut with the aim of enabling a mitigation plan.
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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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The authors investigate the utility of applying unconventional technology to low- or variably producing carbonate reservoirs to increase estimated ultimate recovery and decrease development-scale variability.
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The aim of this study is to address and discuss the reservoir engineering aspects of geological hydrogen storage.
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The honor recognizes recipients for their lasting and significant contributions in the field of IOR.
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The integration of artificial intelligence/machine learning with traditional workflows marks a turning point, unleashing the immense potential of these proven techniques to address our everyday challenges in reservoir simulation.
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Unconventional reservoirs bear a unique perplexity in that, at every scale, they are different from their conventional counterparts and even one another. This month’s selection of papers is all about those differences.
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