Reservoir
This month’s selections of unconventional technology articles investigate the factors affecting productivity and performance and highlight the need for effective strategies to enhance performance and mitigate impairment issues.
The service giant shares how its new technology can sense rock properties 50 ft ahead of the bit in horizontal wells.
This study aims to thoroughly assess the influence of various completions, fracturing stimulation, and intrinsic reservoir properties affecting the productivity of 10 major unconventional plays while uncovering insights and trends unique to each play
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This paper outlines the importance of numerical rate transient analysis for dry gas wells, describing a simple, fully penetrating planar fracture model. -
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 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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This paper highlights an approach of using active hydrogen to stimulate hard-to-recover formations from candidate-well selection through pilot execution and evaluation.
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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.
