Unconventional/complex reservoirs
The Middle East’s largest unconventional gas development officially begins production as Saudi Aramco targets 6 million BOE/D of gas and liquids capacity by 2030.
This paper provides an account of the design, implementation, and operational insights from an enhanced geothermal system proppant stimulation targeting a volcanic, dry rock setting with an approximately 330°C bottomhole temperature.
Oman is embarking on a renewed effort to deploy the latest hydraulic fracturing technologies and techniques, tailored to its unique reservoirs and challenges.
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This study compares seven imputation techniques for predicting missing core-measured horizontal and vertical permeability and porosity data in two wells drilled in the North Rumaila oil field in southern Iraq. -
This paper describes an approach that combines rock typing and machine-learning neural-network techniques to predict the permeability of heterogeneous carbonate formations accurately.
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This study describes the performance of machine-learning models generated by the self-organizing-map technique to predict electrical rock properties in the Saman field in northern Colombia.
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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.
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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 deal significantly expands the company’s position in the Bakken Shale play of North Dakota.
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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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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.
