Unconventional/complex reservoirs
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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.
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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. -
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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Based on 6 years of firsthand experience, refrac experts share some of their biggest insights into where the US market is headed and how to identify the best candidates.
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The new frontier of production improvement combines surveillance techniques and analysis to determine which variables boost output.
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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 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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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.
