Reservoir simulation
The aim of this study is to incorporate detailed geological, petrophysical, and hydraulic fracturing models to better predict and mitigate the effects of interbench interactions.
The objective of this paper is to apply a developed workflow to determine the propped hydraulic fracture geometry in a horizontal multistage fractured well, incorporating production, pressure, and strain data.
This study explores the feasibility of implementing in-situ carbon dioxide recycling for sequestration as a fit-for-purpose developmental strategy for a Malaysian gas field characterized by an initial carbon-dioxide content of approximately 60%.
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The Russian company has built a computing cluster in St. Petersburg designed to generate digital twins of oil fields. The new distributed-computing system is capable of processing more than 100 gigabits per second, speeding up the digital-modeling process five-fold. -
In this paper, the authors show the limitation of CEOS for modeling reservoir behavior of liquid-phase black and volatile oil in highly undersaturated reservoirs.
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In my view, we still do not possess a full understanding of oil production in unconventional fractured reservoirs. Our ability to forecast such assets remains elusive, even with copious amounts of analytics, mountains of data, and an arsenal of machine-learning tools.
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In this work, the authors developed a numerical model of in-situ upgrading (IU) on the basis of laboratory experiences and validated results, applying the model to an IU test published in the literature.
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Machine-learning methods have gained tremendous attention in the last decade. The underlying idea behind machine learning is that computers can identify patterns and learn from data with minimal human intervention. This is not very different from the notion of automatic history matching.
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This paper outlines the value of 4D for reducing uncertainty in the range of history-matched models and improving the production forecast.
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The struggle to overcome the challenge of frac hits has led to a critical dialogue about which pathway the shale sector should take. One idea is to simply put the problem at the center of every major decision.
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A new geostatistics modeling methodology that connects geostatistics and machine-learning methodologies, uses nonlinear topological mapping to reduce the original high-dimensional data space, and uses unsupervised-learning algorithms to bypass problems with supervised-learning algorithms.
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Natural fractures can have a significant effect on fluid flow by creating permeability anisotropy in hydrocarbon reservoirs.
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A radical digital revolution is happening all around us (or so we are told). Applying this to reservoir simulation, we apparently need to understand better when and, more importantly, when not to use such technology—to appreciate its bounds, its limitations, its range of validity, and so on.
