Reservoir simulation
As we see in these papers, new tools and techniques are being developed to match the ability of engineers to meet the challenges posed by assets such as shale reservoirs and maturing fields.
This paper presents a step-by-step work flow to facilitate history matching numerical simulation models of hydraulically fractured shale wells.
In the complete paper, the authors present a novel approach that uses data-mining techniques on operations data of a complex mature oil field in the Gulf of Suez that is currently being waterflooded.
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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 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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This paper presents a saturation-modeling approach for fields and reservoirs with complex hydrocarbon-charging histories. The model resolves saturation-height functions for the primary-drainage, imbibition, and secondary-drainage equilibriums.
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This paper proposes a novel work flow for structural-features modeling that allows the introduction of faults and other structural and nonstructural features to any simulation grid without modification.
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Natural fractures can have a significant effect on fluid flow by creating permeability anisotropy in hydrocarbon reservoirs.
