Reservoir simulation
This paper presents a fundamental research study with the main objective of building a mechanistic numerical model that captures the important mechanisms of polymer flooding through various mechanistic equations using a combined reservoir flow and geochemical numerical simulator.
The authors of this paper describe reservoir-fluid-geodynamics processes that explain the reasons behind varying oil compositions and properties within and across different reservoir compartments.
In this study, a deep-neural-network-based workflow with enhanced efficiency and scalability is developed for solving complex history-matching problems.
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Looking back through previous editions of this article, I note that, in 2011, I wrote, “there’s a growing tendency in some quarters to use very simple models.” That may be true, but there is also a growing tendency among vendors to offer models with more and more features. -
Large volumes of gas can be produced at high rates with conventional horizontal- or vertical-well configurations for long periods of time from some methane-hydrate accumulations by means of depressurization-induced dissociation.
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Initializing a reservoir simulator requires populating a 3D dynamic-grid-cell model with subsurface data and fit-for-purpose interrelational algorithms. In practice, these prerequisites rarely are satisfied fully.
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This paper describes an all-in-one system that combines nodal-analysis and numerical-simulation models to calculate the effect of intelligent-completion components—such as swell packers, internal control valves, and inflow-control devices—on lateral production profiles.
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The Kitan oil field consists of three subsea intelligent wells. The intelligent completions were modeled in detail using commercial dynamic-simulation software to establish a sound and safe operating procedure for the well cleanup and well test.
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