DSDE: In Theory
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This paper describes a work flow that integrates data analysis, machine learning, and artificial intelligence to unlock the potential of large relative permeability databases.
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The objective of this paper is to present a fundamentals-based model of three-phase flow consistent with observation that avoids the pitfalls of conventional models such as Stone II or Baker’s three-phase permeability models.
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This paper describes efforts at the Hydraulic Fracturing Test Site 2 to collect a research data set to improve understating of spatial and temporal hydraulic fracture dimensions, proppant distribution, and reservoir depletion.
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This study introduces a data-driven virtual viscosity meter as a tool to augment inline and laboratory viscosity measurements.
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This paper presents a novel mathematical model for design and evaluation of fluoroboric acid treatments that takes into account the chemical kinetics and equilibrium aspects of important reactions and fluid flow inside the reservoir rock.
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This paper proposes a series of work flows to simplify model deployment and set up an automatic advisory system to provide insight in justifying an engineer’s day-to-day engineering decisions.
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This paper presents an artificial intelligence algorithm called dual heuristic dynamic programming that can be used to solve petroleum optimization-control problems.
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The authors of this paper present an approach integrating characterization of paleo zones, parameterization of paleo-zone conductivity, and application of flow profiles in a history-matching study of a dual-porosity/dual-permeability model.
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This paper compiles recommendations from a broad range of sources into a single document to aid future intelligent-completion installations and operations.
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The authors of this paper develop a robust history-matched reservoir simulation model capable of predicting polymerflooding performance in the first such pilot to enhance heavy oil recovery on Alaska’s North Slope.
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