modeling
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This paper describes an approach that combines rock typing and machine-learning neural-network techniques to predict the permeability of heterogeneous carbonate formations accurately. -
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.
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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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This paper discusses the ecosystem challenges that face the development of nonmetallic tubulars, ranging from academia and research institutes to material suppliers and manufacturing facilities for pipe prototyping.
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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.
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This work presents an integrated multiphase flow model for downhole pressure predictions that produces relatively more-accurate downhole pressure predictions under wide flowing conditions while maintaining a simple form.
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This paper details how the reservoir modeling workflow can be accelerated, and uncertainty reduced, even for challenging greenfield prospects by constructing multiple small fit-for-purpose integrated adaptive models.
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This study introduces a detailed model to capture the physics and chemistry of acid flow in complex horizontal wells completed in carbonate formations.
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This paper describes development-plan optimization and a probabilistic uncertainty study using Latin hypercube experimental design constrained to production performance in a deepwater Gulf of Mexico field.
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This paper presents the design and development of a prototype intelligent water-injection and smart allocation tool aimed at achieving autonomous waterflood operations.
