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Characterizing interwell connectivity in waterflooded reservoirs using data-driven and reduced-physics models: a comparative study

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An Erratum to this article was published on 24 August 2016

A Commentary to this article was published on 23 August 2016

Abstract

Waterflooding is a significantly important process in the life of an oil field to sweep previously unrecovered oil between injection and production wells and maintain reservoir pressure at levels above the bubble-point pressure to prevent gas evolution from the oil phase. This is a critical reservoir management practice for optimum recovery from oil reservoirs. Optimizing water injection volumes and optimizing well locations are both critical reservoir engineering problems to address since water injection capacities may be limited depending on the geographic location and facility limits. Characterization of the reservoir connectivity between injection and production wells can greatly contribute to the optimization process. In this study, it is proposed to use computationally efficient methods to have a better understanding of reservoir flow dynamics in a waterflooding operation by characterizing the reservoir connectivity between injection and production wells. First, as an important class of artificial intelligence methods, artificial neural networks are used as a fully data-driven modeling approach. As an additional powerful method that draws analogy between source/sink terms in oil reservoirs and electrical conductors, capacitance–resistance models are also used as a reduced-physics-driven modeling approach. After understanding each method’s applicability to characterize the interwell connectivity, a comparative study is carried out to determine strengths and weaknesses of each approach in terms of accuracy, data requirements, expertise requirements, training algorithm and processing times.

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Acknowledgments

This work is supported by the Middle East Technical University Northern Cyprus Campus (METU-NCC)—Campus Research Fund; Project No. BAP-FEN-13-YG-2. The support is gratefully acknowledged.

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  1. Petroleum and Natural Gas Engineering Program, Middle East Technical University, Northern Cyprus Campus, Mersin 10, Kalkanlı, 99738, Güzelyurt, Turkey

    Emre Artun

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Correspondence to Emre Artun.

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An erratum to this article is available at http://dx.doi.org/10.1007/s00521-016-2550-y.

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Artun, E. Characterizing interwell connectivity in waterflooded reservoirs using data-driven and reduced-physics models: a comparative study. Neural Comput & Applic 28, 1729–1743 (2017). https://doi.org/10.1007/s00521-015-2152-0

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