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Recent Development of Smart Field Deployment for Mature Waterflood Reservoirs

Author

Listed:
  • Deli Jia

    (Research Institute of Petroleum Exploration & Development, PetroChina, Beijing 100083, China)

  • Jiqun Zhang

    (Research Institute of Petroleum Exploration & Development, PetroChina, Beijing 100083, China)

  • Yanchun Li

    (School of Mechanics Science & Engineering, Northeast Petroleum University, Daqing 163318, China)

  • Li Wu

    (School of Energy, China University of Geosciences (Beijing), Beijing 100083, China)

  • Meixia Qiao

    (School of Mechanics Science & Engineering, Northeast Petroleum University, Daqing 163318, China)

Abstract

In the petroleum industry, artificial intelligence has been applied in seismic and logging interpretation, accurate modeling, optimized drilling operations, well dynamics prediction, safety warning, etc. However, field-scale application and deployment remain a challenge due to the lack of sufficiently powerful algorithms for the integration of multi-granularity data in the time and space domain, for the construction of a deep-learning network able to represent the evolution of well and reservoir dynamics, and finally the lack of investment in surveillance data acquisition. This paper offers a concise review of smart field deployment for mature waterflood reservoirs, including the current status of data foundation construction, and the research progress for applied AI algorithms, as well as application scenarios and overall deployment. With respect to data, the domestic and international oil and gas industry has completed or at least started the large-scale construction and deployment of lake data. However, the data isolation phenomenon is serious in China. Preparation for the integration of new monitoring data for the overall research of reservoirs is insufficient. With respect to algorithms, data-based and model-based AI algorithms have been emerging recently, but the development of the overall proxy model for rapid prediction and automatic model calibration is still in the preliminary period. For application scenarios, relatively simple and independent applications related to geophysical interpretation and production engineering are continuing to emerge, while large-scale reservoir and field application require substantial investment in data acquisition, game-changing algorithms with cloud-based computing architecture, and top-down deployment.

Suggested Citation

  • Deli Jia & Jiqun Zhang & Yanchun Li & Li Wu & Meixia Qiao, 2023. "Recent Development of Smart Field Deployment for Mature Waterflood Reservoirs," Sustainability, MDPI, vol. 15(1), pages 1-22, January.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:1:p:784-:d:1022038
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    References listed on IDEAS

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    1. Turgay Ertekin & Qian Sun, 2019. "Artificial Intelligence Applications in Reservoir Engineering: A Status Check," Energies, MDPI, vol. 12(15), pages 1-22, July.
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