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Geological–Engineering Synergistic Optimization of CO 2 Flooding Well Patterns for Sweet Spot Development in Tight Oil Reservoirs

Author

Listed:
  • Enhui Pei

    (Key Laboratory of Enhanced Oil and Gas Recovery of Ministry of Education, Northeast Petroleum University, Daqing 163000, China)

  • Chao Xu

    (Key Laboratory of Enhanced Oil and Gas Recovery of Ministry of Education, Northeast Petroleum University, Daqing 163000, China)

  • Chunsheng Wang

    (Key Laboratory of Enhanced Oil and Gas Recovery of Ministry of Education, Northeast Petroleum University, Daqing 163000, China)

Abstract

CO 2 flooding technology has been established as a key technique that is both economically viable and environmentally sustainable, achieving enhanced oil recovery (EOR) while advancing CCUS objectives. This study addresses the challenge of optimizing CO 2 flooding well patterns in tight oil reservoirs through a geological–engineering integrated approach. A semi-analytical model incorporating startup pressure gradients and miscible/immiscible two-phase flow was developed to dynamically adjust injection intensity. An effective driving coefficient model considering reservoir heterogeneity and fracture orientation was proposed to determine well pattern boundaries. Field data from Blocks A and B were used to validate the models, with the results indicating optimal injection intensities of 0.39 t/d/m and 0.63 t/d/m, respectively. Numerical simulations confirmed that inverted five-spot patterns with well spacings of 240 m (Block A) and 260 m (Block B) achieved the highest incremental oil production (3621.6 t/well and 4213.1 t/well) while reducing the gas channeling risk by 35–47%. The proposed methodology provides a robust framework for enhancing recovery efficiency in low-permeability reservoirs under varying geological conditions.

Suggested Citation

  • Enhui Pei & Chao Xu & Chunsheng Wang, 2025. "Geological–Engineering Synergistic Optimization of CO 2 Flooding Well Patterns for Sweet Spot Development in Tight Oil Reservoirs," Sustainability, MDPI, vol. 17(11), pages 1-23, May.
  • Handle: RePEc:gam:jsusta:v:17:y:2025:i:11:p:4751-:d:1661521
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