Chinese Journal of Computational Physics ›› 2024, Vol. 41 ›› Issue (4): 503-514.DOI: 10.19596/j.cnki.1001-246x.8745

Previous Articles     Next Articles

A Production Prediction Model for Fractured Horizontal Wells with Irregular Fracture Network in Low Permeability Reservoirs

Siyu LIU(), Kun WANG(), Mingying XIE, Shasha FENG, Li LI, Yang GAO   

  1. China National Offshore Oil Corporation (China) Limited, Shenzhen Branch, Shenzhen, Guangdong 518067, China
  • Received:2023-04-14 Online:2024-07-25 Published:2024-08-24
  • Contact: Kun WANG

Abstract:

A non-uniform complex fracture network structure formed by fracturing is considered with introducing the fractal theory and combining the stress sensitivity of fracture system to demonstrate the change of fluid flow capacity, and a production prediction model for three-zone compound seepage flow in fractured horizontal wells in low permeability reservoirs is established successfully in this paper. Laplace transform, perturbation theory, and numerical inversion are applied to obtain the analytical solution of the proposed production model, and the productivity formulas for single well under two situations of uniform and non-uniform distribution of fractures in horizontal wells are derived. The reliability of the production prediction model is verified with the real production data and basic parameters of a fractured horizontal well in Bohai oilfield, and the effects of related influential parameters on production of horizontal well are analyzed. The research results show that smaller the stress sensitivity coefficient and threshold pressure gradient results in larger the oil production. Larger the fractal dimension contributes to larger the horizontal well production. Additionally, the oil production increases with the increase of the number of fractures and fracture half-length, but the growth rate slows down, which means these parameters have optimal values.

Key words: low permeability reservoirs, fractured horizontal wells, fracture network, fractal, production prediction

CLC Number: