Fracture plugging optimization for drill-in fluid loss control and formation damage prevention in fractured tight reservoir

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2016

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Xu, C.
Kang, Y.
Chen, F.
You, Z.

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Journal of Natural Gas Science and Engineering, 2016; 35(Part A):1216-1227

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Chengyuan Xu, Yili Kang, Fei Chen, Zhenjiang You

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Abstract

Well-developed natural fractures are beneficial for the economic and efficient development of tight reservoirs. However, they also lead to drill-in fluid loss and induced severe formation damage. Fracture plugging with loss control material (LCM) is the most common way to control lost circulation. Fracture plugging effect largely depends on the fracture propagation pressure, because plugging failure is mainly caused by fracture propagation in fractured formation. Nevertheless, the effects of the plugging parameters on the fracture propagation pressure are still unclear. The current paper develops a mathematical model for fracture propagation pressure accounting for fracture plugging. Key indexes are proposed for fracture plugging optimization based on parameter analysis. Laboratory experiments are conducted to select reasonable LCM type and concentration. The application procedure of the proposed model to drill-in fluid loss control is presented and successfully applied to field case study. The modelling results show that the plugging zone length, width and permeability are the major plugging parameters that affect the fracture propagation pressure. The larger the plugging zone width and the smaller the plugging zone length and permeability, the higher the fracture propagation pressure. Maximum plugging pressure, total loss volume before sealing and D90 degradation rate are proposed as the three indexes for LCM selection. Experimental results show that the combination of rigid granule, fiber and elastic particle can create a synergistic effect to optimize the fracture plugging effect. For the 500 μm width fracture, the optimal concentrations for rigid granule, fiber and elastic particle are 5.0%, 1.5% and 2.5%, respectively.

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© 2016 Elsevier B.V. All rights reserved.

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