Effectiveness and sensitivity analysis of solution gas re-injection in Baikouquan tight formation, Mahu sag for enhanced oil recovery

Abstract

To address the fast productivity decline of the horizontal wells and low oil recovery during natural depletion in Baikouquan formation, the approach of solution gas re-injection was proposed with the primary objective of further developing this formation. Herein, a field-scale numerical compositional reservoir model was built up based on the formation properties and then the effects of permeability, fractures and formation stress on the production dynamics were thoroughly investigated. A sensitivity analysis, which can correlate the oil recovery with these parameters, was also performed. The results showed that the re-injection of solution gas could remarkably retard the production depletion of the horizontal wells thereby improving the oil production. The oil recovery rate increased with permeability, fracture half-length, fracture conductivity, and formation dip. With regard to the fracture distribution, it was found that the interlaced fracture outperformed the aligned fracture for the solution gas re-injection. The influence of the formation stress should be carefully considered in the production process. Sensitivity analysis indicated that the formation dip was the paramount parameter, and the permeability, fracture half-length, and fracture conductivity also played central roles. The results of this study supplement earlier observations and provide constructive envision for enhanced oil recovery of tight reservoirs.

Keywords

Tight oil reservoir / Mahu sag / Solution gas re-injection / Numerical simulation / Fracturing / Sensitivity analysis

Acknowledgements

The authors gratefully acknowledge the financial support of the National Natural Science Foundation of China (51974265 and 51804264), Science Foundation Shanxi Province, China (201701D121129), Science Foundation of Shanxi Institute of Energy (ZY-2017001), and Youth Science and Technology Innovation Team of SWPU (2017CXTD04). The authors also thank the Computer Modeling Group Ltd. for providing the CMG software for this study. The authors would like to thank the anonymous reviewers for valuable comments and suggestions.

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