Multi-fractured horizontal wells (MFHWs) are the most widely used technology for producing tight oil and gas reservoirs. Production data from a MFHW may exhibit multiple linear flow periods including linear flow within the fracture, linear flow in the stimulated reservoir volume (SRV), and linear flow in the unstimulated region of the reservoir. This study focuses on an SRV containing infinite-conductivity hydraulic fractures and no fluid flow contribution from the unstimulated region. The existing analytical models for these flow periods have been developed based on the linearized form of the flow equation. However, these models introduce considerable errors in permeability estimation and production forecasts for tight oil reservoirs if they do not account for stress-sensitivity.

In previous work by the authors, the stress-sensitivity of permeability was incorporated into rate transient analysis (RTA) of tight oil reservoirs during transient flow period for wells containing a single hydraulic fracture. In this paper, the effects of stress-dependent formation permeability on the production data of MFHWs are studied. A new model is used to correct the conventional RTA techniques for these effects to improve permeability estimation and oil production forecasting.

This study shows that the conventional methods that do not account for stress sensitivity give less accurate results for MFHWs producing under a high pressure drawdown. The results show that the new method reduces the error of the conventional techniques significantly and provides a reliable strategy for RTA of MFHWs.

This study fulfills two important requirements of the tools for RTA of MFHWs; simplicity and accuracy. The strategy is to keep the conventional analysis routine unchanged, with a correction factor applied to account for the effects of the stress-sensitivity of permeability. The value of the correction factor is that it shows how far the conventional analytical methods are from the exact solutions. Further, the correction factor is used to remove the considerable error in conventional analyses.

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