Prediction of Refracturing Effect of Tight Gas Reservoirs Based on Bayesian Inversion Algorithm

Area of Science:

• Petroleum Engineering
• Geoscience
• Reservoir Engineering

Background:

• Refracturing is vital for maintaining and restoring tight gas well productivity, as initial hydraulic fractures can degrade over time.
• Optimizing refracturing timing and parameters is essential for maximizing economic benefits in tight gas field development.
• Existing methods lack sufficient guidance for effective refracturing strategies, necessitating improved predictive models.

Purpose of the Study:

• To develop an inversion method for determining pre-refracturing formation and fracture parameters.
• To provide theoretical and technical guidance for optimizing refracturing operations in tight gas wells.
• To validate the proposed method using field data from the Sulige gas field.

Main Methods:

• Utilizing an inverse problem approach based on a dynamic productivity prediction model for tight gas wells.
• Applying a Bayesian inversion algorithm to estimate formation and fracture parameters prior to refracturing.
• Conducting field applications including well selection, timing determination, and effect prediction.

Main Results:

• The proposed Bayesian inversion method successfully estimates pre-refracturing parameters.
• Field application in the Sulige gas field demonstrated effective well selection and timing optimization.
• Predicted refracturing effects were validated against actual production data, showing significant productivity enhancement.

Conclusions:

• The developed inversion method provides a robust framework for optimizing refracturing strategies in tight gas wells.
• Accurate parameter estimation and timing optimization are critical for successful refracturing operations.
• This approach offers valuable theoretical guidance for high-efficiency, production-increasing refracturing construction.