Optimization of fracturing parameters for tight oil production based on genetic algorithm

Abstract

It is difficult to determine the main controlling factors of tight oil production. In addition to the problem of uncontrollable prediction accuracy, the numerical prediction model established by the main controlling factors will also make the correctly predicted low production samples lose the value of development. Applying the optimization algorithm with fast convergence speed and global optimization to optimize the controllable parameters in the high-precision numerical prediction model can effectively improve the productivity of low production wells with timeliness, and bring greater economic value while saving development cost. Using PCA-GRA method, the sample weight and the weighted correlation ranking results of parameters affecting tight oil production were obtained. Thereupon then the main controlling factors of tight oil production were determined. Then we set up a BP neural network model with by taking the main controlling factors as input and tight oil production as output. The prediction effect of the network was good and can be put into use. The results of sensitivity analysis showed that the network was stable, and the total fracturing fluid volume had the greatest impact on the production of tight oil. Finally, by using genetic algorithm, we optimized the fracturing parameters of all low production well samples in the field data. Combined with the fracturing parameters of all high production well samples and the optimized fracturing parameters of low production wells, the optimal interval of fracturing parameters was given, which can provide guidance for the field fracturing operation.

Keywords

PCA-GRA method / Main controlling factors / BP neural network / Genetic algorithm / Optimization of fracturing parameters

Declaration of competing interests

The authors confirm that this article content has no conflict of interest.

Acknowledgements

The authors gratefully acknowledge the financial support of the National Science and Technology Major Projects of China (2016ZX05065 and 2016ZX05042-003).

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