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16 August 2023 Research on prediction method of oilfield water drive formation pressure based on blending integrated model
Shaohua Zhou, Xueyi Zhang, Yu Li
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Proceedings Volume 12787, Sixth International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2023); 127870V (2023) https://doi.org/10.1117/12.3004954
Event: 6th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE 2023), 2023, Shenyang, China
Abstract
In oil drilling, the direction of oil and gas migration and the characteristics of lithological changes can be determined based on changes in formation pressure. Therefore, accurate and effective prediction of formation pressure is a prerequisite for achieving high-quality, efficient, and safe drilling. In response to the problems of high labor cost, low timeliness, and insufficient prediction accuracy in traditional methods for multi well or large area measurement, this paper proposes an oilfield water drive formation pressure prediction model based on Blending integrated model. Four models of random forest, Adaboost, XGBoost and LightGBM are selected as the base model, and the three-layer BP neural network is selected as the meta model. Compare the model in this paper with the base model separately. After comparison, the accuracy of the Blending integrated model proposed in this paper on the water injection wells test set is 94.32%, and the accuracy on the oil production wells test set is 93.87%. This model has the best performance in predicting the formation pressure of oilfield water drive, effectively improving the accuracy of predicting the formation pressure of oilfield water drive.
(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Shaohua Zhou, Xueyi Zhang, and Yu Li "Research on prediction method of oilfield water drive formation pressure based on blending integrated model", Proc. SPIE 12787, Sixth International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2023), 127870V (16 August 2023); https://doi.org/10.1117/12.3004954
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KEYWORDS
Data modeling
Integrated modeling
Education and training
Random forests
Neural networks
Correlation coefficients
Machine learning
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