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- Title
基于极限梯度爬升算法与支持向量回归算法变权 组合模型致密油的采收率预测.
- Authors
张金水; 田冷; 黄诗慧; 董鹏举
- Abstract
Due to the characteristics of low permeability and productivity of tight oil reservoirs, hydraulic fracturing is widely applied to improve oil recovery. According to different geological and fracturing parameter changes, predicting the recovery rate after modification is instructive for fracturing modification. At present, the theoretical model of tight oil recovery after fracturing affected by multiple factors is difficult to accurately predict the change of oil reservoir recovery after fracturing in real-time according to the fracturing method and parameters. To further improve the prediction accuracy of tight oil recovery prediction, machine learning was introduced to make predictions, some improvements were made based on the extreme gradient boosting algorithm (XGBoost) and the support vector regression algorithm (SVR), a variable weight combination model XGBoost-SVR was obtained. The combined model can complement both single model’s advantages to avoid the range error caused by a single model parameter, and thus increasing the model prediction error tolerance rate. Firstly, factors affecting the recovery of tight oil were collected and sorted, and the relevant original data sets were established after analyzing the influence of geological factors, reservoir factors, and engineering factors on the recovery factor. Secondly, the preprocessed data sets were inputted into the SVR single model and the XGBoost single model for training separately, and the single model prediction value was obtained. Finally, an adaptive variable weight combination method based on residuals was used to establish the XGBoost-SVR combination model, which can obtain the final prediction results of each model, and clarify the factors affecting the recovery factor and the weight ratio of each factor. The prediction results show that compared with the SVR and XGBoost single models, the combined model has a prediction accuracy of 94. 63%, which reflects better adaptability.
- Publication
Science Technology & Engineering, 2022, Vol 22, Issue 12, p4778
- ISSN
1671-1815
- Publication type
Article