基于XGBoost与SVR变权组合模型致密油的采收率预测
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TE331

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国家自然科学基金(51974329)


Tight oil recovery prediction based on XGBoost and SVR variable weight combination model
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National Natural Science Foundation of China(51974329)

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    摘要:

    致密油储层因具有渗透率与产能低下的特点,多采用大型水力压裂改造储层来提高采收率,根据不同的地质、压裂参数变化,预测改造后的采收率对于压裂施工改造有良好的指导作用。目前多因素影响的致密油压裂后采收率预测理论模型,难以实时准确地根据压裂方式及参数来预测压裂后油藏采收率变化。为进一步提升致密油的采收率预测精确度,本文引进机器学习进行预测,基于极限梯度爬升算法(XGBoost)和支持向量回归算法(SVR)进行了一定改进得到变权组合模型XGBoost-SVR,模型借鉴残差进化机制,实现加权融合系数的最优组合,该组合模型可对两种单模型进行优势互补,避免了因单一模型参数导致的范围性误差,增大模型预测容错率。本文首先对致密油的采收率影响因素进行收集整理,分析地质因素、储层因素和工程因素对采收率的影响,构造相关原始数据集;其次将预处理后数据集输入SVR单模型和XGBoost单模型分别进行训练,得出单模型预测值;最后采用基于残差的自适应的变权组合方法建立XGBoost-SVR组合模型,得到各模型最终预测结果,明确采收率影响因素及各影响因素权重比。模型预测结果表明:与SVR和XGBoost单模型相比,组合模型在预测精度达到94.63%,表现出更好的适应性。

    Abstract:

    Due to the characteristics of low permeability and productivity of tight oil reservoirs, hydraulic fracturing has been 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 is introduced to make predictions and has certain improvements have been made based on the eXtreme Gradient Boosting Algorithm (XGBoost) and the Support Vector Regression Algorithm (SVR) to obtain the variable weight combination model XGBoost-SVR. 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 are collected and sorted, and the relevant original data sets have been established after analyzing the influence of geological factors, reservoir factors, and engineering factors on the recovery factor; secondly, the preprocessed data sets are inputted into the SVR single model and the XGBoost single model for training separately, and the single model prediction value is obtained; finally, an adaptive variable weight combination method based on residuals is 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. It is shown from the prediction results that compared with the SVR and XGBoost single models, the combined model has a prediction accuracy of 94.63%, which reflects better adaptability.

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张金水,田冷,黄诗慧,等. 基于XGBoost与SVR变权组合模型致密油的采收率预测[J]. 科学技术与工程, 2022, 22(12): 4778-4787.
Zhang Jinshui, Tian Leng, Huang Shihui, et al. Tight oil recovery prediction based on XGBoost and SVR variable weight combination model[J]. Science Technology and Engineering,2022,22(12):4778-4787.

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  • 收稿日期:2021-08-11
  • 最后修改日期:2022-04-04
  • 录用日期:2021-12-03
  • 在线发布日期: 2022-05-07
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