基于地质力学数据的井下裂缝宽度智能预测方法研究与应用
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作者单位:

1.中海油研究总院有限责任公司;2.中国石油大学(北京)

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TE343

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工信部高技术船舶科研项目(CBG2N2142)


Study and Application of Intelligent Prediction Method for Underground Fracture Width Based on Geomechanical Data
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1.CNOOC Research Institute Company Limited;2.China University of Petroleum (Beijing)

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    摘要:

    准确预测井下裂缝宽度对于选择合适的堵漏工艺和材料至关重要,但现有基于物理机理的裂缝宽度解析解模型存在显著的局限性。本文分别基于CNN (convolutional neural network) 神经网络、LSTM ( long short-term memory) 神经网络、CNN-LSTM融合神经网络等算法,结合地质力学数据,构建了多个井下裂缝宽度预测模型。结果表明,CNN-LSTM模型在测试集上的预测效果最佳,预测稳定性最好,相关系数R2 值为0.967,MSE (Mean Squared Error) 值为0.0005。同时,有效预测了B-2井的150个样本点,预测准确率高达90%以上。利用CNN-LSTM模型预测了渤海油田B-5井的裂缝宽度,并优化了堵漏剂配方,成功提高了钻井现场堵漏成功率。这一应用表明,井下裂缝宽度智能预测模型能为工程师提供可靠的决策支持,确保堵漏工艺的有效性,从而提高现场一次堵漏成功率。

    Abstract:

    Accurately predicting the underground fracture width is crucial for selecting suitable plugging techniques and materials. However, existing analytical models based on physical mechanisms for fracture width have significant limitations. In this study, multiple underground fracture width prediction models were constructed using convolutional neural network (CNN), long short-term memory (LSTM) neural network, and CNN-LSTM fusion neural network algorithms, combined with geomechanical data. The results showed that the CNN-LSTM model had the best predictive performance on the test set, the correlation coefficient R2 is 0.967, and the Mean Squared Error (MSE) is 0.0005. Moreover, it successfully predicted 150 sample points from well B-2 with an accuracy rate of over 90%. By utilizing the CNN-LSTM model to predict fracture width and optimize the plugging agent formulation, the success rate of on-site plugging in well B-5 in the Bohai oilfield was significantly improved. This application demonstrates that fracture width prediction based on intelligent models can provide reliable decision support for engineers, ensuring the effectiveness of plugging techniques and enhancing the success rate of initial plugging on-site.

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蔡文军,丁建琦,李中,等. 基于地质力学数据的井下裂缝宽度智能预测方法研究与应用[J]. 科学技术与工程, , ():

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  • 收稿日期:2023-11-07
  • 最后修改日期:2024-05-20
  • 录用日期:2024-05-21
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