基于机器学习的页岩气总有机碳含量预测模型
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TE348

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中国石油-西南石油大学创新联合体科技合作项目;“不同构型页岩储层流体流动规律及开发优化理论与方法”(编号:2020CX030202)


Prediction Model of Total Organic Carbon Content in Shale Gas
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    摘要:

    总有机碳含量是评价页岩气的重要指标之一,其对页岩气藏地质“甜点”的准确预测和后期开采过程中射孔井段和压裂井段的选择至关重要。针对现有的页岩气藏总有机碳含量预测方法中,△logR方法人为确定参数多、主观性强,多元回归法泛化能力弱等缺点,本文以川南海相页岩气为研究对象,通过对研究区块测井资料和实验室岩心分析结果的整理,优选出不同的参数作为模型训练的特征向量,建立总有机碳含量的BP神经网络和支持向量机预测模型,分析不同模型之间的差异,对模型特征组合等影响因素进行分析,最后用优选出的模型对未参与训练的井进行总有机碳含量的预测并与实测值对比,得到基于不含能谱测井资料的BP神经网络模型能真实地反映出测井资料与储层的非线性关系,预测结果与实际结果之间的误差小。

    Abstract:

    The total organic carbon content is one of the important indicators for evaluating shale gas, and it is crucial to the accurate prediction of the geological "sweet spot" of shale gas reservoirs and the selection of perforated and fractured wells in the later production process. In view of the shortcomings of the existing total organic carbon content prediction methods for shale gas reservoirs, method △logR has many artificial parameters, strong subjectivity, and multiple regression analysis method has the disadvantage of weak generalization ability. This paper took the marine shale gas in southern Sichuan as the research object, selected different parameters as the eigenvectors of model training by sorting out the logging data and laboratory core analysis results in the research block, established the BP neural network and support vector machine prediction models of the total organic carbon content, analyzed the differences between different models and the influencing factors such as the combination of model features. Finally, we used the selected model to predict the total organic carbon content of the wells that did not participate in the training, compared with the measured values. It is concluded that the BP neural network model without energy spectrum logging data can truly reflect the nonlinear relationship between the logging data and the reservoir, and the error between the predicted results and the actual results is small.

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魏明强,周金鑫,段永刚,等. 基于机器学习的页岩气总有机碳含量预测模型[J]. 科学技术与工程, 2023, 23(30): 12917-12925.
Wei Mingqiang, Zhou Jinxin, Duan Yonggang, et al. Prediction Model of Total Organic Carbon Content in Shale Gas[J]. Science Technology and Engineering,2023,23(30):12917-12925.

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  • 收稿日期:2022-12-15
  • 最后修改日期:2023-07-30
  • 录用日期:2023-03-23
  • 在线发布日期: 2023-11-15
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