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玛湖凹陷风城组岩石力学参数自适应权重组合预测

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准噶尔盆地玛湖凹陷风城组岩性复杂,为准确预测其岩石力学参数,提出了 一种自适应权重组合预测方法.首先分析、对比传统方法和不同机器学习算法(BP神经网络、XGBoost、支持向量机(SVM)、随机森林(RF)、卷积神经网络(CNN)、决策树(CART)、长短时记忆神经(LSTM)网络等)的预测效果,传统方法难以准确预测岩石力学参数,而不同机器学习算法的预测效果不同,其中抗压强度、抗张强度和脆性指数预测的最优机器学习算法模型为SVM,弹性模量为BP,泊松比为RF,内聚力为XGBoost,内摩擦角和断裂韧性为LSTM网络;单一机器学习算法难以实现对多个岩石力学参数的同步准确预测.在此基础上,通过对不同岩石力学参数选取不同预测基模型,再根据基模型预测效果赋予权重并进行组合,以开展自适应权重组合预测.结果表明,该方法能够有效提升机器学习算法的预测精度和泛化性能,可实现复杂岩性地层多个岩石力学参数的同步准确预测.
Adaptive weight combination forecast of rock mechanical parameters in the Fengcheng Formation of Mahu Sag
The lithology of the Fengcheng Formation in Mahu Sag,Junggar Basin is complex.To accurately predict its rock mechanical parameters,this paper proposes an adaptive weight combination forecast method.Firstly,the paper analyzes and compares the predictive performance of traditional methods and different ma-chine learning algorithms(BP neural network,XGBoost,support vector machine(SVM),random forest(RF),convolutional neural network(CNN),Classifation and regression tree(CART),long-short term memory neural(LSTM)network,etc.).Traditional methods are difficult to achieve accurate forecasts of rock mechanical pa-rameters,while different machine learning algorithms have different predictive effects.The optimal machine learning algorithm model for predicting compressive strength,tensile strength,and brittleness index is SVM.The optimal models for predicting elastic modulus,Poisson's ratio,and cohesion are BP,RF,and XGBoost,respectively.The optimal model for predicting internal friction angle and fracture toughness is LSTM network.A single machine learning algorithm is difficult to achieve synchronous and accurate forecasts of multiple rock mechanical parameters.On this basis,adaptive weight combination forecast is carried out by selecting different forecast base models for different rock mechanical parameters,assigning weights based on the forecast effect of the base models,and combining them.The results show that this method can effectively improve the forecast accuracy and generalization performance of machine learning algorithms and can achieve synchronous and ac-curate forecasts of multiple rock mechanical parameters in complex lithological formations.

rock mechanical parameterscomplex lithologic formationsmachine learningadaptive combination forecast

唐俊方、熊健、刘向君、甘仁忠、罗德江、梁利喜

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西南石油大学油气藏地质及开发工程国家重点实验室,四川成都 610500

中国地质大学(武汉)科技部地球深部钻探与深地资源开发国际联合研究中心,湖北武汉 430074

中国石油新疆油田公司,新疆克拉玛依 834000

成都理工大学数学地质四川省重点实验室,四川成都 610059

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岩石力学参数 复杂岩性地层 机器学习 自适应组合预测

国家自然科学基金项目中国石油科技创新基金项目数学地质四川省重点实验室2022年开放基金课题科技部地球深部钻探与深地资源开发国际联合研究中心开放课题

422721902023DQ02-0101scsxdz2022-05DE DRD-2023-03

2024

10.13810/j.cnki.issn.1000-7210.2024.01.001

石油地球物理勘探
东方地球物理勘探有限责任公司

石油地球物理勘探

CSTPCD北大核心
影响因子：1.766
ISSN：1000-7210
年,卷(期)：2024.59(1)
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