基于Adaboost-WOA-BP模型的总有机碳含量预测方法
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长江大学地球物理与石油资源学院

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P631

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国家科技重大专项下属任务(2016ZX05002-004-009)


The prediction method of total organic carbon content based on Adaboost-WOA-BP model
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College of Geophysics and Petroleum Resources,Yangtze UniversityWuhan

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    摘要:

    页岩储层总有机碳含量(TOC,Total Organic Carbon)是页岩生烃潜力及页岩气富集程度页的重要参数,其精确预测对油气勘探开发具有重要意义。常规的线性回归方法的受到地区以及测井资料之间复杂的非线性关系的影响,存在预测精度有限的问题。为此提出一种Adaboost-WOA-BP预测模型来进行TOC含量的预测,将WOA(Whale Optimization Algorithm)算法优化过的BP(Backpropagation)神经网络作为Adaboost(Adaptive Boosting)算法的弱学习器,集成多个弱学习器进而构建一个强的学习器。优选自然伽马,密度,声波时差等与计算TOC含量相关的敏感测井参数作为预测模型的输入,通过与常规线性回归算法、BP神经网络WOA-BP神经网络这三种方法进行对比,Adaboost-WOA-BP模型具有更高的TOC含量预测精度,预测TOC与实测TOC符合率达到95%。

    Abstract:

    The total organic carbon content in shale reservoirs is a crucial parameter for assessing hydrocarbon generation potential and shale gas enrichment. Accurate prediction of TOC is essential for oil and gas exploration and development. Conventional linear regression methods are limited in their predictive accuracy due to the complex nonlinear relationships among regional and well logging data. To address this issue, we propose an Adaboost-WOA-BP prediction model for TOC content. This model integrates Whale Optimization Algorithm optimized Backpropagation neural networks as weak learners within the Adaboost framework to construct a strong learner. We select sensitive logging parameters such as natural gamma, density, and sonic travel time as inputs for the prediction model. Compared to conventional linear regression, BP neural networks, and WOA-BP neural networks, the Adaboost-WOA-BP model demonstrates higher predictive accuracy, achieving a 95% match between predicted and measured TOC values.

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陈甄明,谢锐杰,彭宏昶,等. 基于Adaboost-WOA-BP模型的总有机碳含量预测方法[J]. 科学技术与工程, , ():

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  • 收稿日期:2024-04-19
  • 最后修改日期:2024-04-30
  • 录用日期:2024-05-22
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