总有机碳(TOC)含量是评估烃源岩储层品质和生烃潜力的重要地球化学参数之一,其准确预测对页岩油气勘探开发具有重要意义.随着人工智能技术的快速发展,单一机器学习方法常被应用于TOC含量评价.然而,单一机器学习方法存在过拟合、欠拟合和目标函数局部最优等问题.集成模型被证实通过整合多个智能算法可以提高预测精度和稳定性能,其中组合策略是优化集成模型的关键之一.算术平均法作为组合策略难以充分发挥最佳模型的预测性能,而且容易受到预测误差较大的智能算法的影响.加权求和法作为组合策略根据训练数据确定加权系数,在训练集上表现出色,却在测试集中表现欠佳.本文提出了一种基于智能匹配技术的集成模型(IMTEM),采用极限梯度提升、随机森林、支持向量机和极限学习机作为算法模块对输入数据进行初步处理,提取的特征信息与原始测井响应共同输入到前馈神经网络层中进行非线性转换以及特征学习,从而对页岩TOC含量进行准确且连续的评价.将本文提出的方法应用于四川盆地龙马溪组页岩TOC含量预测,测试结果表明,相比于两种集成模型、5 种基础模型和ΔlogR方法,IMTEM的预测结果与岩心实测TOC含量一致性更高,更适用于页岩TOC含量的预测.

Total organic carbon(TOC)content is a crucial geochemical parameter for assessing reservoir quality and hydro-carbon generation potential of source rocks.The accurate prediction of TOC content is important for optimizing the exploration and development processes of shale oil and gas.With the rapid development of artificial intelligence technologies,individual machine learning algorithms have been increasingly applied to evaluate TOC content in shale.Despite the promising results of the individual machine learning algorithms,they are often subject to several challenges including overfitting,underfitting,and getting trapped in local optima of objective function.To address these limitations,the ensemble learning models are developed.Ensemble learning models leverage the strengths of multiple individual intelligent algorithms to enhance prediction accuracy and stability.Among them,combination strategy is one of the key factors in optimizing the ensemble learning models.Arithmetic average method as the simplest combination strategy fails to fully use prediction performance of the best individual intelligent model,and it can be severely affected by the individual intelligent model with a large prediction error,which can interfere with prediction outcome of overall model.In comparison,weighted summation method as a common combination strategy assigns the weights to different individual intelligent models according to their performance on training data.This method will perform excellently on training set,but it tends to have a poor performance when applied to test set.This paper develops an ensemble model based on an intelligent matching technology(IMTEM).The proposed method utilizes a set of robust intelligent algorithms including extreme gradient boosting,random forest,support vector machine,and extreme learning machine as algorithm modules to initially process input data.Then,the processed feature information combined with original log responses is fed to feedforward neural network layer for nonlinear transformation and feature learning,thereby enabling accurate and continuous estimation of TOC content in shale.To validate effectiveness of the IMTEM,the proposed method is applied to the prediction of TOC content in the Longmaxi Formation shale in the Sichuan Basin.Test results indicate that,compared to two ensemble models,five baseline models,and the ΔlogR method,predictions of the IMTEM exhibit higher consistency with measured TOC content.This demonstrates that the IMTEM is more suitable for predicting TOC content in shale.