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Received：12 August 2020，
Published：28 October 2021
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识别储层岩性是开展地层对比和沉积展布等地质基础研究工作的重要前提。致密砂岩储层多为砂、泥岩薄互层，形成的岩性不仅种类较多，而且大部分岩性的测井响应也较为相似，为相关的岩性识别工作带来了困难。基于机器学习技术在模式识别上具有强大的分析性能，选用了稳定性好且计算效率高的XGBoost模型来解决致密砂岩储层岩性识别问题。该模型在建模过程中需要较多的经验参数参与，且计算速度随着自变量的增加而逐渐降低，为此提出了采用CRBM模型和PSO模型对其进行改进。CRBM模型具备数据提取功能，可从源数据中挖掘出更少但更利于建模的新自变量，而PSO模型可通过迭代计算确定XGBoost所有经验参数的最优值。以姬塬油田西部长4+5段部分取心井资料为基础，通过设计两个实验来验证提出的混合模型的预测能力。为加强验证效果，在实验中加入了PNN和SVM的优化模型进行对比。实验后发现所提出模型的预测准确率最高，均在90%以上。实验结果表明所提出的模型不仅能有效解决致密砂岩储层岩性识别问题，且较以往经典预测模型更具推广应用性。
Lithology identification is universally regarded as a prerequisite for some fundamental geological work such as stratigraphic correlation and analysis of sedimentary system distribution. The tight sandstone reservoirs are generally composed of multiple thin sandstone-mud alternating beds
featuring a variety of lithological types
most of which are similar in log responses
posing great difficulty for lithology identification. Given the powerful analytical performance of machine learning techniques shown on pattern recognition
XGBoost
a stable and highly efficient model is adopted to predict lithology of the tight sandstone reservoirs. However
this model needs many empirical parameters to complete its prediction
and its computing efficiency will be reduced when it deals with more independent variables. The CRBM and PSO models are
therefore
introduced to improve the prediction performance. CRBM is functional to extract features from original data so that fewer but more significant independent variables can be created by the CRBM in modeling. While PSO is effective to determine the optimal values via iterative computation for those empirical parameters used by XGBoost. Based on the data derived from some cored wells of Chang 4+5 Members in western Jiyuan oilfield
two experiments are designed to validate the prediction performance of the hybrid model proposed
CRBM-PSO-XGBoost. Furthermore
two optimized models formed by PNN and SVM are selected to make a comparison
as a way to consolidate the validation effect. The experiment results manifest that the prediction accuracy comes to peak by the proposed model
up to over 90%. In all
the hybrid model proposed in the study
capable of identifying lithology of tight sandstone reservoirs
is of wider application to lithology prediction compared with previous models.
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