岩性识别是储层测井评价的重要环节,储层发育程度和孔隙度等参数的评价精度很大程度上取决于岩性识别的准确率.然而南海北部珠江口盆地惠州26-6井区的火成岩岩性复杂,普遍存在的蚀变现象对常规测井数据产生了很大影响,令常规测井岩性识别更加困难,识别精度难以满足勘探需求.为了提高蚀变火成岩地层的岩性识别准确率,本文结合常规测井和元素录井数据,建立了基于不同机器学习算法的岩性识别方法并进行对比分析,得到了适用于确定蚀变火成岩岩性的综合识别方法.首先利用岩心元素数据建立录井元素校正方法,得到可靠的元素录井数据,并以常规测井的采样间隔为标准对标准化元素录井数据进行线性插值;之后优选出与岩性相关性更高的常规测井和元素录井曲线,分别采用k近邻(KNN)和支持向量机(SVM)两种机器学习算法对研究区的构造片岩、闪长岩、蚀变辉绿岩、花岗闪长岩、花岗岩和蚀变花岗岩等6种火成岩进行岩性识别.在研究区内4 口有岩石薄片鉴定资料井的 目标层中,按照对应深度提取数据点(共145个),其中80%作为训练样本,其余20%作为测试样本.以样本测试精度和全井岩性识别效果作为评价指标,对两种算法进行对比,结果表明:KNN和SVM算法的识别准确率均为92.65%,但是KNN算法全井识别效果更符合地层岩性分布特征,说明基于KNN算法的测、录井综合岩性识别更适用于研究区.
Lithology recognition plays an important role in reservoir logging evaluation,influencing the accuracy of critical parameters such as development degree and porosity.In Huizhou 26-6 well block within the Pearl River Mouth basin in the northern South China Sea,the lithology of igneous rocks is intricate,with widespread alteration significantly impacting conventional logging data.As a result,the conventional lithology identification faces difficulty in satisfying the exploration needs.To enhance the accuracy of identifying altered igneous rocks,we integrate conventional logging and element cutting logging to establish lithology identification methods through diverse machine learning algorithms.A comparative analysis leads to a comprehensive identification method of discerning altered igneous rocks.Initially,a core element data-based correction method for element cutting logging is established to obtain reliable data.Subsequently,the k-nearest neighbor(KNN)method and the support vector machine(SVM)method are employed to identify the lithology of six igneous rocks in the study area—diorite,tectonic schist,altered diabase,granodiorite,altered granite,and granite.In the target layer of four wells with rock slice identification data in Huizhou 26-6 well block,data points(145 in total)are extracted according to the corresponding depth,of which 80%are used as training samples and the remaining 20%as test samples.Taking sample test accuracy and whole well lithology recognition effect as evaluation indicators,the results of comparing the two algorithms indicate that the recognition accuracy of KNN and SVM algorithms is both 92.65%,but the whole well recognition effect of KNN algorithm is more in line with the distribution characteristics of stratigraphic lithology,indicating that the comprehensive lithology recognition based on KNN algorithm is more suitable for the study area.