基于图论聚类和最小临近算法的岩性识别方法——以四川盆地西部雷口坡组碳酸盐岩储层为例  

A lithology recognition method based on multi-resolution graph-based clustering and K-Nearest Neighbor:A case study from the Leikoupo Formation carbonate reservoirs in western Sichuan Basin

作　　者：孔强夫 杨才 李浩[1] 耿超 邓健 Kong Qiangfu;Yang Cai;Li Hao;Geng Chao;Deng Jian(Petroleum Exploration&Production Research Institute,SINOPEC,Beijing 100083,China;International Well Logging of Great-wall Drilling Company,CNPC,Beijing 100083,China;Shu’nan Gas Field of Southwest Oil&Gas Field Company,PetroChina,Luzhou,Sichuan 646000,China;Huabei Oilfield Company,PetroChina,Renqiu,Hebei 062552,China)

机构地区：[1]中国石化石油勘探开发研究院,北京100083 [2]中国石油集团长城钻探工程有限公司国际测井公司,北京100083 [3]中国石油西南油气田分公司蜀南气矿,四川泸州646000 [4]中国石油华北油田公司勘探开发研究院,河北任丘062552

出　　处：《石油与天然气地质》2020年第4期884-890,共7页Oil & Gas Geology

基　　金：中国科学院A类战略性先导科技专项(XDA14010204);国家自然科学基金项目(41902149)。

摘　　要：碳酸盐岩具有非均质性强、岩性变化快和岩石类型复杂的特征,岩性精细识别难度大,严重制约了储层参数的计算及后续油气开发。以四川盆地西部雷口坡组碳酸盐岩储层为例,结合岩心和薄片等分析测试资料将储层发育的岩性分为8类:藻粘结白云岩、粉晶白云岩、泥晶白云岩、灰质白云岩、白云质灰岩、灰岩、膏质白云岩和石膏,明确了不同岩性的测井响应特征。采用机器学习的思想,将已知岩性定名样本作为训练数据,利用图论聚类分析方法建立岩性识别训练模型,在此基础上结合最小临近算法对未取心井岩性进行预测,实现了不同岩性的精细识别。区块应用结果表明:该方法岩性识别整体符合率高达91.3%,有效提高了岩性识别精度。Carbonate rocks have the characteristics of strong heterogeneity, changing lithology and various rock types, which make it difficult to recognize their fine lithologic features and seriously restrict the calculation of reservoir parameters as well as subsequent oil/gas development. The carbonate reservoirs in the Leikoupo Formation in western Sichuan Basin were studied to deal with the problem. Core and thin slice observation and other analysis results revealed eight distinctive lithologic facies in the reservoirs: the algal bonded dolomite, crystal powder dolomite, dolomicrite, calcite dolomite, dolomitic limestone, limestone, gypsum dolomite and gypsum. Their log responses were also identified. In addition, machine learning was combined with multi-resolution graph-based clustering to establish a lithology identification training model by using the known and named lithologic samples as training data. Subsequently, the lithology of reservoirs in other wells was predicted with the K-Nearest Neighbor, thus realizing a fine identification of different lithologic facies. Field application of the method shows a 91.3% of overall coincidence rate of lithology recognition, indicating an improved accuracy in lithology identification.

关 键 词：测井响应 机器学习 图论聚类 最小临近算法 盲井预测 岩性识别 碳酸盐岩 四川盆地西部 

分 类 号：TE135.1[石油与天然气工程—油气勘探]