CAJ | 학술논문

针对传统储层流体识别方法识别精度低、运算量大、过于依赖个人经验的缺点，提出基于密度聚类的K近邻法，根据待测层段测井数据的空间分布规律，将样本按相对密度聚类成数据簇，并利用K近邻投票获得各簇所属类别。将该方法应用在某油田奥陶系鹰山组碳酸盐岩储层识别中。结果表明，较之其他常用识别方法，该算法识别精度高，泛化性和鲁棒性强，在处理大数据分类问题时具有明显优势，且在识别常规方法难以识别的油水同层时取得了较好的效果，具有良好的应用前景，为利用数据挖掘方法解决油田勘探开发中的复杂问题提供了新思路。
침대전통저층류체식별방법식별정도저、운산량대、과우의뢰개인경험적결점，제출기우밀도취류적K근린법，근거대측층단측정수거적공간분포규률，장양본안상대밀도취류성수거족，병이용K근린투표획득각족소속유별。장해방법응용재모유전오도계응산조탄산염암저층식별중。결과표명，교지기타상용식별방법，해산법식별정도고，범화성화로봉성강，재처리대수거분류문제시구유명현우세，차재식별상규방법난이식별적유수동층시취득료교호적효과，구유량호적응용전경，위이용수거알굴방법해결유전감탐개발중적복잡문제제공료신사로。
Reservoir fluid identification is an indispensable link in logging interpretation. In order to remove the defects of tra-ditional approaches, such as unsatisfying accuracy, excessive computation, undue dependence on personal experience, a density clustering based K-nearest neighbor method was proposed. According to the spatial distribution of the interval logging data under test, data clusters are formed based on relative density. And then with K-nearest neighbor voting method, the cat-egories of all clusters become available. Comparing with other commonly used identification methods, tested on the carbonate reservoir of Ordovician Yingshan Formation in an oil field, this approach shows a high accuracy, strong generalization and ro-bustness, as well as better effects on oil-water layer identification which is usually difficult for the compared methods. The method has a good application prospect and provides a new thought on solving complex problems in oilfield exploration and development with data mining methods.