CAJ | 학술논문

沉积相研究是油气勘探开发中一项十分重要的任务.在桩241块初次利用神经网络模式识别法进行测井微相识别,将识别后的结果与岩心微相划分结果相对比,测井相的识别完全满足研究的需要,为密井网条件下沉积微相划分提供了一种新的思路和方法.采用神经网络判识沉积微相技术,可以提高沉积微相的分析和解释精度.
침적상연구시유기감탐개발중일항십분중요적임무.재장241괴초차이용신경망락모식식별법진행측정미상식별,장식별후적결과여암심미상화분결과상대비,측정상적식별완전만족연구적수요,위밀정망조건하침적미상화분제공료일충신적사로화방법.채용신경망락판식침적미상기술,가이제고침적미상적분석화해석정도.
Study on sedimentary facies is a main subject of the oil-gas exploration. Neural net discrimination of micro-sedimentary facies is first applied to block Z241. The neural net-discriminated results can be correlated with the well-logged micro-sedimentary facies and meet completely the need of the study thus provides a new thought and a technique for micro-sedimentary facies division under close exploration well net condition. The technique is playing an important role in improving efficiency and accuracy of analysis and interpretation of the micro-sedimentary facies.