神经网络技术在测井相分析及水淹层判别中的应用

APPLICATION OF NEURAL NETWORK TO ANALYZING LOGGING FACIES AND IDENTIFYING FLOODING LAYERS

以测井相分析和常规测井资料定性识别水淹层的理论为基础，运用神经网络技术，对勘探阶段的探井进行了沉积相识别，并对油田开发阶段的水淹层进行了级别划分．对长庆、大港等油田的４口探井进行了测井相分析，并对２０多口开发井的单井进行了评价．结果表明，神经网络技术可以有效地应用于油田勘探开发测井中．

Based on the theories of logging facies analysis and qualitative methods for flooding layers recognition by logging data, neural network technique has been introduced both in explorating area to identify sedimentary facies and in developing fields to distinguish flooding layers from oil bearings, as well as to determine the flooding levels. In the research, about 4 explorating wells were analyzed with logging faccies and other almost 30 developing wells were evaluated by this technique. All results show that the neural network technique is effective to deal with some problems meeting in explorating and developing stages of oilfield.