论文部分内容阅读
为研究柴达木盆地E区大型背斜构造沉积相及砂体展布规律,在二维地震数据闭合差校正、邻区井标定引入及精细层位解释基础上,利用改进算法的Kohonen神经网络技术开展二维地震相划分研究,识别出三角洲前缘水下分流河道、分流间湾及滩坝等微相。本文研究认为,研究区古流向为南东—北西向,储集砂体较发育,主要富集于研究区中部,现今构造东高点位于有利沉积相带。改进算法的Kohonen神经网络二维地震相划分技术补充了沉积相研究成果,适合于西部二维地震资料覆盖的风险探区,具较强的推广价值。
In order to study the distribution of sedimentary facies and sand body in large anticline tectonic zone E in Qaidam Basin, based on the correction of two-dimensional seismic data, the introduction of well calibration and the interpretation of fine horizon, the improved algorithm Kohonen neural network Technology to carry out research on the division of two-dimensional seismic facies and identify microfacies such as delta front distributary channel, shunt bay and beach dam. This study suggests that the paleocurrent in the study area is in the south-east-north-west direction, and the reservoir sand bodies are well developed, and are mainly concentrated in the central part of the study area. The present-day high tectonics is located in the favorable sedimentary facies belt. The improved Kohonen neural network two-dimensional seismic facies division technique complements the research results of sedimentary facies and is suitable for the risk exploration area covered by the two-dimensional seismic data in the west, which has a strong promotion value.