大庆徐家围子地区深部致密砂砾岩气层识别

THE RECOGNITION OF GAS-BEARING TIGHT SANDSTONE/GRAVEL RESERVOIRS AT THE DEEP OF XUJIAWEIZI IN DAQING

大庆徐家围子地区深部致密砂砾岩储层的骨架成分除了含有石英、长石外,还含有许多酸性喷发岩,而且各种骨架成分的含量随深度变化比较大;复杂的岩性使得气的影响被岩性的影响所淹没,从而造成常规气层识别方法失效。针对这一问题,应用神经网络方法准确求取地层颗粒密度和泥质含量,然后对中子和密度测井值进行岩性影响校正和泥质影响校正,使得求出的中子和密度孔隙度仅受地层气的影响,最后将得到的中子和密度孔隙度进行对比,应用中子和密度孔隙度的差异识别气层。

The matrix lithology of tight sandstone/gravel reservoirs at the deep of Xujiaweizi in Daqing is complex, which includes not only quartz and feldspar but also many fragments of acid eruptive rock. The lithologic composition varies with depth. So the influence of gas is submerged by the influence of complex lithology in log data, which leads to the failure to recognize gas reservoirs by using conventional methods. In order to solve the problem, matrix density and shale content are accurately obtained with neural network. And the density porosity and neutron porosity obtained after lithology and shale correction are only influenced by gas. Thus the difference between density porosity and neutron porosity can be used to recognize gas reservoirs.