Wang Yuxia, Guo Jingzhe, Ma Zhanrong, Zhou Lifa
Department of Geology, Northwest University, Xi'an, 710069, China.
Exploration Department, Changqing Oilfield, Xi'an, 710069, China.
Sci Rep. 2024 Oct 8;14(1):23466. doi: 10.1038/s41598-024-74611-1.
In recent years, industrial gas flow has been obtained from the bauxite gas reservoir in the southwestern Ordos Basin, which has made the identification of aluminium-bearing rock reservoirs a popular topic. To accelerate the exploration and development of this type of gas reservoir, major element testing, rock thin section identification and principal component analysis (PCA) were conducted, and a method for rapid and accurate identification of bauxite reservoirs via conventional logging was established. The test results clearly revealed the vertical stratification of major elements and three lithologies in the aluminium (Al)-bearing rock series in the study area. The log response characteristics of effective gas reservoirs were summarized, providing a basis for subsequent research on identifying effective bauxite reservoirs via mathematical dimensionality reduction of logging curves. The porosity comparison of strata with different lithologies suggests that dissolution pores are more developed in Al-rich layers, providing insight for identifying effective reservoirs by AlO content. On the basis of the above findings, a lithological identification chart of Al-bearing rock series was established via principal component analysis (PCA), and an effective bauxite reservoir logging identification model based on AlO content prediction was developed. The results show that using the dimensionality reduction method for principal component analysis of logging curves with overlapping information can avoid model distortion caused by multicollinearity. The research results can be used to identify bauxite reservoirs quickly and accurately without other test data.
近年来，鄂尔多斯盆地西南部铝土岩气藏实现了工业气流，使得含铝岩石储层的识别成为热门话题。为加快这类气藏的勘探开发，开展了主元素测试、岩石薄片鉴定和主成分分析（PCA），并建立了一种利用常规测井快速准确识别铝土岩储层的方法。测试结果清晰揭示了研究区含铝岩石系列主元素的垂向分层及三种岩性。总结了有效气藏的测井响应特征，为后续通过测井曲线数学降维识别有效铝土岩储层的研究提供了依据。不同岩性地层的孔隙度对比表明，富铝层溶蚀孔隙更发育，为通过AlO含量识别有效储层提供了思路。基于上述研究成果，通过主成分分析（PCA）建立了含铝岩石系列岩性识别图版，并建立了基于AlO含量预测的有效铝土岩储层测井识别模型。结果表明，利用降维方法对具有重叠信息的测井曲线进行主成分分析，可避免多重共线性导致的模型失真。研究成果可在无需其他测试资料的情况下快速准确识别铝土岩储层。
