A new lithologic classification method for tight sandstone reservoirs based on rock components and logging response characteristics. (29th November 2017)

• Record Type: Journal Article
• Authors: Zhou, Xueqing; Zhang, Zhansong; Zhang, Chong; Nie, Xin; Zhang, Chaomo; Zhu, Linqi
• Is Part Of: Journal of geophysics and engineering. Volume 14:Number 6(2017:Dec.)
• Page Start: 1599
• Page End: 1607
• Publication Date: 2017-11-29
• Subjects: rock components -- lithologic identification -- well logging -- tight sandstone reservoir; Geophysics -- Periodicals; Prospecting -- Geophysical methods -- Periodicals; Engineering -- Periodicals; 622.1505
• DOI: 10.1088/1742-2140/aa8eb5
• Languages: English
• ISSNs: 1742-2132

Abstract

Abstract: The original lithology classification method for tight sandstone reservoirs has a low prediction accuracy and it does not accurately reflect reservoir characteristics. We propose a new method that uses thin sections, logging curves, and core physical data to classify the lithology of the Ordos Basin, China. First, the relationship between the rock components and physical properties in the study area was analyzed and we found that quartz and rock debris played an active role in the properties of the reservoir, while feldspar minerals in the reservoir had a negative effect on it. Second, we synthesized the logging response characteristics of various rock components and divided the lithology into (1) high feldspar content, low quartz content, low rock debris content, and (2) low feldspar content, high quartz content, high rock debris content, using ((quartz + rock debris)/feldspar) as an index for lithologic classification. The lithology identification model was established using a support vector machine approach with a regression accuracy of 84.62%. Applying the model to the well in the study area to distinguish the lithology, results were in good agreement with thin section data, the physical properties of the reservoir, and the production capacity. Lithology can effectively reflect reservoir characteristics and play an important guiding role in the identification of reservoirs and the evaluation of productivity.