An Identified Method for Lacustrine Shale Gas Reservoir Lithofacies Using Logs: A Case Study for No. 7 Section in Yanchang Formation in Ordos Basin
Hongyan Yu1, 2, *, Zhenliang Wang1, Hao Cheng1, Qianqian Yin1, Bojiang Fan4, Xiaoyan Qin1, Xiaorong Luo3, Xiangzeng Wang4, Lixia Zhang4
Identifiers and Pagination:Year: 2015
First Page: 297
Last Page: 307
Publisher Id: TOPEJ-8-297
Article History:Received Date: 18/10/2014
Revision Received Date: 15/1/2015
Acceptance Date: 23/6/2015
Electronic publication date: 19/8/2015
Collection year: 2015
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Unconventional reservoirs are keys to oil and gas exploration and development, especially shale gas reservoirs. Discriminated shale gas reservoir lithofacies are, in particular, a primary problem in shale gas reservoir engineering. The mineral composition will affect both absorbed and free gas contents, therefore their identification is important. The mineral composition is one part of lithofacies. The shale content has always been used in previous lithological identifications: this method is effective in sand reservoirs; however, it is not suitable for use in shale gas reservoirs. This paper takes No.7 section in Yanchang formation in Ordos basin as an example. Through a lithological analysis, it was concluded that overlap method and cross-plot method are not also inappropriate for shale gas reservoirs. The Ordos basin shale gas reservoir is divided into seven lithofacies. We form a mathematical method and apply it to shale gas reservoirs using the shale volume and ΔlgR which are available from conventional well logging and reflect organic matter in the processed dataset. Decision tree is used here. However, there were too many parameters to discriminate all lithofacies precisely. Principal component analysis (PCA) is a technique used to reduce multidimensional data sets to lower dimensions for analysis. This technique can be useful in petro-physics and geology as a preliminary method of combining multiple logs into a single entity or two logs without losing information. Combining PCA and a decision tree algorithm, the lithofacies of a shale gas reservoir were accurately discriminated.