Prediction of natural fracture in shale oil reservoir based on R/S analysis and conventional logs

Abstract

Investigation into natural fractures is extremely important for the exploration and development of low-permeability reservoirs. Previous studies have proven that abundant oil resources are present in the Upper Triassic Yanchang Formation Chang 7 oil-bearing layer of the Ordos Basin, which are accumulated in typical low-permeability shale reservoirs. Natural fractures are important storage spaces and flow pathways for shale oil. In this study, characteristics of natural fractures in the Chang 7 oil-bearing layer are first analyzed. The results indicate that most fractures are shear fractures in the Heshui region, which are characterized by high-angle, unfilled, and ENE-WSW-trending strike. Subsequently, natural fracture distributions in the Yanchang Formation Chang 7 oil-bearing layer of the study area are predicted based on the R/S analysis approach. Logs of AC, CAL, ILD, LL8, and DEN are selected and used for fracture prediction in this study, and the R(n)/S(n) curves of each log are calculated. The quadratic derivatives are calculated to identify the concave points in the R(n)/S(n) curve, indicating the location where natural fracture develops. Considering the difference in sensitivity of each log to natural fracture, gray prediction analysis is used to construct a new parameter, fracture prediction indicator K, to quantitatively predict fracture development. In addition, fracture development among different wells is compared. The results show that parameter K responds well to fracture development. Some minor errors may probably be caused by the heterogeneity of the reservoir, limitation of core range and fracture size, dip angle, filling minerals, etc.

Graphical abstract

Keywords

natural fracture prediction / shale oil reservoir / R/S analysis / Chang 7 oil-bearing layer / Ordos Basin

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Acknowledgments

The authors would like to thank the anonymous reviewers for offering their constructive suggestions and comments, which have improved this manuscript in many aspects. The financial supports are from the National Natural Science Foundation of China (Grant Nos. 41702130 and 41971335), Natural Science Foundation of Jiangsu Province, China (BK20201349), and Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).

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