- 1School of Geosciences, China University of Petroleum(EastChina), Qingdao, China
- 2State Key Laboratory of Deep Oil and Gas, China University of Petroleum (EastChina), Qingdao, China
- 3Exploration and Development Research Institute, Sinopec Shengli Oilfield Company, Dongying, China
- 4Oil and Gas Exploration Management Center, Sinopec Shengli Oilfield Company, Dongying, China
Electrical imaging logs are widely used for the quantitative characterization of unconventional reservoirs. However, their applicability is severely limited in pervasively fractured continental shale-oil reservoirs due to strong heterogeneity and complex resistivity responses.
To address this limitation, an electrical statistical framework is developed that integrates variogram-based attributes with smoothness functions to identify fracture occurrence and quantitatively characterize fracture development in shale reservoirs. Resistivity percentile curves derived from electrical imaging logs are analyzed using a moving-window scheme to extract multidimensional parameters, including variance, Shannon entropy, variogram metrics, and smoothness indices. These parameters are jointly used to construct a fracture development index. In addition, fracture-prone intervals are identified using an adaptive thresholding approach constrained by geological rules and resistivity separation characteristics. To suppress the influence of stratigraphic background trends, a local background-resistivity normalization is applied, enabling fracture classification based on resistivity ratios.
The method is validated using shale-oil reservoirs of the Shahejie Formation in the Dongying Sag, eastern China. The results demonstrate that, within a tolerance range of 0.125 m, fracture identification derived from electrical image logs achieves an 85.4% agreement with core descriptions. The identification accuracies for high-conductivity and high-resistivity fractures reach 90.41% and 80.65%, respectively, while approximately 47% of the identified fractures exhibit high conductivity.The proposed approach provides new insights into fracture-filling properties and their vertical distribution, highlighting its applicability to shale-oil reservoir characterization, sweet-spot evaluation, and hydraulic fracturing design.
This research was supported by the National Oil & Gas Major Project (No. 2024ZD1405102).
Fig. 1. Electrical Imaging–Based Statistical Indicators for Fracture Identification in Well FY3
How to cite: Wang, Z., Ge, X., Jiang, L., Sun, H., Li, Z., Zhang, D., and Yang, D.: Electrical Imaging–Based Statistical Indicators for Fracture Identification in Shale-Oil Reservoirs, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3394, https://doi.org/10.5194/egusphere-egu26-3394, 2026.
