Seismic Multi-Attribute Fusion for Natural Fracture Characterization in Carbonate Geothermal Reservoirs: Methodology and Application

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Abstract
The Wumishan Formation carbonate buried-hill reservoir in Hejian, Jizhong Depression, is a key geothermal reservoir where natural fractures control high-yield production. However, sparse control wells and seismic attribute non-uniqueness render conventional manual fusion subjective, while complex models tend to overfit under small-sample conditions. This study presents a well-controlled seismic multi-attribute fusion method that uses four-level (I–IV) well-log reservoir fracture interpretation as supervised calibration labels to predict 3D reservoir fracture distribution. Using 3D seismic data and comprehensive well-log fracture interpretation from wells JR1-1 and JR1-6, more than ten fracture-sensitive attributes were computed and systematically compared against well fracture grades through well-seismic calibration. Three complementary attributes—high-resolution eigenvalue coherence, maximum principal component curvature, and maximum likelihood similarity enhancement—were selected as primary fusion channels. An ordinal logistic regression model was then trained on the well-calibrated samples, jointly solving feature weights and ordinal cumulative classification thresholds within a unified likelihood framework to map seismic features onto the four-level reservoir fracture classification. Application to the target block shows that data-driven attribute weights are consistent with expert manual weights, and leave-one-well-out cross-validation yields 80.5–84.1% reproducibility (Kappa = 0.744). The resulting reservoir fracture distribution exhibits pronounced spatial zonation and vertical segmentation, with dominant fractures concentrated at structural highs on the reservoir top surface. This method provides a robust, well-controlled framework for natural fracture characterization and can guide geothermal well target optimization and reservoir stimulation design in deep geothermal fields.
Funding Information
  • the Talent Fund of Liaoning Petrochemical University (1100130301)
  • National Natural Science Foundation of China (52574041)
  • China National Petroleum Corporation (2024DJ95)