Fracture Identification Based on Graph Pooling and Graph Construction in Continental Shale

Identification of natural fractures in continental shale is a problematic task by conventional logging. To address this issue, a method known as Frac-gPCC that combines graph pooling, graph construction, and node classification to train the identification model is proposed in this study. This method integrates existing geological knowledge into the graph structure and model network construction and captures the topology information of a single fracture and fractured zone through the calculation of graphs. The model effectively addresses the issue of incorrectly identifying samples as nonfractures when the number of fracture samples is significantly less than nonfractures. Concurrently, this method utilizes a large number of unlabeled samples effectively to incorporate them in model training, avoiding the problem of limited labeled samples. The identification process is divided into three steps: first, reconstruct the logging curve and use the graph pooling section to filter and fuse the node information within a certain depth interval to enhance the characteristics of the tool response to fractures. Second, it integrates the relationship between fractures and lithologies, as well as the spatial distribution characteristics of the strata, into the structure of the global graph. Finally, the nodes of the constructed global graph are classified through the node classification section, which naturally supports combination generalization. This method is applied in the Fengcheng Formation of the Mahu Sag, Western China. The results showed that the identification accuracy for testing data reaches 91.96%. Collectively, this reflects the superiority of Frac-gPCC in fracture identification, providing a successful workflow for continental shale characterization.