A Deep-learning based Model with Intra- and Inter-Well Constraints for Intelligent Identification of Stratigraphic Layers

Geological stratification interpretation is a critical task in oil and gas exploration, aiming to delineate subsurface formations based on well logging data and provide a structural framework for drilling and reservoir development. Traditional stratification methods rely heavily on manual interpretation, which is subjective, labor-intensive, and often inconsistent, making it inadequate for complex geological settings. To address these limitations, this study proposes a fine-scale stratification method based on a Multi-Layer Perceptron (MLP), incorporating both intra-well and inter-well stratigraphic constraints into the model architecture. The proposed approach begins with principal component analysis (PCA) to reduce the dimensionality of logging parameters while retaining key geological features. Selected input features include well location, depth, drilling time, gamma ray logs, and lithology logs. An MLP model is constructed, and a custom loss function integrates stratigraphic consistency both within single wells and across multiple wells to improve formation boundary prediction. Furthermore, the study introduces a spatial segmentation strategy based on well locations to evaluate both interpolation performance within known areas and extrapolation capability to unseen regions. A case study in a coalbed methane block of the Ordos Basin demonstrates the effectiveness of the method. The model achieves a prediction accuracy of up to 95.04% in stratigraphic regions similar to the training data. Even when applied to extrapolated areas with well distances of approximately 1500–2000 meters from the nearest training point, the model maintains an accuracy of 85.36%. These results indicate that the proposed method not only delivers high precision in familiar formations but also generalizes well to new drilling areas. In conclusion, the MLP-based stratification model developed in this study reduces reliance on expert knowledge and exhibits strong performance in both precision and generalization. It provides a practical and reliable tool for automated stratigraphic interpretation and can support the planning of infill wells and the development of new blocks.