Fluid classification with dynamic graph convolution network by local linear embedding well logging data

Abstract

Fluid prediction is pivotal in exploration, aiding in the identification of targets and estimating reserve potential. To enhance well logging data processing, we employ local linear embedding (LLE) for dimensionality reduction. LLE effectively reduces data dimensionality by identifying local linear relationships and preserving essential local structure in a low-dimensional space, which is particularly advantageous for log data that often contains formation-specific information, including fluid content. The process of dimensionality reduction through LLE retains vital stratigraphic information, which is key for insightful subsequent analyses. Next, we utilize a dynamic graph convolutional network (DGCN) integrated with a multi-scale temporal self-attention (TSA) module for fluid classification on the reduced data. This multi-scale temporal self-attention module is specifically designed to capture time series information inherent in well logging data, allowing the model to autonomously learn and interpret temporal dependencies and evolutionary patterns in the data. This enhances the accuracy of fluid prediction, particularly in the context of varying rock layer characteristics over time. Our methodology, combining LLE with DGCN-TSA, has demonstrated high accuracy in applications such as Tarim Oilfield logging data analysis. It amalgamates advanced technologies with a robust generalization ability. In practical applications, this approach provides steadfast support for oil and gas exploration, significantly contributing to the refinement of fluid prediction accuracy.