Visual Analytics of Stratigraphic Correlation for Multi-Attribute Well-Logging Data Exploration

Abstract

Stratigraphic correlation based on well-logging data is able to help geological interpreters analogize and deduce underground sedimentary morphology. A great deal of traditional methods has been studied to automatically or manually achieve stratigraphic correlation, the courses of which are usually time-consuming and not intuitive. Many uncertainties are easily generated to reduce the effectiveness of stratigraphic correlation and the accuracy of geological interpretation. To address this issue, this paper introduces an interactive visual analytics system for identifying correlation patterns and improving correlation accuracies using large-scale well-logging data. First, we propose a novel stratigraphic correlation model with the composition of multi-log curve integration, layer identification, and layer matching. Then, a visualization framework is designed by working closely with domain experts in an iterative manner to get deeper insights into the course of stratigraphic correlation based on a few visual interfaces such as map view, correlation view, matrix view and attribute view. Also, a rich set of interactions is provided allowing interpreters to refine the results of stratigraphic correlation according to domain knowledge and user requirements. Furthermore, case studies based on real-world datasets and interviews with domain experts have demonstrated the effectiveness of our system for the stratigraphic correlation and geological interpretation.