(ChinaVis 2019) uncertainty visualization in stratigraphic correlation based on multi-source data fusion

As a most important step in geological interpretation, stratigraphic correlation plays important roles in reservoir estimation and geologic modeling. A variety of datasets are used for stratigraphic correlation, such as well-logging data and seismic data, which are collected by different kinds of sensors. However, much uncertainty will be generated in the traditional course of stratigraphic correlation, because the complex underground geological structures cannot be comprehensively depicted by single dataset. Therefore, in this paper, we propose a visualization system to present and reduce the uncertainty in stratigraphic correlation based on the fusion analysis of multi-source datasets. First, a synthetic seismogram is modeled for each drilling well and a traditional time-depth conversion is conducted to match the seismic data and logging data. Then, an uncertainty model is proposed to quantify the depth difference between seismic horizons and stratigraphic structures extracted from different datasets. Furthermore, a set of visual designs are integrated into an uncertainty visualization system, enabling users to conduct intuitive uncertainty exploration and supervised optimization of stratigraphic correlation. Case studies based on real-world datasets and interviews with domain experts have demonstrated the effectiveness of our system in analyzing the uncertainty of stratigraphic correlation and refining the results of geological interpretation.