Visual selection of standard wells for large scale logging data via discrete choice model.

[Display omitted] • A new blue noise sampling is proposed to preserve the spatial distribution of standard wells. • A stratigraphic correlation model is designed to estimate the similarity between wells. • A discrete choice model is used to select standard wells with stochastic utility maximization. • A visualizaiton framework is implemented enabling users to visually select and evaluate standard wells. It is a time-consuming and laborious task to conduct accurate geological interpretation based on large-scale logging data. A subset of drilling wells are always selected undergoing detailed stratigraphic correlation, aiming to enhance the efficiency of geological interpretation and retain the matching accuracy of stratigraphic structures. However, standard wells are selected by hand in the course of traditional standard well selection, which will easily bring many uncertainties for subsequent interpretation of geological structures. In this paper, we propose a visual analytics system to support supervised standard well selection via a discrete choice model. Firstly, an adaptive blue noise sampling model is applied to determine spatial distribution of standard wells, and a stratigraphic correlation model is designed to measure the similarity between drilling wells from different attribute perspectives. Then, we utilize a discrete choice model to select standard wells with the spatial distribution and multiple attributes taken into consideration. Furthermore, several meaningful visualizations are designed allowing users to get deeper insights into the selection of standard wells, and a rich set of interactions are also provided enabling users to further optimize the discrete choice model. At last, a visualization framework is implemented to integrate the models and visual designs, by means of which the geological interpreters are able to visually select, evaluate and optimize the standard wells. The effectiveness of our work and its application values for geological interpretation are further demonstrated through case studies based on real-world datasets and interviews with domain experts. [ABSTRACT FROM AUTHOR]