Abstract
In geomodeling, it is commonly accepted that the distribution of physical properties is controlled by the architecture of geological objects. However, insufficient data and the complexity of earth processes create an ill-posed problem where many architectures are plausible. Consequently, several geologists will produce different geological models for the same location. This contribution proposes a way to objectivize the ranking of those conceptual models by comparing them with hard data, both globally for the whole study region and locally for certain of its sectors. The idea is to extend the multi-point geostatistics direct sampling algorithm to be able to extract data events from different training images, representing several competing geological models, and to record the training image origin of values pasted on simulation grid cells. By tracking the frequency with which every training image is visited, we can rank the likelihood of each geological model. Histograms of the frequency of usage of each training image will provide a global ranking of the several conceptual models, while maps of these frequencies can be used to produce the local rankings. We demonstrate this method in two synthetic fluvial depositional environments where three distinct geological concepts are being proposed, with different abundances of hard data. Results indicate that the proposed method could be a useful tool in defining which geological concept dominates at a particular region and which is the frequency ranking for each training image on that region.
Highlights
Geological heterogeneity has a strong influence on the distribution of physical properties related to earth resources such as the concentration of economic minerals, porosity and permeability
When evaluating the geological conditions of an area, geologists must deal with complex, scarce, and unevenly distributed data originating from a broad range of measurement devices with different scales
The objective of this paper is to propose a data-driven method to rank the preference for local conceptual models
Summary
Geological heterogeneity has a strong influence on the distribution of physical properties related to earth resources such as the concentration of economic minerals, porosity and permeability. The process of identifying and building a geological architecture, including the location of important geological bodies and their relationships, is usually established before spatially populating them with physical properties (Strebelle and Remy 2005; Zhang et al 2006; de Almeida 2010) This conventional workflow has proven to be effective in both the exploration and exploitation phases of natural resources. Direct measurements of critical physical properties, e.g. out of core samples, are very limited so that the physical properties are mostly interpolated or calculated from indirect measurements such as well logs, seismic data, or other geophysical methods The illposedness of this problem allows several geologists to have different interpretations for the same data on the same area. This work highlighted the need for tools that can robustly quantify the likelihood of proposed geological concepts against further independent data, as a means for validation, selection, or ranking of competing geologic interpretations
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
