Abstract
When too few field measurements are available for the geological modeling of complex folded structures, the results of implicit methods typically exhibit an unsatisfactory bubbly aspect. However, in such cases, anisotropy data are often readily available but not fully exploited. Among them, fold axis data are a straightforward indicator of this local anisotropy direction. Focusing on the so-called potential field method, this work aims to evaluate the effect of the incorporation of such data into the modeling process. Given locally sampled fold axis data, this paper proposes to use the second-order derivatives of the scalar field in addition to the existing first-order ones. The mathematical foundation of the approach is developed, and the respective efficiencies of both kinds of constraints are tested. Their integration and impact are discussed based on a synthetic case study, thereby providing practical guidelines to geomodeling tool users on the parsimonious use of data for the geological modeling of complex folded structures.
Highlights
Structural geological modeling aims to achieve three-dimensional models of geological objects and of their relationships
We focus on field measurement data but the following remarks can be generalized to borehole data, or manual or automatic surface interpretation from geophysical surveys
This paper tackles a well-known issue encountered in the potential field method: when the data is sparse, classical models can lead to unsatisfactory results
Summary
Structural geological modeling aims to achieve three-dimensional models of geological objects and of their relationships It is a mandatory and determining step for most geological studies in various fields of application: resource estimation, reservoir study, risk assessment, environmental remediation. It relies on a conceptual model proposed by geologists based on field observations Given these preliminary data, the main goal is to choose a suitable mathematical model with parameters that give an optimal representation of the reality [Wellmann and Caumon, 2018]. The former directly builds geological interfaces by solving a least-square smooth approximation problem on a discrete mesh [Mallet, 1992] The latter relies on a preliminary interpolation of a three-dimensional scalar field from geological data. Are the surfaces of interest extracted: they are those holding the same scalar value, referred to as their potential
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