Appraising structural interpretations using seismic data — Theoretical elements

Abstract

Structural interpretation of seismic images can be highly subjective, especially in complex geologic settings. A single seismic image will often support multiple geologically valid interpretations. However, it is usually difficult to determine which of those interpretations are more likely than others. We have referred to this problem as structural model appraisal. We have developed the use of misfit functions to rank and appraise multiple interpretations of a given seismic image. Given a set of possible interpretations, we compute synthetic data for each structural interpretation, and then we compare these synthetic data against observed seismic data; this allows us to assign a data-misfit value to each structural interpretation. Our aim is to find data-misfit functions that enable a ranking of interpretations. To do so, we formalize the problem of appraising structural interpretations using seismic data and we derive a set of conditions to be satisfied by the data-misfit function for a successful appraisal. We investigate vertical seismic profiling (VSP) and surface seismic configurations. An application of the proposed method to a realistic synthetic model shows promising results for appraising structural interpretations using VSP data, provided that the target region is well-illuminated. However, we find appraising structural interpretations using surface seismic data to be more challenging, mainly due to the difficulty of computing phase-shift data misfits.

Multi‐scenario Interpretations From Sparse Fault Evidence Using Graph Theory and Geological Rules

“…Furthermore, for a human being, it seems difficult to work with more than a few alternative scenarios deemed representative of the uncertainties. An effective way to address this problem is to use model clustering in model space (e.g., Suzuki et al, 2008) or in data space (e.g., Irakarama et al, 2019; Scheidt et al, 2018). A challenge, in both cases, comes from the redundancy of models sampled by a particular stochastic methods: Indeed, simulation methods tend to generate many similar models in a priori likely regions of the search space.…”

“…Stochastic structural modeling has already been proposed to generate several scenarios while taking account of seismic image quality and faults below seismic resolution (Aydin & Caers, 2017;Hollund et al, 2002;Holden et al, 2003;Irving et al, 2010;Julio et al, 2015aJulio et al, , 2015bLecour et al, 2001); uncertainty related to reflection seismic acquisition and processing (Osypov et al, 2013;Thore et al, 2002); geological field measurement uncertainty (Jessell et al, 2014;Lindsay et al, 2012;Pakyuz-Charrier et al, 2019;Wellmann et al, 2014); structural parameters for folding (Grose et al, 2018(Grose et al, , 2019; and observation gaps (Aydin & Caers, 2017;Cherpeau & Caumon, 2015;Cherpeau et al, 2010b;Holden et al, 2003). Considering several structural interpretations has also proved useful to propagate uncertainties to subsurface flow problems (Julio et al, 2015b), to rank structural models against physical data and ultimately to falsify some of the interpretations using a Bayesian approach (Cherpeau et al, 2012;de la Varga & Wellmann, 2016;Irakarama et al, 2019;Seiler et al, 2010;Suzuki et al, 2008;Wellmann et al, 2014).…”