Quantifying structural uncertainty on fault networks using a marked point process within a Bayesian framework

Aydin, Orhun; Caers, Jef

doi:10.1016/j.tecto.2017.04.027

Cited by 23 publications

(23 citation statements)

References 61 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In these approaches the structural data represent the prior geological knowledge, or model parameters ( P ( θ )) and the data ( P ( D )) are usually not the structural data sets used to create geological models. For example, Aydin and Caers () introduce a likelihood function that represents the mismatch between fault observations (seismic interpretations) where the priors are the strike and dip from analog areas. Wellmann et al () and de la Varga and Wellmann () use geology‐based likelihood functions that characterize geological and geophysical observations such as fault geometry, probability of folding, probability of a discontinuity, the probability of an unconformity, or potential field responses where the model parameters (prior knowledge) includes the structural observations.…”

Section: Discussionmentioning

confidence: 99%

“…Geological modeling has previously been considered as an inverse problem (e.g., Aydin & Caers, 2017;de la Varga & Wellmann, 2016;Wellmann et al, 2017;Wood & Curtis, 2004). In these approaches the structural data represent the prior geological knowledge, or model parameters (P( )) and the data (P(D)) are usually not the structural data sets used to create geological models.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Inversion of Structural Geology Data for Fold Geometry

et al. 2018

View full text Add to dashboard Cite

Recent developments in structural modeling techniques have dramatically increased the capability to incorporate fold‐related data into the modeling workflow. However, these techniques are lacking a mathematical framework for properly addressing structural uncertainties. Previous studies investigating structural uncertainties have focused on the sensitivity of the interpolator to perturbing the input data. These approaches do not incorporate conceptual uncertainty about the geological structures and interpolation process to the overall uncertainty estimate. In this work, we frame structural modeling as an inverse problem and use a Bayesian framework to reconcile structural parameters and data uncertainties. Bayesian inference is applied for determining the posterior probability distribution of fold parameters given a set of structural observations and prior distributions based on general geological knowledge and regional observations. This approach allows for an inversion of structural geology data, where each realization can differ in the structural description of the fold geometries, instead of finding only a single best fit solution. We show that analyzing the variability between the resulting models highlights uncertainties associated with the geometry of regional structures. These areas can be used to target where additional data would be most beneficial for improving the model quality and efficiently reducing structural uncertainty.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Inversion of Structural Geology Data for Fold Geometry

et al. 2018

View full text Add to dashboard Cite

show abstract

“…Such a rule returns a number between 0 (v i cannot belong to the fault family ϕ) and 1 (if it is highly likely that v i belongs to the fault family ϕ). Family rules are defined from both the available structural data and the prior regional information such as outcropping analogs or regional tectonic data [as in Caumon, 2015, Aydin andCaers, 2017]. In practice, such rules can use the apparent orientation of an interpreted fault, or the structural style of a fault (i.e.…”

Section: Geological Interpretation Rules To Reduce the Number Of Possmentioning

confidence: 99%

“…There is room for using a large set of geological rules during structural interpretation. In the previous works of Cherpeau and Caumon [2015] and Aydin and Caers [2017], the rules used during stochastic fault modeling are related to the three-dimensional modeling algorithm. Defining new interpretation rules would probably call for intrusive changes in their algorithms.…”

Section: Definition Of Geological Rules According To the Contextmentioning

confidence: 99%

Structural Interpretation of Sparse Fault Data Using Graph Theory and Geological Rules

2019

View full text Add to dashboard Cite

Structural uncertainties arise partly from the association of sparse fault interpretations made from 2D seismic lines or limited outcrop observations. We propose a graph formalism to describe the problem of associating spatial fault evidence. A combinatorial analysis shows that the number of association scenarios is related to the Bell number and increases exponentially. This makes the complete exploration of the uncertainties computationally highly challenging. We formulate prior geological knowledge as numerical rules to reduce the number of scenarios and to make structural interpretation more repeatable and objective. We use the Bron-Kerbosch graph algorithm to detect the major possible structures. This framework opens the way to a numerically assisted exploration of uncertainties during structural interpretation.

show abstract

“…However, relying on a single model cannot reflect the inherent geological uncertainty (Neuman, 2003). Recent advances in geostatistics have shown the importance of using multiple model realizations for uncertainty quantification in many geoscience fields, including glaciology (e.g., Cullen et al, 2017), hydrogeology (e.g., Barfod et al, 2018;Zhou et al, 2014), hydrology (e.g., Goovaerts, 2000;Marko et al, 2014), hydrocarbon reservoir modeling (e.g., Caers and Zhang, 2004;Christie et al, 2002;Dutta et al, 2019;Yin et al, 2019), and geothermal (e.g., Rühaak et al, 2015;Vogt et al, 2010). Geostatistical approaches can provide multiple geological models that are conditioned or constrained to borehole data.…”

Section: Introductionmentioning

confidence: 99%

Automated Monte Carlo-based quantification and updating of geological uncertainty with borehole data (AutoBEL v1.0)

2020

View full text Add to dashboard Cite

Abstract. Geological uncertainty quantification is critical to subsurface modeling and prediction, such as groundwater, oil or gas, and geothermal resources, and needs to be continuously updated with new data. We provide an automated method for uncertainty quantification and the updating of geological models using borehole data for subsurface developments within a Bayesian framework. Our methodologies are developed with the Bayesian evidential learning protocol for uncertainty quantification. Under such a framework, newly acquired borehole data directly and jointly update geological models (structure, lithology, petrophysics, and fluids), globally and spatially, without time-consuming model rebuilding. To address the above matters, an ensemble of prior geological models is first constructed by Monte Carlo simulation from prior distribution. Once the prior model is tested by means of a falsification process, a sequential direct forecasting is designed to perform the joint uncertainty quantification. The direct forecasting is a statistical learning method that learns from a series of bijective operations to establish “Bayes–linear-Gauss” statistical relationships between model and data variables. Such statistical relationships, once conditioned to actual borehole measurements, allow for fast-computation posterior geological models. The proposed framework is completely automated in an open-source project. We demonstrate its application by applying it to a generic gas reservoir dataset. The posterior results show significant uncertainty reduction in both spatial geological model and gas volume prediction and cannot be falsified by new borehole observations. Furthermore, our automated framework completes the entire uncertainty quantification process efficiently for such large models.

show abstract

Quantifying structural uncertainty on fault networks using a marked point process within a Bayesian framework

Cited by 23 publications

References 61 publications

Inversion of Structural Geology Data for Fold Geometry

Inversion of Structural Geology Data for Fold Geometry

Structural Interpretation of Sparse Fault Data Using Graph Theory and Geological Rules

Automated Monte Carlo-based quantification and updating of geological uncertainty with borehole data (AutoBEL v1.0)

Contact Info

Product

Resources

About