Geological Objects and Physical Parameter Fields in the Subsurface: A Review

Caumon, Guillaume

doi:10.1007/978-3-319-78999-6_28

Cited by 9 publications

(8 citation statements)

References 160 publications

(169 reference statements)

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“…The geomodel-driven approach, therefore, should certainly not be confused with simply adding all detail and complexity that is present in nature, to obtain a "realistic" picture. The aim of modeling is always an abstraction, and this consideration holds here in the same way as in other cases (see also discussions in Ringrose and Bentley (2015); Caumon (2018) and throughout this paper).…”

Section: Combining Elementsmentioning

confidence: 99%

“…3.2. In this section, we build on the same formalism as Sambridge et al (2013) and further explain and elaborate the ideas expressed by Caumon (2018). Figure 6 shows various geomodels representing possible seismic wave velocity fields in the subsurface, and a corresponding set of one-dimensional profiles showing the true field (in red) and how the various representations approximate it (in black).…”

Section: Geomodel Representationsmentioning

confidence: 99%

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3-D Structural geological models: Concepts, methods, and uncertainties

Wellmann

Caumon

2018

Advances in Geophysics

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163

103

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The Earth below ground is the subject of interest for many geophysical as well as geological investigations. Even though most practitioners would agree that all available information should be used in such an investigation, it is common practice that only a fraction of geological and geophysical information is used. We believe that some reasons for this omission are (a) an incomplete picture of available geological modeling methods, and (b) the problem of the perceived static picture of an inflexible geological representation in an image or geological model. With this work, we aim to contribute to the problem of subsurface interface detection through (a) the review of state-of-the-art geological modeling methods that allow the consideration of multiple aspects of geological realism in the form of observations, information and knowledge, cast in geometric representations of subsurface structures, and (b) concepts and methods to analyze, quantify, and communicate related uncertainties in these models. We introduce a formulation for geological model representation and interpolation and uncertainty analysis methods with the aim to clarify similarities and differences in the diverse set of approaches that developed in recent years. We hope that this chapter provides an entry point to recent developments in geological modeling methods, helps researchers in the field to better consider uncertainties, and supports the integration of geological observations and knowledge in geophysical interpretation, modeling and inverse approaches.

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Section: Combining Elementsmentioning

confidence: 99%

Section: Geomodel Representationsmentioning

confidence: 99%

3-D Structural geological models: Concepts, methods, and uncertainties

Wellmann

Caumon

2018

Advances in Geophysics

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163

103

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“…In general, these static reservoir models represent the internal anatomy, lithological configuration and petrophysical properties of hydrocarbon reservoirs as cell-based corner-point reservoir grids (Farmer, 3 2005; Ringrose & Bentley, 2015). For each reservoir, or sector thereof, the uncertainty relating to geological and petrophysical features is usually addressed by generating multiple equiprobable models built using stochastic modelling methods (Caumon, 2018). The approaches that are typically used to model the distributions of meander-belt facies associations in subsurface fluvial successions consist of general-purpose geostatistical modelling tools, such as those based on two-point statistics (e.g., sequential indicator simulations; Journel & Alabert, 1989;Deutsch & Journel, 1992) or on multi-point statistics (e.g., Srivastava, 1994;Strebelle, 2002;Straubhaar & Malinverni, 2014;Arnold et al, 2019;Calderon et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…A solution to this is offered by inverse (i.e., reverse) stratigraphic modelling methods (e.g., ChaRMigS method of Parquer, et al, 2017), which honour observations of abandoned meanders by integrating them into a vector-based modelling structure that can be used to determine the temporal evolution of a river channel. In all cases, the question of uncertainty assessment remains central and calls for considering multiple realizations of reservoir geometry (Pyrcz et al, 2015;Caumon, 2018;Arnold et al, 2019). Overall, these rule-based approaches have been shown to be a promising way to address the shortcomings of pixel-based and object-based methods, in particular towards honouring all quantitative spatial observations while maintaining consistent relationships between the simulated architectural elements.…”

Section: Introductionmentioning

confidence: 99%

Combined inverse and forward numerical modelling for reconstruction of channel evolution and facies distributions in fluvial meander-belt deposits

Parquer

Yan

Colombera

et al. 2020

Marine and Petroleum Geology

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The sedimentary record of meandering rivers contains a diverse and complex set of lithological heterogeneities, which impact natural resource management. Different methods exist to model such accumulated successions present in the subsurface by integrating knowledge of system evolutionary behaviour and geometries visible on seismic time or stratal slices. With reference to case-study examples, we review, discuss and employ two of these methods: (i) ChaRMigS generates possible scenarios for channel evolution and meander cutoffs by a reverse migration process; (ii) PB-SAND is a forward stratigraphic model which simulates fluvial point-bar geometry and facies distributions from known palaeo-channel geometries. We introduce a workflow to demonstrate how these two methods can be applied in combination to predict fluvial meander-belt facies distributions, using a subsurface dataset on a Pleistocene succession from the Gulf of Thailand where abandoned channels are visible on seismic time slices, but for which bar-accretion geometries and the exact timing of channel abandonment are unclear. Results show the value of a combined modelling approach to automate the stochastic generation of facies distributions constrained by seismic interpretations.

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“…From borehole data, geological models are constructed for appraisal and uncertainty quantification, such as estimating water volumes stored in groundwater systems or heat storage in a geothermal system. Realistic geological modelling involves complex procedures (Caumon, 2010(Caumon, , 2018de la Varga et al, 2019). This is due to the hierarchical nature of geological formations: fluids are contained in a porous medium, the porous medium is defined by various lithologies, lithological variation is contained in faults and layers (structure).…”

Section: Introductionmentioning

confidence: 99%

Automated Monte Carlo-based Quantification and Updating of Geological Uncertainty with Borehole Data (AutoBEL v1.0)

Yin¹,

Strebelle²,

Caers³

2019

Preprint

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Abstract. We provide an automated method for uncertainty quantification and updating of geological models using borehole data for subsurface developments (groundwater, geothermal, oil & gas, and CO2 sequestration, etc.) within a Bayesian framework. Our methodologies are developed with the Bayesian Evidential Learning protocol for uncertainty quantification. Under such framework, newly acquired borehole data directly and jointly update geological models (structure, lithology, petrophysics and fluids), globally and spatially, without time-consuming model re-buildings. To address the above, 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 falsification process, a sequential direct forecasting is designed to perform the joint uncertainty quantification. The direct forecasting is a data-scientific 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, allows for fast computation posterior geological models. The proposed framework is completely automated in an opensource project. We demonstrate its application by applying to a generalized synthetic dataset motivated by a gas reservoir from Australia. 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.

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Geological Objects and Physical Parameter Fields in the Subsurface: A Review

Cited by 9 publications

References 160 publications

3-D Structural geological models: Concepts, methods, and uncertainties

3-D Structural geological models: Concepts, methods, and uncertainties

Combined inverse and forward numerical modelling for reconstruction of channel evolution and facies distributions in fluvial meander-belt deposits

Automated Monte Carlo-based Quantification and Updating of Geological Uncertainty with Borehole Data (AutoBEL v1.0)

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