Probability maps of reservoir presence and sensitivity analysis in stratigraphic forward modeling

Gervais, Véronique; Ducros, Mathieu; Granjeon, Didier

doi:10.1306/0913171611517242

Cited by 18 publications

(21 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(12). These indices are variance-based global sensitivity metrics and can be employed to apportion the variance of a target model output amongst the uncertain parameters employed to describe the system behavior (see Gervais et al 2018 for applications to SFMs).…”

Section: And References Therein)mentioning

confidence: 99%

“…As these models involve a large number of input parameters, a key limitation is the validation process of the resulting modeled stratigraphy, which is one of the main outputs of SFMs (Falivene et al 2014). Applications of SFMs are usually based on literature information to constrain model parameter values on a qualitative comparison between final modeling outputs and field data, parameter uncertainty being only considered by trial and error (Gervais et al 2018). In this context, a significant amount of work and time resources need to be invested to manually calibrate stratigraphic models to somehow match available information.…”

Section: Introductionmentioning

confidence: 99%

“…These authors show promising results regarding the applicability of an automatic model calibration procedure to SFMs. Given the typically large number of input parameters required in SFMs, the effectiveness and efficiency of model calibration may be largely improved by relying on the joint use of Sensitivity Analysis (SA) and reduced order models, as discussed by Gervais et al (2018) in the context of forward uncertainty quantification for SFMs. Surrogate models may then be used to reduce the computational cost associated with uncertainty propagation of the most relevant inputs to a selected range of outputs (e.g., distribution of sand volumes, Gervais et al 2018).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Stochastic Inverse Modeling and Parametric Uncertainty of Sediment Deposition Processes Across Geologic Time Scales

et al. 2020

View full text Add to dashboard Cite

In this work an integrated methodological and operational framework for diagnosis and calibration of Stratigraphic Forward Models (SFMs) which are typically employed for the characterization of sedimentary basins is presented. Model diagnosis rests on local and global sensitivity analysis tools and leads to quantification of the relative importance of uncertain model parameters on modeling goals of interest. Model calibration is performed in a stochastic framework, leading to estimates of distributions of model parameters (and ensuing spatial distributions of model outputs) conditional on available information. Starting from a considerable number of uncertain model parameters, which is typically associated with SFMs of the kind analyzed, the approach leads to the identification of a reduced set of parameters which are most influential to drive stratigraphic modeling results. Probability distributions of these model parameters conditional on available data are then evaluated through stochastic inverse modeling. To alleviate computational efforts, this step is performed through a combination of a surrogate model constructed through the Polynomial Chaos Expansion approach and a machine learning algorithm for efficient search of the parameter space during model inversion. As a test bed for the workflow, focus is on a realistic synthetic three-dimensional scenario which is modeled through a widely used SFM that enables one to perform three-dimensional numerical simulations of the accumulation of siliciclastic and carbonate sediments across geologic time scales. These results constitute a robust basis upon which further deployment of the approach to industrial field settings can be designed.

show abstract

Section: And References Therein)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Stochastic Inverse Modeling and Parametric Uncertainty of Sediment Deposition Processes Across Geologic Time Scales

et al. 2020

View full text Add to dashboard Cite

show abstract

“…The model was calibrated on thicknesses and facies; (2) in the second step, a set of probability maps and sensitivity analysis were performed on 2 variables of interest (TOC and oxygen level at the sea floor). These analyses are based on a recent Global Sensitivity Analysis (GSA) approach by Gervais et al [27] and give access for this study to the spatial influence of the critical parameters (bathymetry, organic matter transport, oxygen mixing rate and primary productivity) on the output parameters (TOC and oxygen level). Besides the results of the sensitivity analysis, several models among the most representatives of the data from well studies and corresponding to different numerical scenario, were also compared to hypothesis available in literature and discussed.…”

Section: Introductionmentioning

confidence: 99%

3D Numerical Modelling and Sensitivity Analysis of the Processes Controlling Organic Matter Distribution and Heterogeneity—A Case Study from the Toarcian of the Paris Basin

et al. 2018

Self Cite

View full text Add to dashboard Cite

The active debate about the processes governing the organic-rich sediment deposition generally involves the relative roles of elevated primary productivity and enhanced preservation related to anoxia. However, other less spotlighted factors could have a strong impact on such deposits, e.g., residence time into the water column (bathymetry), sedimentation rate, transport behavior of organo-mineral floccules on the sea floor. They are all strongly interrelated and may be obscured in the current conceptual models inspired from most representative modern analogues (i.e., upwelling zones and stratified basins). To improve our comprehension of organic matter distribution and heterogeneities, we conducted a sensitivity analysis on the processes involved in organic matter production and preservation that have been simulated within a 3D stratigraphic forward model. The Lower-Middle Toarcian of the Paris Basin was chosen as a case study as it represents one of the best documented example of marine organic matter accumulation. The relative influence of the critical parameters (bathymetry, diffusive transport, oxygen mixing rate and primary production) on the output parameters (Total Organic Carbon, and oxygen level), determined performing a Global Sensitivity Analysis, shows that, in the context of a shallow epicontinental basin, a moderate primary productivity (>175 gC·m−2·year−1) can led to local anoxia and organic matter accumulation. We argue that, regarding all the processes involved, the presence and distribution of organic-rich intervals is linked as a first-order parameter to the morphology of the basin (e.g., ramp slope, bottom topography). These interpretations are supported by very specific ranges of critical parameters that allowed to obtain output parameter values in accordance with the data. This quantitative approach and its conclusions open new perspectives about the understanding of global distribution and preservation of organic-rich sediments.

show abstract

“…The model was calibrated on thicknesses and facies; (2) in the second step, a set of probability maps and sensitivity analysis were performed on 2 variables of interest (TOC and oxygen level at the sea floor). These analyses are based on a recent global sensitivity analysis approach by Gervais et al [27] and give access for this study to the spatial influence of the critical parameters (bathymetry, organic matter transport, oxygen mixing rate and primary productivity) on the output parameters (TOC and oxygen level). Besides the results of the sensitivity analysis, several models among the most representatives of the data from well studies and corresponding to different numerical scenario, were also compared to hypothesis available in literature and discussed.…”

Section: Introductionmentioning

confidence: 99%

3D Numerical Modelling and Sensitivity Analysis of the Processes Controlling Organic Matter Distribution and Heterogeneity. A Case Study from the Toarcian of the Paris Basin

Bruneau¹,

Villié²,

Ducros³

et al. 2018

Preprint

Self Cite

View full text Add to dashboard Cite

The active debate about the processes governing the organic-rich sediment deposition generally involves the relative roles of elevated primary productivity and enhanced preservation related to anoxia. However, other less spotlighted factors could have a strong impact on such deposits: e.g. residence time into the water column (bathymetry), sedimentation rate, transport behavior of organo-mineral floccules on the sea floor. They are all strongly interrelated and may be obscured in the current conceptual models inspired from most representative modern analogues (i.e. upwelling zones and stratified basins). To improve our comprehension of organic matter distribution and heterogeneities, we conducted a sensitivity analysis on the processes involved in organic matter production and preservation which have been simulated within a 3D stratigraphic forward model. The Lower-Middle Toarcian of the Paris Basin was chosen as a case study as it represents one of the best documented example of marine organic matter accumulation. The relative influence of the critical parameters (bathymetry, diffusive transport, oxygen mixing rate and primary production) on the output parameters (Total Organic Carbon, and oxygen level), determined performing a Global Sensitivity Analysis, shows that, in the context of a shallow epicontinental basin, a moderate primary productivity (> 175 gC.m-².yr-1) can led to local anoxia and organic matter accumulation. We argue that, regarding all the processes involved, the presence and distribution of organic-rich intervals is linked as a first-order parameter to the morphology of the basin (e.g. ramp slope, bottom topography). These interpretations are supported by very specific ranges of critical parameters which allowed to obtain output parameter values in accordance with the data. This quantitative approach and its conclusions open new perspectives about the understanding of global distribution and preservation of organic-rich sediments.

show abstract

Probability maps of reservoir presence and sensitivity analysis in stratigraphic forward modeling

Cited by 18 publications

References 23 publications

Stochastic Inverse Modeling and Parametric Uncertainty of Sediment Deposition Processes Across Geologic Time Scales

Stochastic Inverse Modeling and Parametric Uncertainty of Sediment Deposition Processes Across Geologic Time Scales

3D Numerical Modelling and Sensitivity Analysis of the Processes Controlling Organic Matter Distribution and Heterogeneity—A Case Study from the Toarcian of the Paris Basin

3D Numerical Modelling and Sensitivity Analysis of the Processes Controlling Organic Matter Distribution and Heterogeneity. A Case Study from the Toarcian of the Paris Basin

Contact Info

Product

Resources

About