Geophysical basin modeling: Generation of high quality velocity and density cubes for seismic imaging and gravity field monitoring in complex geology settings

Brevik, Ivar; Szydlik, Teresa; Corver, Maarten P.; Prisco, Giuseppe De; Stadtler, Christopher; Helgesen, Hans Kristian

doi:10.1190/segam2014-0444.1

Cited by 13 publications

(5 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, basin modeling provides a quantitative framework to simulate sub-surface properties coherent with physical processes occurring throughout the geo-history (Hantschel and Kauerauf, 2009). Brevik et al (2014) coined the term geophysical basin modeling (GBM) as the procedure of estimating velocity models by integrating rock physics with basin model outcomes. The geo-history of porosity, effective stress, temperature and mineral volume fractions ultimately affect the velocity of rocks perceived by seismic waves.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Geophysical Basin Modeling with Seismic Kinematics Metrics to Quantify Uncertainty for Pore Pressure Prediction

Fonseca¹,

Pradhan²,

Mukerji³

2023

Preprint

View full text Add to dashboard Cite

Bayesian geophysical basin modeling (BGBM) methodology is an interdisciplinary workflow that incorporates data, geological expertise, and physical processes through Bayesian inference in sedimentary basin models. Its application culminates in subsurface models and properties that integrate the geo-history of a basin, rock physics definitions, well log and drilling data, and seismic information. Monte Carlo basin modeling realizations are performed by sampling from prior probability distributions on facies parameters and basin boundary conditions. After data assimilation, the accepted set of posterior sub-surface models yields uncertainty quantification of subsurface properties. This procedure is especially suitable for pore pressure prediction in a predrill stage. However, the high computational cost of seismic data assimilation decreases the practicality of the workflow. Therefore, we introduce and investigate seismic traveltimes criteria as computationally faster proxies for analyzing the seismic data likelihood when employing BGBM. The proposed surrogate schemes weigh the prior basin model results with the available seismic data with no need to perform expensive seismic depth-migration procedures for each Monte Carlo realization. Furthermore, we apply BGBM in a real field case from the Gulf of Mexico using a 2D section for pore pressure prediction considering different kinematics criteria. The workflow implementation with the novel seismic data assimilation proxies is compared with the complete computationally expensive benchmark approach, which utilizes a global analysis of the residual moveout in depth-migrated seismic image samples. Moreover, we validate and compare the outcomes for predicted pore pressure with mud-weight data from a blind well. The fast proxy of analyzing the depth-positioning of seismic horizons proposed in this work yields similar uncertainty quantification results in pore pressure prediction compared to the computationally expensive benchmark. The proposed fast proxies make the BGBM methodology efficient and practical.

show abstract

Section: Introductionmentioning

confidence: 99%

Bayesian Geophysical Basin Modeling with Seismic Kinematics Metrics to Quantify Uncertainty for Pore Pressure Prediction

Fonseca¹,

Pradhan²,

Mukerji³

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Rock physics provides physical models of the rock microstructure, which can be used to relate basin modeling generated properties to elastic properties, and consequently to velocities (Figure 1). Brevik et al (2011Brevik et al ( , 2014, De Prisco et al (2015) and Szydlik et al (2015) lay out a framework for integrating basin modeling with geophysics in what has been termed as Geophysical Basin Modeling. Their proposed workflow entails modeling the basin in the area of interest and subsequently employing rock physics based models to generate the initial velocity model and model parameter covariance to be used in the velocity inversion process.…”

Section: Introductionmentioning

confidence: 99%

Approximate Bayesian inference of seismic velocity and pore-pressure uncertainty with basin modeling, rock physics, and imaging constraints

et al. 2020

View full text Add to dashboard Cite

We have introduced a methodology for quantifying seismic velocity and pore-pressure uncertainty that incorporates information regarding the geologic history of a basin, rock physics, well log, drilling, and seismic data. In particular, our approach relies on linking velocity models to the basin modeling outputs of porosity, mineral volume fractions, and pore pressure through rock-physics models. We account for geologic uncertainty by defining prior probability distributions on lithology-specific porosity compaction model parameters, permeability-porosity model parameters, and heat-flow boundary condition. Monte Carlo basin simulations are performed by sampling the prior uncertainty space. We perform probabilistic calibration of the basin model outputs by defining data likelihood distributions to represent well data uncertainty. Rock physics modeling transforms the basin modeling outputs to give us multiple velocity realizations used to perform multiple depth migrations. We have developed an approximate Bayesian inference framework that uses migration velocity analysis in conjunction with well data for updating velocity and basin modeling uncertainty. We apply our methodology in 2D to a real field case from the Gulf of Mexico; our methodology allows for building a geologic and physical model space for velocity and pore-pressure prediction with reduced uncertainty.

show abstract

“…Then they use an empirical relationship between velocity and effective stress, derived from well data (Petmecky et al, 2008), to create a velocity volume. More recently, De Prisco et al (2014) and Szydlik et al (2015) use methods presented by Brevik et al (2014) to build isotropic and anisotropic subsalt velocity models with porosity, permeability, and effective stress volumes calculated from an integrated basin simulator. In each of these cases, the basin simulator is a tool that uses a geologic interpretation to create a migration velocity volume for the subsalt region, where tomography is either significantly limited or cannot be applied.…”

Section: Introductionmentioning

confidence: 99%

Isotropic and anisotropic velocity-model building for subsalt seismic imaging

2016

View full text Add to dashboard Cite

The effectiveness of basin simulators for deriving subsalt velocity models has been previously shown through the use of a correlation to relate effective stress to velocity. We build on this and others' work by using physical models to relate porosity to velocity. This process yields a physically realizable isotropic velocity model that is consistent with the geologic model and matches the tomographic velocity model above salt and in regions where the tomographic velocity estimate is accurate. We then use a geomechanical simulator to model the stress distribution in and around allochthonous salt in which material properties between salt and sediment change. Our stress model is the basis for an anisotropic velocity model using Murnaghan's theory for finite elastic deformation. This formulation, with bounds placed on the elastic coefficients, leads to significant imaging improvements adjacent to salt.

show abstract

Geophysical basin modeling: Generation of high quality velocity and density cubes for seismic imaging and gravity field monitoring in complex geology settings

Cited by 13 publications

References 2 publications

Bayesian Geophysical Basin Modeling with Seismic Kinematics Metrics to Quantify Uncertainty for Pore Pressure Prediction

Bayesian Geophysical Basin Modeling with Seismic Kinematics Metrics to Quantify Uncertainty for Pore Pressure Prediction

Approximate Bayesian inference of seismic velocity and pore-pressure uncertainty with basin modeling, rock physics, and imaging constraints

Isotropic and anisotropic velocity-model building for subsalt seismic imaging

Contact Info

Product

Resources

About