Article
Geochemistry & Geophysics
Dario Grana, Leandro de Figueiredo, Klaus Mosegaard
Summary: Stochastic petrophysical inversion is a method to predict reservoir properties from seismic data, and recent advances in stochastic optimization allow generating multiple realizations of rock and fluid properties. This paper presents a Bayesian approach based on an efficient implementation of the Markov chain Monte Carlo (MCMC) method for seismic data inversion. The approach includes vertical and lateral correlation models, and is tested on 1D and extended to 2D problems. The results show the advantage of integrating a spatial correlation model.
Article
Computer Science, Interdisciplinary Applications
Yamei Cao, Hui Zhou, Bo Yu
Summary: A decorated linearized seismic-petrophysics inversion (DLSPI) method is proposed based on a linearized seismic-petrophysics model and principal component analysis (PCA). This method can improve the accuracy of petrophysical properties estimation by avoiding the statistical correlation between different parameters.
COMPUTERS & GEOSCIENCES
(2023)
Article
Geochemistry & Geophysics
Qiang Guo, Jing Ba, Li-Yun Fu, Cong Luo
Summary: Researchers have developed a novel inversion method for jointly estimating the elastic and petrophysical parameters of rocks from prestack seismic data. By combining a full rock-physics model and the exact Zoeppritz equation as the forward model, they have addressed the ill conditioning of the inverse problem and the complex prior distribution of model parameters with a regularization term based on a prior Gaussian mixture model under a Bayesian framework.
Article
Geochemistry & Geophysics
Kyle T. Spikes, Mrinal K. Sen
Summary: Correlations among inputs in rock-physics models are important for reducing uncertainty and defining reliable models. A Bayesian framework is used to identify correlations in two rock-physics models, using velocity and porosity measurements on carbonate samples. The results show that repeated Bayesian analysis reveals evident correlations among inputs, providing inputs for optimized models. The identified correlations should be used in relevant applications.
JOURNAL OF GEOPHYSICS AND ENGINEERING
(2022)
Review
Geochemistry & Geophysics
Dario Grana, Leonardo Azevedo, Leandro De Figueiredo, Patrick Connolly, Tapan Mukerji
Summary: Seismic reservoir characterization aims to predict rock and fluid properties based on seismic measurements, using geophysical models and mathematical methods. Seismic inversion estimates elastic properties, while rock-physics inversion estimates petrophysical properties. Deterministic and probabilistic methods can be applied to solve these problems.
Article
Geochemistry & Geophysics
Roberto Miele, Bernardo Viola Barreto, Paula Yamada, Luiz Eduardo S. Varella, Anderson L. Pimentel, Joao Felipe Costa, Leonardo Azevedo
Summary: Geostatistical seismic rock physics AVA inversion predicts rock and fluid properties iteratively by updating the rock physics model, overcoming limitations of calibration and well-log data.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Geochemistry & Geophysics
Amir Joolaei, Alireza Arab-Amiri, Ali Nejati
Summary: Traditionally, local deterministic optimization techniques have been used for nonlinear gravity inversion problems, but recently global optimization methods such as a hybrid of ICA and FA algorithm have shown promising results. This hybrid method improves exploratory capability and convergence rate, making it a potential alternative to local optimization techniques in highly nonlinear geophysical problems.
Article
Geochemistry & Geophysics
Qiang Guo, Cong Luo, Dario Grana
Summary: This paper introduces a rock-physics AVO inversion method for jointly estimating petrophysical and pore-geometry parameters in carbonate reservoirs. The method combines a linearized rock-physics model with Bayesian linear theory to derive an analytical solution to the inverse problem. The method is tested on benchmark data and compared with conventional methods, showing improved petrophysical results and advantages over two-step and nonlinear inversion methods.
Article
Geochemistry & Geophysics
Dario Grana, Brian Russell, Tapan Mukerji
Summary: This study uses canonical correlation analysis to infer the relationship between seismic data and petrophysical properties, and proposes a two-step inversion approach. This approach avoids the calibration of a rock-physics model and maximizes the correlation with petrophysical properties through parameterization. Additionally, a probabilistic method is introduced to propagate the uncertainty from the seismic to the petrophysical domain. The results from synthetic and real case studies demonstrate the high accuracy of this method compared to traditional approaches.
Article
Computer Science, Interdisciplinary Applications
Siddharth Garia, Arnab Kumar Pal, Shreya Katre, Satyabrata Nayak, K. Ravi, Archana M. Nair
Summary: This study integrates seismic and well log data using inversion driven by a laboratory-based rock physics model, aiming to reduce non-uniqueness in estimating reservoir properties. The results show a reasonable to high correlation between the derived density and porosity using well log and the laboratory-based model, indicating the effectiveness of the proposed approach in deriving realistic models for quantitative decision analysis.
EARTH SCIENCE INFORMATICS
(2023)
Article
Geosciences, Multidisciplinary
Bo Yu, Hui Zhou, Lingqian Wang, Wenling Liu
Summary: The study introduces a decorated Bayesian linearized inversion method to address statistical errors in prestack inversion. The decorrelated model parameters have zero cross-variograms, eliminating the need for cross-variogram estimation.
MATHEMATICAL GEOSCIENCES
(2021)
Article
Green & Sustainable Science & Technology
Bastien Dupuy, Anouar Romdhane, Peder Eliasson, Hong Yan
Summary: This study presents a two-step strategy combining geophysical and rock physics inversions for quantitative CO2 monitoring, using a Bayesian formulation to account for uncertainties. Demonstrated using data from the Sleipner CO2 storage project, the workflow involves deriving rock frame properties from baseline data and obtaining a 2D spatial distribution of CO2 saturation with uncertainty assessment. The study also highlights the need for advanced rock physics models and the recommendation of a joint rock physics inversion approach for better conformance verification.
INTERNATIONAL JOURNAL OF GREENHOUSE GAS CONTROL
(2021)
Article
Geosciences, Multidisciplinary
Rupeng Ma, Jing Ba, Jose Carcione, Maxim Lebedev, Changsheng Wang
Summary: The petrophysical properties are important indicators for identifying oil and gas reservoirs. Ultrasonic measurements show that properties with high fluid-sensitivity indicators can successfully distinguish between gas, oil, and water, while siltstones and dolomites can be identified based on data distribution areas.
FRONTIERS IN EARTH SCIENCE
(2021)
Article
Geochemistry & Geophysics
Haixia Zhao, Bangyu Wu, Jinghuai Gao
Summary: The simplified macro-equations of porous elastic media predict two types of compressional (P) wave and two types of shear (S) wave, with different impact of factors like permeability, porosity, and fluid viscosity on velocity dispersion and attenuation. The presence of high viscous fluid in heavy oil sand results in stronger dispersion and attenuation of waves compared to brine-saturated sandstone. Understanding the influence of fluid properties, particularly viscosity, is essential for explaining the behavior of solid-fluid combinations in seismic studies.
GEOPHYSICAL PROSPECTING
(2021)
Article
Computer Science, Artificial Intelligence
Vishal R. Ahuja, Utkarsh Gupta, Shivani R. Rapole, Nishank Saxena, Ronny Hofmann, Ruarri J. Day-Stirrat, Jaya Prakash, Phaneendra K. Yalavarthy
Summary: Digital Rock Physics utilizes digital image acquisition and analysis techniques to create 3D digital images of rock samples, which are used for computational modeling and simulations to predict petrophysical properties. We propose a deep learning-based Super-Resolution model called Siamese-SR to improve the resolution of Digital Rock images while retaining the texture and providing optimal denoising.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2022)
Article
Mathematics, Interdisciplinary Applications
Ralf Janicke, Beatriz Quintal, Fredrik Larsson, Kenneth Runesson
COMPUTATIONAL MECHANICS
(2019)
Article
Geochemistry & Geophysics
Eva Caspari, Mikhail Novikov, Vadim Lisitsa, Nicolas D. Barbosa, Beatriz Quintal, J. German Rubino, Klaus Holliger
GEOPHYSICAL PROSPECTING
(2019)
Article
Geochemistry & Geophysics
Amir Mollajan, Hossein Memarian, Beatriz Quintal
Article
Geochemistry & Geophysics
Yury Alkhimenkov, Eva Caspari, Boris Gurevich, Nicolas D. Barbosa, Stanislav Glubokovskikh, Jurg Hunziker, Beatriz Quintal
Article
Geochemistry & Geophysics
Simon Lissa, Nicolas D. Barbosa, Eva Caspari, Yury Alkhimenkov, Beatriz Quintal
JOURNAL OF GEOPHYSICAL RESEARCH-SOLID EARTH
(2020)
Article
Geochemistry & Geophysics
Yury Alkhimenkov, Ludovic Rass, Lyudmila Khakimova, Beatriz Quintal, Yury Podladchikov
Summary: Biot's equations describe the theory of poroelasticity with wide applications, requiring high-performance computing for numerical solutions. Dimensional analysis reduces material parameters needed for experiments and emphasizes key parameters governing wave propagation in poroelastic media. High efficiency numerical implementation allows for investigation of three-dimensional and high-resolution scenarios.
JOURNAL OF GEOPHYSICAL RESEARCH-SOLID EARTH
(2021)
Article
Geochemistry & Geophysics
Simon Lissa, Matthias Ruf, Holger Steeb, Beatriz Quintal
Summary: A workflow has been developed to compute seismic-wave moduli dispersion and attenuation due to squirt flow in cracked Carrara marble samples using micro X-ray computed tomography images. The study processed, segmented and meshed the images, and used a finite-element method to solve the Navier-Stokes equations for fluid flow in cracks. Results showed significant attenuation and dispersion of P and S waves caused by squirt flow.
Article
Geochemistry & Geophysics
Samuel Chapman, Jan V. M. Borgomano, Beatriz Quintal, Sally M. Benson, Jerome Fortin
Summary: This study investigates seismic attenuation in partially saturated rocks through experiments and numerical simulations. The results demonstrate significant attenuation and modulus dispersion due to the heterogeneous gas distribution during fluid pressure diffusion. The numerical solutions closely match the experimental data.
JOURNAL OF GEOPHYSICAL RESEARCH-SOLID EARTH
(2021)
Article
Geochemistry & Geophysics
Simon Lissa, Nicolas D. Barbosa, Beatriz Quintal
Summary: This study investigated the effects of fracture geometry on wave-induced fluid pressure diffusion by numerically computing seismic velocity and attenuation on models with realistic fracture geometries. Results show small discrepancies in the anisotropic behavior of P and S waves compared to a simple analytical model, with the exception of S wave attenuation. The dissipation caused by pressure gradients induced by S waves in mildly curved fractures suggests a potential method for inferring fracture hydraulic properties from S wave attenuation.
JOURNAL OF GEOPHYSICAL RESEARCH-SOLID EARTH
(2021)
Article
Geochemistry & Geophysics
Yury Alkhimenkov, Beatriz Quintal
Summary: Seismic wave propagation in porous rocks saturated with liquid exhibits dispersion and attenuation due to fluid flow at the pore scale, called squirt flow. Accurate quantitative description is crucial for inverting rock and fluid properties. Many analytical models for squirt flow based on simplified pore geometries have been proposed, but their accuracy is poor. In this study, a new analytical model for squirt flow is developed, validated against numerical solutions, and shown to provide accurate predictions for attenuation and dispersion across the frequency range.
Article
Geochemistry & Geophysics
Yury Alkhimenkov, Beatriz Quintal
Summary: Seismic waves exhibit strong attenuation and velocity dispersion in porous rocks due to fluid flow. We extend the classical pore geometry model and propose a new analytical model to characterize seismic attenuation and velocity dispersion more accurately.
Article
Geochemistry & Geophysics
Nisar Ahmed, Wiktor Waldemar Weibull, Beatriz Quintal, Dario Grana, Tuhin Bhakta
Summary: This paper presents a gradient descent optimization-based inversion technique to predict the unknown model properties in AVO data, including P- and S-wave velocities, quality factors and density. The proposed inversion method is tested on synthetic seismic data and reliably retrieves the unknown elastic and an-elastic properties.
GEOPHYSICAL JOURNAL INTERNATIONAL
(2023)
Article
Physics, Fluids & Plasmas
Santiago G. Solazzi, Beatriz Quintal, Klaus Holliger
Summary: This work presents a numerical approach to model the attenuation and modulus dispersion of compressional waves due to squirt flow in porous media saturated by Maxwell-type non-Newtonian fluids. The results show that wave signatures strongly depend on the Deborah number, with larger values leading to increased attenuation and a shift towards higher frequencies.
Article
Geochemistry & Geophysics
Yury Alkhimenkov, Eva Caspari, Simon Lissa, Beatriz Quintal
Article
Geochemistry & Geophysics
Simon Lissa, Nicolas D. Barbosa, J. German Rubino, Beatriz Quintal