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Collaborating Authors
Simulation
Investigating the causes of permeability anisotropy in heterogeneous conglomeratic sandstone using multiscale digital rock
Chi, Peng (China University of Petroleum (East China), China University of Petroleum (East China)) | Sun, Jianmeng (China University of Petroleum (East China), China University of Petroleum (East China)) | Yan, Weichao (Ocean University of China, Ocean University of China) | Luo, Xin (China University of Petroleum (East China), China University of Petroleum (East China)) | Ping, Feng (Southern University of Science and Technology)
Heterogeneous conglomeratic sandstone exhibits anisotropic physical properties, rendering a comprehensive analysis of its physical processes challenging with experimental measurements. Digital rock technology provides a visual and intuitive analysis of the microphysical processes in rocks, thereby aiding in scientific inquiry. Nevertheless, the multiscale characteristics of conglomeratic sandstone cannot be fully captured by a single-scale digital rock, thus limiting its ability to characterize the pore structure. Our work introduces a proposed workflow that employs multiscale digital rock fusion to investigate permeability anisotropy in heterogeneous rock. We utilize a cycle-consistent generative adversarial network (CycleGAN) to fuse CT scans data of different resolutions, creating a large-scale, high-precision digital rock that comprehensively represents the conglomeratic sandstone pore structure. Subsequently, the digital rock is partitioned into multiple blocks, and the permeability of each block is simulated using a pore network. Finally, the total permeability of the sample is calculated by conducting an upscaling numerical simulation using the Darcy-Stokes equation. This process facilitates the analysis of the pore structure in conglomeratic sandstone and provides a step-by-step solution for permeability. From a multiscale perspective, this approach reveals that the anisotropy of permeability in conglomeratic sandstone stems from the layered distribution of grain sizes and differences in grain arrangement across different directions.
- Europe > Norway > North Sea > Central North Sea > Utsira High > PL 338 > Block 16/1 > Edvard Grieg Field > Åsgard Formation (0.99)
- Europe > Norway > North Sea > Central North Sea > Utsira High > PL 338 > Block 16/1 > Edvard Grieg Field > Skagerrak Formation (0.99)
- Europe > Norway > North Sea > Central North Sea > Utsira High > PL 338 > Block 16/1 > Edvard Grieg Field > Hegre Formation (0.99)
- (3 more...)
Power-law frequency-dependent Q simulations in viscoacoustic media using decoupled fractional Laplacians
Zhang, Yabing (China University of Mining and Technology) | Zhu, Hejun (The University of Texas at Dallas) | Liu, Yang (China University of Petroleum (Beijing)) | Chen, Tongjun (China University of Mining and Technology)
Quantifying seismic attenuation of wave propagation in the Earths interior is essential for studying subsurface structures. Previous approaches for attenuation simulations (e.g., the standard linear solid and the fractional derivative model) are mainly based on the frequency-independent quality factor Q assumption. However, seismic attenuation in high-temperature and high-pressure regions usually exhibits power-law frequency-dependent Q characteristics. To simulate this Q effect in attenuative media, we derive a new viscoacoustic wave equation with decoupled fractional Laplacians in the time domain. Unlike the existing methods using relaxation functions to fit the power-law relationship in a specific frequency band, our proposed equation is directly derived from the approximated complex modulus, which explicitly involves the reference quality factor and fractional exponent parameters. Furthermore, this equation contains two fractional Laplacians, which can easily simulate decoupled amplitude dissipation and phase distortion effects, making it amenable to Q-compensated reverse-time migration. In the implementation, a Taylor-series expansion and a pseudo-spectral method are introduced to solve the fractional Laplacians with variable fractional exponents. Numerical experiments demonstrate the effectiveness of the proposed method for power-law frequency-dependent Q simulations. As a forward-modeling engine, our derived viscoacoustic wave equation is a good supplement to the current Q simulation methods and it could be applied in many seismic applications, including Q-compensated reverse time migration and full-waveform inversion.
Complex-valued adaptive-coefficient finite-difference frequency-domain method for wavefield modeling based on the diffusive-viscous wave equation
Zhao, Haixia (Xi’an Jiaotong University, National Engineering Research Center of Offshore Oil and Gas Exploration) | Wang, Shaoru (Xi’an Jiaotong University) | Xu, Wenhao (Hohai University) | Gao, Jinghuai (Xi’an Jiaotong University, National Engineering Research Center of Offshore Oil and Gas Exploration)
ABSTRACT The diffusive-viscous wave (DVW) equation is an effective model for analyzing seismic low-frequency anomalies and attenuation in porous media. To effectively simulate DVW wavefields, the finite-difference or finite-element method in the time domain is favored, but the time-domain approach proves less efficient with multiple shots or a few frequency components. The finite-difference frequency-domain (FDFD) method featuring optimal or adaptive coefficients is favored in seismic simulations due to its high efficiency. Initially, we develop a real-valued adaptive-coefficient (RVAC) FDFD method for the DVW equation, which ignores the numerical attenuation error and is a generalization of the acoustic adaptive-coefficient FDFD method. To reduce the numerical attenuation error of the RVAC FDFD method, we introduce a complex-valued adaptive-coefficient (CVAC) FDFD method for the DVW equation. The CVAC FDFD method is constructed by incorporating correction terms into the conventional second-order FDFD method. The adaptive coefficients are related to the spatial sampling ratio, number of spatial grid points per wavelength, and diffusive and viscous attenuation coefficients in the DVW equation. Numerical dispersion and attenuation analysis confirm that, with a maximum dispersion error of 1% and a maximum attenuation error of 10%, the CVAC FDFD method only necessitates 2.5 spatial grid points per wavelength. Compared with the RVAC FDFD method, the CVAC FDFD method exhibits enhanced capability in suppressing the numerical attenuation during anelastic wavefield modeling. To validate the accuracy of our method, we develop an analytical solution for the DVW equation in a homogeneous medium. Three numerical examples substantiate the high accuracy of the CVAC FDFD method when using a small number of spatial grid points per wavelength, and this method demands computational time and computer memory similar to those required by the conventional second-order FDFD method. A fluid-saturated model featuring various layer thicknesses is used to characterize the propagation characteristics of DVW.
- Geophysics > Seismic Surveying > Seismic Processing (1.00)
- Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling > Seismic Inversion (0.93)
- Geophysics > Seismic Surveying > Seismic Interpretation (0.93)
- Information Technology > Artificial Intelligence > Machine Learning (0.46)
- Information Technology > Hardware > Memory (0.34)
ABSTRACT Gassmann’s equations have been known for several decades and are widely used in geophysics. These equations are treated as exact if all the assumptions used in their derivation are fulfilled. However, a recent theoretical study claimed that Gassmann’s equations contain an error. Shortly after that, a 3D numerical calculation was performed on a simple pore geometry that verifies the validity of Gassmann’s equations. This pore geometry was simpler than those in real rocks but arbitrary. Furthermore, the pore geometry that was used did not contain any special features (among all possible geometries) that were tailored to make it consistent with Gassmann’s equations. In other recent studies, I also performed numerical calculations on several other more complex pore geometries that supported the validity of Gassmann’s equations. To further support the validity of these equations, I provide here one more convergence study using a more realistic geometry of the pore space. Given that there are several studies that rederive Gassmann’s equations using different methods and numerical studies that verify them for different pore geometries, it can be concluded that Gassmann’s equations can be used in geophysics without concern if their assumptions are fulfilled. MATLAB routines to reproduce the presented results are provided.
- North America > United States > Massachusetts (0.29)
- Europe (0.29)
Artificial intelligence (AI) is increasingly being employed to assist in the development of materials, including metal-organic frameworks (MOFs), to develop carbon capture technologies. MOFs are modular materials made up of three building blocks: inorganic nodes such as zinc or copper; organic nodes; and organic linkers made up of carbon, oxygen, and other elements. By changing the relative positions and configurations of the building blocks, the potential combinations for creation of unique MOFs are countless. The idea is to create a porous carbon dioxide "trap" to capture carbon from the air. The structure created by the building blocks can be thought of simplistically as a scaffolding with joints (linkers) that functions to absorb carbon.
- Energy > Power Industry (0.32)
- Government > Regional Government (0.31)
Probabilistic physics-informed neural network for seismic petrophysical inversion
Li, Peng (University of Wyoming) | Liu, Mingliang (Stanford University) | Alfarraj, Motaz (King Fahd University of Petroleum and Minerals, King Fahd University of Petroleum and Minerals) | Tahmasebi, Pejman (Colorado School of Mines) | Grana, Dario (University of Wyoming)
ABSTRACT The main challenge in the inversion of seismic data to predict the petrophysical properties of hydrocarbon-saturated rocks is that the physical relations that link the data to the model properties often are nonlinear and the solution of the inverse problem is generally not unique. As a possible alternative to traditional stochastic optimization methods, we develop a method to adopt machine-learning algorithms by estimating relations between data and unknown variables from a training data set with limited computational cost. We develop a probabilistic approach for seismic petrophysical inversion based on physics-informed neural network (PINN) with a reparameterization network. The novelty of our approach includes the definition of a PINN algorithm in a probabilistic setting, the use of an additional neural network (NN) for rock-physics model hyperparameter estimation, and the implementation of approximate Bayesian computation to quantify the model uncertainty. The reparameterization network allows us to include unknown model parameters, such as rock-physics model hyperparameters. Our method predicts the most likely model of petrophysical variables based on the input seismic data set and the training data set and provides a quantification of the uncertainty of the model. The method is scalable and can be adapted to various geophysical inverse problems. We test the inversion on a North Sea data set with poststack and prestack data to obtain the prediction of petrophysical properties. Compared with regular NNs, the predictions of our method indicate higher accuracy in the predicted results and allow us to quantify the posterior uncertainty.
- Asia > Middle East > Saudi Arabia (0.28)
- North America > United States > Colorado (0.28)
- North America > United States > California (0.28)
- (4 more...)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock (0.94)
- Geology > Geological Subdiscipline > Geomechanics (0.89)
- Geophysics > Seismic Surveying > Seismic Processing > Seismic Migration (1.00)
- Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling > Seismic Inversion (1.00)
- Geophysics > Seismic Surveying > Seismic Interpretation > Seismic Reservoir Characterization > Amplitude vs Offset (AVO) (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)
- Data Science & Engineering Analytics > Information Management and Systems > Neural networks (1.00)
- Data Science & Engineering Analytics > Information Management and Systems > Artificial intelligence (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Neural Networks (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Learning Graphical Models > Directed Networks > Bayesian Learning (0.88)
- Information Technology > Artificial Intelligence > Representation & Reasoning > Uncertainty > Bayesian Inference (0.66)
Complex-valued adaptive-coefficient finite-difference frequency-domain method for wavefield modeling based on the diffusive-viscous wave equation
Zhao, Haixia (Xi’an Jiaotong University, National Engineering Research Center of Offshore Oil and Gas Exploration) | Wang, Shaoru (Xi’an Jiaotong University) | Xu, Wenhao (Hohai University) | Gao, Jinghuai (Xi’an Jiaotong University, National Engineering Research Center of Offshore Oil and Gas Exploration)
ABSTRACT The diffusive-viscous wave (DVW) equation is an effective model for analyzing seismic low-frequency anomalies and attenuation in porous media. To effectively simulate DVW wavefields, the finite-difference or finite-element method in the time domain is favored, but the time-domain approach proves less efficient with multiple shots or a few frequency components. The finite-difference frequency-domain (FDFD) method featuring optimal or adaptive coefficients is favored in seismic simulations due to its high efficiency. Initially, we develop a real-valued adaptive-coefficient (RVAC) FDFD method for the DVW equation, which ignores the numerical attenuation error and is a generalization of the acoustic adaptive-coefficient FDFD method. To reduce the numerical attenuation error of the RVAC FDFD method, we introduce a complex-valued adaptive-coefficient (CVAC) FDFD method for the DVW equation. The CVAC FDFD method is constructed by incorporating correction terms into the conventional second-order FDFD method. The adaptive coefficients are related to the spatial sampling ratio, number of spatial grid points per wavelength, and diffusive and viscous attenuation coefficients in the DVW equation. Numerical dispersion and attenuation analysis confirm that, with a maximum dispersion error of 1% and a maximum attenuation error of 10%, the CVAC FDFD method only necessitates 2.5 spatial grid points per wavelength. Compared with the RVAC FDFD method, the CVAC FDFD method exhibits enhanced capability in suppressing the numerical attenuation during anelastic wavefield modeling. To validate the accuracy of our method, we develop an analytical solution for the DVW equation in a homogeneous medium. Three numerical examples substantiate the high accuracy of the CVAC FDFD method when using a small number of spatial grid points per wavelength, and this method demands computational time and computer memory similar to those required by the conventional second-order FDFD method. A fluid-saturated model featuring various layer thicknesses is used to characterize the propagation characteristics of DVW.
- Geophysics > Seismic Surveying > Seismic Processing (1.00)
- Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling > Seismic Inversion (0.93)
- Geophysics > Seismic Surveying > Seismic Interpretation (0.93)
- Information Technology > Artificial Intelligence > Machine Learning (0.46)
- Information Technology > Hardware > Memory (0.34)
ABSTRACT Gassmann’s equations have been known for several decades and are widely used in geophysics. These equations are treated as exact if all the assumptions used in their derivation are fulfilled. However, a recent theoretical study claimed that Gassmann’s equations contain an error. Shortly after that, a 3D numerical calculation was performed on a simple pore geometry that verifies the validity of Gassmann’s equations. This pore geometry was simpler than those in real rocks but arbitrary. Furthermore, the pore geometry that was used did not contain any special features (among all possible geometries) that were tailored to make it consistent with Gassmann’s equations. In other recent studies, I also performed numerical calculations on several other more complex pore geometries that supported the validity of Gassmann’s equations. To further support the validity of these equations, I provide here one more convergence study using a more realistic geometry of the pore space. Given that there are several studies that rederive Gassmann’s equations using different methods and numerical studies that verify them for different pore geometries, it can be concluded that Gassmann’s equations can be used in geophysics without concern if their assumptions are fulfilled. MATLAB routines to reproduce the presented results are provided.
- North America > United States > Massachusetts (0.29)
- Europe (0.29)
ABSTRACT Unlike the common situation for which vertical wells penetrate horizontal layers, the trajectory of high-angle wells is usually not aligned with the principal axes of elastic rock properties. Borehole sonic measurements acquired in high-angle wells in general do not exhibit axial symmetry in the vicinity of bed boundaries and thin layers, and sonic waveforms remain strongly affected by the corresponding contrast in elastic properties across bed boundaries. The latter conditions often demand sophisticated and time-consuming numerical modeling to reliably interpret borehole sonic measurements into rock elastic properties. The problem is circumvented by implementing the eikonal equation based on the fast marching method to (1) calculate first-arrival times of borehole acoustic waveforms and (2) trace raypaths between sonic transmitters and receivers in high-angle wells. Furthermore, first-arrival times of P and S waves are calculated at different azimuthal receivers included in wireline borehole sonic instruments and are verified against waveforms obtained via 3D finite-difference time-domain simulations. Calculations of traveltimes, wavefronts, and raypaths for challenging synthetic examples with effects due to formation anisotropy and different inclination angles indicate a transition from a head wave to a boundary-induced refracted wave as the borehole sonic instrument moves across bed boundaries. Apparent slownesses obtained from first-arrival times at receivers can be faster or slower than the actual slownesses of rock formations surrounding the borehole, depending on formation dip, azimuth, anisotropy, and bed boundaries. Differences in apparent acoustic slownesses measured by adjacent azimuthal receivers reflect the behavior of wave propagation within the borehole and across bed boundaries and can be used to estimate bed-boundary orientation and anisotropy. The high-frequency approximation of traveltimes obtained with the eikonal equation saves more than 99% of calculation time with acceptable numerical errors, with respect to rigorous time-domain numerical simulation of the wave equation, and is therefore amenable to inversion-based measurement interpretation. Apparent slownesses extracted from acoustic arrival times suggest a potential method for estimating formation elastic properties and inferring boundary geometries.
- Geophysics > Borehole Geophysics (1.00)
- Geophysics > Seismic Surveying > Seismic Processing (0.93)
- Well Drilling > Well Planning > Trajectory design (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Exploration, development, structural geology (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management > Open hole/cased hole log analysis (1.00)
ABSTRACT Thin and highly conductive objects are challenging to model in 3D direct-current (DC) problems because they often require excessive mesh refinement that leads to a significant increase in computational costs. RESistor network (RESnet) is a novel algorithm that converts any 3D geo-electric simulation to solving an equivalent 3D resistor network circuit. Two features of RESnet make it an attractive choice in the DC modeling of thin and conductive objects. First, in addition to the conductivity with units of Siemens per meter (S/m) defined at the cell centers (cell conductivity), RESnet allows conductive properties defined on mesh faces and edges as face conductivity with units of S and edge conductivity with units of S·m, respectively. Face conductivity is the thickness-integrated conductivity, which preserves the electric effect of sheet-like conductors without an explicit statement in the mesh. Similarly, edge conductivity is the product of the cross-sectional area and the intrinsic conductivity of a line-like conductive object. Modeling thin objects using face and edge conductivity can avoid extremely small mesh grids if the DC problem concerns electric field responses at a much larger scale. Second, once the original simulation is transformed into an equivalent resistor network, certain types of infrastructure, similar to above-ground metallic pipes, can be conveniently modeled by directly connecting the circuit nodes, which cannot interact with each other in conventional modeling programs. Bilingually implemented in MATLAB and Python, the algorithm has been made open source to promote wide use in academia and industry. Three examples are provided to validate its numerical accuracy, demonstrate its capability in modeling steel well casings, and indicate how it can be used to simulate the effect of complex metallic infrastructure on DC resistivity data.