Layer | Fill | Outline |
---|
Map layers
Theme | Visible | Selectable | Appearance | Zoom Range (now: 0) |
---|
Fill | Stroke |
---|---|
Collaborating Authors
Europe
Posterior sampling with convolutional neural network-based plug-and-play regularization with applications to poststack seismic inversion
Izzatullah, Muhammad (King Abdullah University of Science and Technology (KAUST)) | Alkhalifah, Tariq (King Abdullah University of Science and Technology (KAUST)) | Romero, Juan (King Abdullah University of Science and Technology (KAUST)) | Corrales, Miguel (King Abdullah University of Science and Technology (KAUST)) | Luiken, Nick (King Abdullah University of Science and Technology (KAUST)) | Ravasi, Matteo (King Abdullah University of Science and Technology (KAUST))
ABSTRACT Uncertainty quantification is a crucial component in any geophysical inverse problem, as it provides decision makers with valuable information about the inversion results. Seismic inversion is a notoriously ill-posed inverse problem, due to the band-limited and noisy nature of seismic data; as such, quantifying the uncertainties associated with the ill-posed nature of this inversion process is essential for qualifying the subsequent interpretation and decision-making processes. Selecting appropriate prior information is a crucial — yet nontrivial — step in probabilistic inversion because it influences the ability of sampling-based inference algorithms to provide geologically plausible posterior samples. However, the necessity to encapsulate prior knowledge into a probability distribution can greatly limit our ability to define expressive priors. To address this limitation and following in the footsteps of the plug-and-play (PnP) methodology for deterministic inversion, we develop a regularized variational inference framework that performs posterior inference by implicitly regularizing the Kullback-Leibler divergence loss — a measure of the distance between the approximated and target probabilistic distributions — with a convolutional neural network-based denoiser. We call this new algorithm PnP Stein variational gradient descent and determine its ability to produce high-resolution trustworthy samples that realistically represent subsurface structures. Our method is validated on synthetic and field poststack seismic data.
- 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 (1.00)
- Europe > Norway > North Sea > Central North Sea > South Viking Graben > PL 046 > Block 15/9 > Volve Field > Shetland Group > Åsgard Formation (0.99)
- Europe > Norway > North Sea > Central North Sea > South Viking Graben > PL 046 > Block 15/9 > Volve Field > Shetland Group > Svarte Formation (0.99)
- Europe > Norway > North Sea > Central North Sea > South Viking Graben > PL 046 > Block 15/9 > Volve Field > Shetland Group > Sleipner Formation (0.99)
- (17 more...)
- Information Technology > Artificial Intelligence > Representation & Reasoning > Uncertainty > Bayesian Inference (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Neural Networks > Deep Learning (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Learning Graphical Models > Directed Networks > Bayesian Learning (0.93)
Posterior sampling with CNN-based, Plug-and-Play regularization with applications to Post-Stack Seismic Inversion
Izzatullah, Muhammad (King Abdullah University of Science and Technology (KAUST)) | Alkhalifah, Tariq (King Abdullah University of Science and Technology (KAUST)) | Romero, Juan (King Abdullah University of Science and Technology (KAUST)) | Corrales, Miguel (King Abdullah University of Science and Technology (KAUST)) | Luiken, Nick (King Abdullah University of Science and Technology (KAUST)) | Ravasi, Matteo (King Abdullah University of Science and Technology (KAUST))
Uncertainty quantification is a crucial component in any geophysical inverse problem, as it provides decision-makers with valuable information about the inversion results. Seismic inversion is a notoriously ill-posed inverse problem, due to the band-limited and noisy nature of seismic data; as such, quantifying the uncertainties associated with the ill-posed nature of this inversion process is essential for qualifying the subsequent interpretation and decision-making processes. Selecting appropriate prior information is a crucialyet, non-trivialstep in probabilistic inversion since it influences the ability of sampling-based inference algorithms to provide geologically-plausible posterior samples. However, the necessity to encapsulate prior knowledge into a probability distribution can greatly limit our ability to define expressive priors. To address this limitation, and following in the footsteps of the Plug-and-Play methodology for deterministic inversion, we present a regularized variational inference framework that performs posterior inference by implicitly regularizing the Kullback-Leibler divergence lossa measure of the distance between the approximated and target probabilistic distributionswith a CNN-based denoiser. We call this new algorithm Plug-and-Play Stein Variational Gradient Descent (PnP-SVGD) and demonstrate its ability to produce high-resolution, trustworthy samples that realistically represent subsurface structures. The proposed method is validated on both synthetic and field post-stack seismic data.
- Overview (0.46)
- Research Report > New Finding (0.45)
- Geophysics > Seismic Surveying > Seismic Processing > Seismic Migration (1.00)
- Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling > Seismic Inversion (1.00)
- Europe > Norway > North Sea > Central North Sea > South Viking Graben > PL 046 > Block 15/9 > Volve Field > Shetland Group > Åsgard Formation (0.99)
- Europe > Norway > North Sea > Central North Sea > South Viking Graben > PL 046 > Block 15/9 > Volve Field > Shetland Group > Svarte Formation (0.99)
- Europe > Norway > North Sea > Central North Sea > South Viking Graben > PL 046 > Block 15/9 > Volve Field > Shetland Group > Sleipner Formation (0.99)
- (17 more...)
- Information Technology > Artificial Intelligence > Representation & Reasoning > Uncertainty > Bayesian Inference (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Neural Networks > Deep Learning (0.93)
- Information Technology > Artificial Intelligence > Machine Learning > Learning Graphical Models > Directed Networks > Bayesian Learning (0.93)
- Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning (0.89)
ABSTRACT Characterizing the elastic properties in deep-buried reservoirs beneath complex overburden structures remains challenging for seismic inversion. Elastic full-waveform inversion (FWI) is capable of quantitatively estimating the subsurface elastic properties with a reasonably high resolution. However, elastic FWI using high frequencies is computationally expensive because fine discretization is required to stabilize the wavefield simulation. In addition, it is challenging for elastic FWI to obtain the high-resolution inversion results of the deep targets due to complex overburden structures and limited energy illumination of the target of interest. To overcome these limitations, we propose a target-oriented high-resolution elastic FWI scheme by using the estimated elastic data for a virtual survey deployed only above a zone of interest. Specifically, we have developed an elastic redatuming approach to retrieve virtual elastic data by solving an iterative least-squares data-fitting problem. In addition to the elastic multicomponent data recorded at the acquisition surface, an estimate of the overburden model is required. A synthetic data example indicates that an overburden model estimated by elastic FWI with a low-frequency band enables our elastic redatuming approach to reconstruct the virtual elastic data representing the seismic reflection response from the target zone. We then perform regularized elastic FWI by using the redatumed elastic data to estimate the elastic properties in the target zone with reduced computational cost. In the numerical examples, the Marmousi2 synthetic data and 2D Volve field data are used to demonstrate the feasibility and practicality of the proposed method.
- Europe > Norway > North Sea > Central North Sea > South Viking Graben > PL 046 > Block 15/9 > Volve Field > Shetland Group > Åsgard Formation (0.99)
- Europe > Norway > North Sea > Central North Sea > South Viking Graben > PL 046 > Block 15/9 > Volve Field > Shetland Group > Svarte Formation (0.99)
- Europe > Norway > North Sea > Central North Sea > South Viking Graben > PL 046 > Block 15/9 > Volve Field > Shetland Group > Sleipner Formation (0.99)
- (20 more...)
ABSTRACT Time-lapse seismic data acquisition is an essential tool to monitor changes in a reservoir due to fluid injection, such as CO2 injection. By acquiring multiple seismic surveys in the exact same location, the authors can identify the reservoir changes by analyzing the difference in the data. However, such analysis can be skewed by the near-surface seasonal velocity variations, inaccuracy, and repeatability in the acquisition parameters, and other inevitable noise. The common practice (cross equalization) to address this problem uses the part of the data in which changes are not expected to design a matching filter and then apply it to the whole data, including the reservoir area. Like cross equalization, the authors train a recurrent neural network (RNN) on parts of the data excluding the reservoir area and then infer the reservoir-related data. The RNN can learn the time dependency of the data, unlike the matching filter that processes the data based on the local information obtained in the filter window. The authors determine the method of matching the data in various examples and compare it with the conventional matching filter. Specifically, we start by demonstrating the ability of the approach in matching two traces and then test the method on a prestack 2D synthetic data. Then, the authors verify the enhancements of the 4D signal by providing reverse time migration images. The authors measure the repeatability using normalized root-mean-square and predictability metrics and find that, in some cases, our proposed method performed better than the matching filter approach.
- Asia > Middle East > Saudi Arabia (0.68)
- North America (0.68)
- Geophysics > Time-Lapse Surveying > Time-Lapse Seismic Surveying (1.00)
- Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling (1.00)
- Geophysics > Seismic Surveying > Seismic Processing > Seismic Migration (0.86)
- Europe > Norway > North Sea > Central North Sea > Central Graben > Block 2/8 > Valhall Field > Tor Formation (0.99)
- Europe > Norway > North Sea > Central North Sea > Central Graben > Block 2/8 > Valhall Field > Hod Formation (0.99)
- Europe > Norway > North Sea > Central North Sea > Central Graben > Block 2/11 > Valhall Field > Tor Formation (0.99)
- Europe > Norway > North Sea > Central North Sea > Central Graben > Block 2/11 > Valhall Field > Hod Formation (0.99)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Four-dimensional and four-component seismic (1.00)
- Data Science & Engineering Analytics > Information Management and Systems > Neural networks (1.00)
ABSTRACT Full-waveform inversion (FWI) is popularly used to obtain a high-resolution subsurface velocity model. However, it requires either a good initial velocity model or low-frequency data to mitigate the cycle-skipping issue. Reflection-waveform inversion (RWI) uses a migration/demigration process to retrieve a background model that can be used as a good initial velocity in FWI. The drawback of conventional RWI is that it requires the use of least-squares migration, which is often computationally expensive, and it is still prone to cycle skipping at far offsets. To improve the computational efficiency and overcome cycle skipping in the original RWI, we have incorporated it into a recently introduced method called efficient wavefield inversion (EWI) by inverting for the Born-scattered wavefield instead of the wavefield itself. In this case, we use perturbation-related secondary sources in the modified source function. Unlike conventional RWI, the perturbations are calculated naturally as part of the calculation of the scattered wavefield in an efficient way. Because the sources in the reflection-based EWI (REWI) are located in the subsurface, we are able to update the background model along the reflection wavepath. In the background velocity inversion, we calculate the background perturbation by a deconvolution process at each frequency. After obtaining the REWI inverted velocity model, a sequential FWI or EWI is needed to obtain a high-resolution model. We determine the validity of our approach using synthetic data generated from a section of the Sigsbee2A model. To further demonstrate the effectiveness of our approach, we test it on an ocean-bottom cable data set from the North Sea. We find that our methodology leads to improved velocity models as evidenced by flatter angle gathers.
- Europe > Norway > North Sea > Central North Sea > South Viking Graben > PL 046 > Block 15/9 > Volve Field > Shetland Group > Åsgard Formation (0.99)
- Europe > Norway > North Sea > Central North Sea > South Viking Graben > PL 046 > Block 15/9 > Volve Field > Shetland Group > Svarte Formation (0.99)
- Europe > Norway > North Sea > Central North Sea > South Viking Graben > PL 046 > Block 15/9 > Volve Field > Shetland Group > Sleipner Formation (0.99)
- (17 more...)
ABSTRACT Reservoir characterization is an essential component of oil and gas production, as well as exploration. Classic reservoir characterization algorithms, deterministic and stochastic, are typically based on stacked images and rely on simplifications and approximations to the subsurface (e.g., assuming linearized reflection coefficients). Elastic full-waveform inversion (FWI), which aims to match the waveforms of prestack seismic data, potentially provides more accurate high-resolution reservoir characterization from seismic data. However, FWI can easily fail to characterize deep-buried reservoirs due to illumination limitations. We have developed a deep learning-aided elastic FWI strategy using observed seismic data and available well logs in the target area. Five facies are extracted from the well and then connected to the inverted P- and S-wave velocities using trained neural networks, which correspond to the subsurface facies distribution. Such a distribution is further converted to the desired reservoir-related parameters such as velocities and anisotropy parameters using a weighted summation. Finally, we update these estimated parameters by matching the resulting simulated wavefields to the observed seismic data, which corresponds to another round of elastic FWI aided by the a priori knowledge gained from the predictions of machine learning. A North Sea field data example, the Volve Oil Field data set, indicates that the use of facies as prior knowledge helps resolve the deep-buried reservoir target better than the use of only seismic data.
- Geophysics > Seismic Surveying > Seismic Processing (1.00)
- Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling > Seismic Inversion (1.00)
- Europe > Norway > North Sea > Central North Sea > South Viking Graben > PL 046 > Block 15/9 > Volve Field > Shetland Group > Åsgard Formation (0.99)
- Europe > Norway > North Sea > Central North Sea > South Viking Graben > PL 046 > Block 15/9 > Volve Field > Shetland Group > Svarte Formation (0.99)
- Europe > Norway > North Sea > Central North Sea > South Viking Graben > PL 046 > Block 15/9 > Volve Field > Shetland Group > Sleipner Formation (0.99)
- (17 more...)
ABSTRACT Full-waveform inversion (FWI) in its classic form is a method based on minimizing the norm of the difference between the observed and simulated seismic waveforms at the receiver locations. The objective is to find a subsurface model that reproduces the full waveform including the traveltimes and amplitudes of the observed seismic data. However, the widely used -norm-based FWI faces many issues in practice. The point-wise comparison of waveforms fails when the phase difference between the compared waveforms of the predicted and observed data is larger than a half-cycle. In addition, amplitude matching is impractical considering the simplified physics that we often use to describe the medium. To avoid these known problems, we have developed a novel elastic FWI algorithm using the local-similarity attribute. It compares two traces within a predefined local time extension; thus, is not limited by the half-cycle criterion. The algorithm strives to maximize the local similarities of the predicted and observed data by stretching/squeezing the observed data. Phases instead of amplitudes of the seismic data are used in the comparison. The algorithm compares two data sets locally; thus, it performs better than the global correlation in matching multiple arrivals. Instead of picking/calculating one stationary stretching/squeezing curve, we used a weighted integral to find all possible stationary curves. We also introduced a polynomial-type weighting function, which is determined only by the predefined maximum stretching/squeezing and is guaranteed to be smoothly varying within the extension range. Compared with the previously used Gaussian or linear weighting functions, our polynomial one has fewer parameters to play around with. A modified synthetic elastic Marmousi model and the North Sea field data are used to verify the effectiveness of the developed approach and also reveal some of its limitations.
- Europe > Norway > North Sea > Central North Sea > South Viking Graben > PL 046 > Block 15/9 > Volve Field > Shetland Group > Åsgard Formation (0.99)
- Europe > Norway > North Sea > Central North Sea > South Viking Graben > PL 046 > Block 15/9 > Volve Field > Shetland Group > Svarte Formation (0.99)
- Europe > Norway > North Sea > Central North Sea > South Viking Graben > PL 046 > Block 15/9 > Volve Field > Shetland Group > Sleipner Formation (0.99)
- (17 more...)
ABSTRACT Reservoir characterization is an important component of oil and gas production, as well as prediction. Classic reservoir characterization algorithms, both deterministic and stochastic, are typically based on stacked images and rely on simplifications and approximations to the subsurface. Elastic fullwaveform inversion, which aims to match the waveforms of pre-stack seismic data, can potentially provide more accurate high-resolution reservoir characterization from seismic data. However, full-waveform inversion can easily fail to characterize deep-buried reservoirs with strong anisotropic seals. We present a deep learning aided elastic full-waveform inversion strategy using observed seismic data and well logs available in the target area. Five facies are extracted from the well and then connected to the inverted P- and S-wave velocities using the trained neural networks, which corresponds to the distribution of facies in the subsurface. Such a distribution is further converted to the desired reservoir-related parameters such as velocities and anisotropy parameters using a proposed weighted summation. Finally, we further update these estimated parameters by matching the resulting simulated wavefields to the observed seismic data, which corresponds to another round of elastic full-waveform inversion. A North Sea field data example, the Volve Oil Field data set, is used to demonstrate our proposed method. Presentation Date: Wednesday, September 18, 2019 Session Start Time: 8:30 AM Presentation Time: 11:00 AM Location: 225B Presentation Type: Oral
- Geophysics > Seismic Surveying > Seismic Processing > Seismic Migration (1.00)
- Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling > Seismic Inversion (1.00)
- Europe > Norway > North Sea > Central North Sea > South Viking Graben > PL 046 > Block 15/9 > Volve Field > Shetland Group > Åsgard Formation (0.99)
- Europe > Norway > North Sea > Central North Sea > South Viking Graben > PL 046 > Block 15/9 > Volve Field > Shetland Group > Svarte Formation (0.99)
- Europe > Norway > North Sea > Central North Sea > South Viking Graben > PL 046 > Block 15/9 > Volve Field > Shetland Group > Sleipner Formation (0.99)
- (17 more...)
Regularized full-waveform inversion with automated salt flooding
Kalita, Mahesh (King Abdullah University of Science and Technology) | Kazei, Vladimir (King Abdullah University of Science and Technology) | Choi, Yunseok (King Abdullah University of Science and Technology, Korea Institute of Geoscience and Mineral Resources) | Alkhalifah, Tariq (King Abdullah University of Science and Technology)
ABSTRACT Full-waveform inversion (FWI) attempts to resolve an ill-posed nonlinear optimization problem to retrieve the unknown subsurface model parameters from seismic data. In general, FWI fails to obtain an adequate representation of models with large high-velocity structures over a wide region, such as salt bodies and the sediments beneath them, in the absence of low frequencies in the recorded seismic signal, due to nonlinearity and nonuniqueness. We alleviate the ill posedness of FWI associated with data sets affected by salt bodies using model regularization. We have split the optimization problem into two parts: First, we minimize the data misfit and the total variation in the model, seeking to achieve an inverted model with sharp interfaces; and second, we minimize sharp velocity drops with depth in the model. Unlike conventional industrial salt flooding, our technique requires minimal human intervention and no information about the top of the salt. Those features are demonstrated on data sets of the BP 2004 and Sigsbee2A models, synthesized from a Ricker wavelet of dominant frequency 5.5 Hz and minimum frequency 3 Hz. We initiate the inversion process with a simple model in which the velocity increases linearly with depth. The model is well-retrieved when the same constant density acoustic code is used to simulate the observed data, which is still one of the most common FWI tests. Moreover, our technique allows us to reconstruct a reasonable depiction of the salt structure from the data synthesized independently with the BP 2004 model with variable density. In the Sigsbee2A model, we manage to even capture some of the fine layering beneath the salt. In addition, we evaluate the versatility of our method on a field data set from the Gulf of Mexico.
- North America > United States (0.34)
- North America > Mexico (0.24)
- Geophysics > Seismic Surveying > Seismic Processing (1.00)
- Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling > Seismic Inversion (0.85)
- Europe > Norway > North Sea > Central North Sea > Central Graben > Block 2/8 > Valhall Field > Tor Formation (0.99)
- Europe > Norway > North Sea > Central North Sea > Central Graben > Block 2/8 > Valhall Field > Hod Formation (0.99)
- Europe > Norway > North Sea > Central North Sea > Central Graben > Block 2/11 > Valhall Field > Tor Formation (0.99)
- Europe > Norway > North Sea > Central North Sea > Central Graben > Block 2/11 > Valhall Field > Hod Formation (0.99)
Elastic reflection waveform inversion with variable density
Li, Yuanyuan (China University of Petroleum, King Abdullah University of Science and Technology) | Guo, Qiang (King Abdullah University of Science and Technology) | Li, Zhenchun (China University of Petroleum) | Alkhalifah, Tariq (King Abdullah University of Science and Technology)
ABSTRACT Elastic full-waveform inversion (FWI) provides a better description of the subsurface information than those given by the acoustic assumption. However, it suffers from a more serious cycle-skipping problem compared with the latter. Reflection waveform inversion (RWI) is able to build a good background model, which can serve as an initial model for elastic FWI. Because, in RWI, we use the model perturbation to explicitly fit reflections, such perturbations should include density, which mainly affects the dynamics. We applied Born modeling to generate synthetic reflection data using optimized perturbations of the P- and S-wave velocities and density. The inversion for the perturbations of the P- and S-wave velocities and density is similar to elastic least-squares reverse time migration. An incorrect background model will lead to misfits mainly at the far offsets, which can be used to update the background P- and S-wave velocities along the reflection wavepath. We optimize the perturbations and background models in an alternate way. We use two synthetic examples and a field-data case to demonstrate our proposed elastic RWI algorithm. The results indicate that our elastic RWI with variable density is able to build reasonably good background models for elastic FWI with the absence of low frequencies, and it can deal with the variable density, which is required in real cases.
- Geophysics > Seismic Surveying > Seismic Processing > Seismic Migration (1.00)
- Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling > Seismic Inversion (1.00)
- Europe > Norway > North Sea > Central North Sea > South Viking Graben > PL 046 > Block 15/9 > Volve Field > Shetland Group > Åsgard Formation (0.99)
- Europe > Norway > North Sea > Central North Sea > South Viking Graben > PL 046 > Block 15/9 > Volve Field > Shetland Group > Svarte Formation (0.99)
- Europe > Norway > North Sea > Central North Sea > South Viking Graben > PL 046 > Block 15/9 > Volve Field > Shetland Group > Sleipner Formation (0.99)
- (23 more...)