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Collaborating Authors
Colorado School of Mines
P-wave propagation in attenuative elliptical VTI media
Hao, Qi (Jilin University) | Tsvankin, Ilya (Colorado School of Mines)
Elliptical anisotropy is convenient to use as the reference medium in perturbation methods designed to study P-wave propagation for transverse isotropy (TI). We make the elliptically anisotropic TI model attenuative and discuss the corresponding P-wave dispersion relation and the wave equation. Our analysis leads to two conditions in terms of the Thomsen type parameters, which guarantee that the P-wave slowness surface and the dispersion relation satisfy elliptical equations. We also obtain the viscoacoustic wave equation for such elliptically anisotropic media and solve it for point-source radiation using the correspondence principle. For the constant-Q TI model, we use the weighting function method to derive the viscoacoustic wave equation in differential form. Numerical examples validate the proposed elliptical conditions and illustrate the behavior of the P-wavefield in attenuative elliptical TI models.
Simultaneous source acquisition has become common over the past few decades for marine seismic surveys because of the increased efficiency of seismic acquisition by limiting the time, reducing the cost, and having less environmental impact than conventional single-source (or unblended) acquisition surveys. For simultaneous source acquisition, seismic sources at different locations are fired with time delays, and the recorded data are referred to as the blended data. The air-water interface (or free surface) creates strong multiples and ghost reflections for blended seismic data. The multiples and/or ghost reflections caused by a source in the blended data overlap with the primary reflections of another source, thus creating a strong interference between the primary and multiple events of different sources. We develop a convolutional neural network (CNN) method to attenuate free-surface multiples and remove ghost reflections simultaneously from the blended seismic data. The CNN-based solution that we develop operates on single traces and is not sensitive to the missing near-offset traces, missing traces, and irregular/sparse acquisition parameters (e.g.,for ocean-bottom node acquisition and time-lapse monitoring studies). We illustrate the efficacy of our free-surface multiple attenuation and seismic deghosting method by presenting synthetic and field data applications. The numerical experiments demonstrate that our CNN-based approach for simultaneously attenuating free-surface multiples and removing ghost reflections can be applied to the blended data without the deblending step. Although the interference of primaries and multiples from different shots in the blended data makes free-surface multiple attenuation harder than the unblended data, we determine that our CNN-based method effectively attenuates free-surface multiples in the blended synthetic and field data even when the delay time for the blending is different in the training data than in the data to which the CNN is applied.
- North America > United States > Illinois > Madison County (0.34)
- Europe > United Kingdom > North Sea (0.29)
- Europe > Norway > North Sea (0.29)
- North America > United States > Colorado (0.28)
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)
Marchenko imaging assisted by vertical seismic profiling data for land seismic data in the Middle East
Cheng, Liwei (Colorado School of Mines, ExxonMobil Technology and Engineering Company) | Tura, Ali (Colorado School of Mines) | Simmons, James (Colorado School of Mines) | Snieder, Roel (Colorado School of Mines) | Angelov, Petar Vladov (Kuwait Oil Company, Qatar Energy) | Narhari Srinivasa, Rao (Kuwait Oil Company) | Akther, Shamima (Kuwait Oil Company)
ABSTRACT Attenuating interference from internal multiples has challenged seismic data imaging in the Middle East basins. The challenge results from the strong short-period internal multiples that exhibit nearly indistinguishable characteristics from the primaries reflected from the underlying reservoirs due to predominantly horizontal strata and occasional low-relief structures, as indicated in the Jurassic formations in Kuwait. To address the internal-multiple issues, multiple prediction followed by adaptive subtraction is the most common approach in the industry. However, due to the similarities between primaries and multiples, applying adaptive subtraction poses a high risk of primary-amplitude damage, preventing quantitative seismic data interpretation. Therefore, we examine the Marchenko method that retrieves Greenโs functions from surface seismic data for target-oriented imaging without applying adaptive subtraction. Marchenko imaging has produced promising results on several offshore seismic data sets, but an onshore application is still needed. To better understand the effects of internal multiples and implement Marchenko imaging, we perform integrated analysis through well-log, vertical seismic profiling (VSP), and seismic data from a hydrocarbon field in Kuwait. In addition, we use VSP data to cross-check the retrieved Greenโs functions and estimate the scaling factor of the Marchenko method. The results indicate that (1)ย the poor imaging at the center of the field is due to the destructive interference of internal multiples, (2)ย the reverberation of internal multiples between the evaporite formations of the overburden is the most likely candidate that affects the seismic images of the Jurassic reservoirs, (3)ย the retrieved Greenโs functions conform to the recorded Greenโs functions from VSP data, and (4)ย Marchenko imaging provides a means to improve the seismic images of the Jurassic formations in Kuwait.
Using distributed acoustic sensing to characterize unconventional reservoirs via perforation-shot triggered P waves
Li, Peiyao (Colorado School of Mines) | Jin, Ge (Colorado School of Mines)
ABSTRACT The lack of knowledge of lateral heterogeneity in unconventional reservoirs commonly has negative impacts on drilling, completion efficiency, and production. However, current methods, such as well logging and seismic surveying, are limited in their ability to characterize unconventional reservoirs. We develop an alternative geophysical approach that uses distributed acoustic sensing (DAS) and perforation shots to characterize unconventional reservoirs. In our field data set, DAS-recorded perforation shots show strong P-wave signals. The recorded P-wave waveforms from the study area exhibit dispersive behavior, which can be clearly identified after signal processing. The spatial variations in phase velocity along the horizontal wellbore can be reliably measured by averaging the measurements from multiple closely situated perforation shots. We observe a low phase-velocity zone along the study well, which is spatially consistent with the well logs and root mean square amplitude extracted from the 3D seismic volume. The observed dispersive behavior of P waves is validated through numerical modeling. By comparing the results from the proposed method with those from modeling results and other measurements, we conclude that the proposed method results in a reasonable radius of investigation for unconventional reservoir characterization. The method also has the potential to infer hydraulic fracturing effectiveness by comparing the phase-velocity difference before and after stimulation. The data acquisition of the proposed workflow can be combined with perforation shot operations, which provides a cost-effective and suitable approach to investigating lateral heterogeneity in unconventional reservoirs.
- Geophysics > Seismic Surveying > Seismic Processing (1.00)
- Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling (1.00)
- Geophysics > Seismic Surveying > Passive Seismic Surveying > Microseismic Surveying (1.00)
- Geophysics > Seismic Surveying > Borehole Seismic Surveying > Vertical Seismic Profile (VSP) (0.68)
Modeling acoustic wavefields from moving sources in the presence of a time-varying free surface
Almuteri, Khalid (Colorado School of Mines) | Shragge, Jeffrey (Colorado School of Mines) | Sava, Paul (Colorado School of Mines)
Marine vibrators are increasingly being recognized as a viable alternative to seismic airguns for ocean-bottom acquisition due to their ability to generate more low-frequency content and their less adverse impact on marine wildlife. However, their use introduces processing challenges, such as the Doppler effect and time-dependent source-receiver offsets, which are negligible in conventional airgun acquisition. In addition, the time-varying nature of the sea surface during the multi-second acquisition time introduces further challenges for processing and inversion. To accurately account for source motion and time-varying sea surface effects in seismic data processing, we develop a reliable and robust numerical modeling tool. We use a mimetic finite-difference method in a generalized coordinate system to model the full acoustic wavefield triggered by a moving source in the presence of a time-varying sea surface. Our approach uses a time- and space-dependent coordinate transformation, which tracks the source movement and conforms to the irregular time-varying sea surface, to map an irregular physical domain in Cartesian coordinates to a regular computational domain in generalized coordinates. We formulate this coordinate transformation such that both coordinate systems conformally match below the ocean-bottom level. Numerical examples demonstrate that this approach is accurate and stable, even for an unrealistically exaggerated sea state. This computational tool is not limited to modeling, but could also be used to develop advanced processing techniques for marine vibrator data, such as imaging and inversion.
- Geophysics > Seismic Surveying > Seismic Modeling (1.00)
- Geophysics > Seismic Surveying > Seismic Processing (0.88)
- Geophysics > Seismic Surveying > Surface Seismic Acquisition (0.67)
Using Distributed Acoustic Sensing to Characterize Unconventional Reservoirs via Perforation-shot Triggered P Waves
Li, Peiyao (Colorado School of Mines) | Jin, Ge (Colorado School of Mines)
Lack of knowledge of lateral heterogeneity in unconventional reservoirs commonly imposes negative impacts on drilling, completion efficiency, and production. However, current methods, such as well logging and seismic survey, are limited in characterizing unconventional reservoirs. This study proposes an alternative geophysical approach that utilizes Distributed Acoustic Sensing (DAS) and perforation shot to characterize unconventional reservoirs. In our field dataset, DAS recorded perforation shot shows strong P-wave signals. The recorded P-wave waveforms from the study area exhibit dispersive behavior, which can be clearly identified after signal processing. The phase-velocity spatial variations along the horizontal wellbore can be reliably measured by averaging the measurements from multiple close-by perforation shots. We observe a low phase-velocity zone along the study well, which is spatially consistent with well logs and 3-D seismic images. The observed dispersive behavior of P waves is validated via numerical modeling. By comparing the proposed method with modeling results and other measurements, we conclude that the proposed method results in an ideal investigation radius for unconventional reservoir characterization. The method also has the potential to infer hydraulic fracturing effectiveness by comparing the phase-velocity difference before and after stimulation. The data acquisition of the proposed workflow can be combined with perforation shot operations, which provides a cost-effective and suitable approach to investigate lateral heterogeneity for unconventional reservoirs.
- Geophysics > Seismic Surveying > Seismic Processing (1.00)
- Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling (1.00)
- Geophysics > Seismic Surveying > Passive Seismic Surveying > Microseismic Surveying (1.00)
- Geophysics > Seismic Surveying > Borehole Seismic Surveying > Vertical Seismic Profile (VSP) (0.68)
Latest advancements in machine learning for geophysics โ Introduction
Di, Haibin (SLB) | Hu, Wenyi (SLB) | Abubakar, Aria (SLB) | Devarakota, Pandu (Shell Technology Center Houston) | Li, Weichang (Aramco Americas) | Li, Yaoguo (Colorado School of Mines)
- Geophysics > Seismic Surveying (1.00)
- Geophysics > Borehole Geophysics (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)
Stochastic inversion of geophysical data by a conditional variational autoencoder
McAliley, Wallace Anderson (Colorado School of Mines) | Li, Yaoguo (Colorado School of Mines)
ABSTRACT Recovering geologically realistic physical property models by geophysical inversion is a long-standing challenge. Generative neural networks offer a promising path to meet this challenge because they can produce spatially complex models that exhibit the characteristics of a set of training models, even when those characteristics are difficult to quantify. In the context of geophysical inversion, these characteristics may include faults, layers, and sharp contacts between rock units. Here, we develop a framework for incorporating prior geologic knowledge into geophysical inversions using conditional variational autoencoders (CVAEs). We train a CVAE to reconstruct training density models while honoring relative gravity data. Once trained, the decoder network of the CVAE inverts gravity data. The inputs to the decoder are observed gravity data and a set of latent variables that are sampled from a standard normal distribution. The decoder maps from the observed data and latent variables to density models such that the resulting models are consistent with the training models and the input data. Consequently, the inversion fits the observed data and incorporates the information embedded in the training models. The decoder can produce many inverted models instantaneously, sampling an approximation to the posterior model distribution efficiently. We find that the latent variables correspond to independent interpretable ways in which the model can vary while still honoring the observed data. We draw a connection to linear inverse theory, positing that the latent variables are analogous to the principal components of the local posterior model covariance.
Stochastic inversion of geophysical data by a conditional variational autoencoder
McAliley, Wallace Anderson (Colorado School of Mines) | Li, Yaoguo (Colorado School of Mines)
ABSTRACT Recovering geologically realistic physical property models by geophysical inversion is a long-standing challenge. Generative neural networks offer a promising path to meet this challenge because they can produce spatially complex models that exhibit the characteristics of a set of training models, even when those characteristics are difficult to quantify. In the context of geophysical inversion, these characteristics may include faults, layers, and sharp contacts between rock units. Here, we develop a framework for incorporating prior geologic knowledge into geophysical inversions using conditional variational autoencoders (CVAEs). We train a CVAE to reconstruct training density models while honoring relative gravity data. Once trained, the decoder network of the CVAE inverts gravity data. The inputs to the decoder are observed gravity data and a set of latent variables that are sampled from a standard normal distribution. The decoder maps from the observed data and latent variables to density models such that the resulting models are consistent with the training models and the input data. Consequently, the inversion fits the observed data and incorporates the information embedded in the training models. The decoder can produce many inverted models instantaneously, sampling an approximation to the posterior model distribution efficiently. We find that the latent variables correspond to independent interpretable ways in which the model can vary while still honoring the observed data. We draw a connection to linear inverse theory, positing that the latent variables are analogous to the principal components of the local posterior model covariance.