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Patricia de Lugรฃo received a Bachelor of Science degree in environmental engineering and water resources from the University of South Carolina in 1988, a master's degree in geophysics from the Observatรณrio Nacional in Rio de Janeiro in 1992 and a Ph.D. in geophysics from University of Utah in 1997. At Observatรณrio Nacional, she worked with Sergio Fontes on the acquisition, processing, and modeling of magnetotelluric data from the Recรดncavo Basin, Brazil. During her Ph.D. studies at the University of Utah, de Lugรฃo had the good fortune to work with Phil Wannamaker and Michael Zhdanov on the development of modeling and inversion algorithms for magnetotellurics. After her Ph.D., de Lugรฃo worked in the research department at Western Atlas in Houston with Kurt-Martin Strack, where she applied her knowledge in modeling and inversion to the development of algorithms for array borehole tools. In the Geosignal division of Western Atlas, Patricia worked with Lee Bell on two- and three-dimensional refraction tomography techniques for statics correction and initial velocity model for prestack depth migration of seismic data from the foothills of South America to the Gulf of Mexico.
- North America > United States > Utah (0.47)
- South America > Brazil > Rio de Janeiro > Rio de Janeiro (0.26)
- Geophysics > Electromagnetic Surveying (1.00)
- Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling (0.72)
- Geophysics > Seismic Surveying > Seismic Processing (0.57)
- South America > Brazil > Brazil > South Atlantic Ocean > Santos Basin (0.99)
- South America > Brazil > Bahia > Reconcavo Basin (0.99)
- Information Technology > Knowledge Management (0.76)
- Information Technology > Communications > Collaboration (0.76)
The goal of the talk is to start you on the way to becoming a data connoisseur, instead of merely an indiscriminate consumer. The talk will be example-driven for a broad audience, however I will also have tutorial sections that I can include for academic audiences wanting to dig deeper with a longer talk.
- Information Technology > Knowledge Management (0.76)
- Information Technology > Communications > Collaboration (0.76)
Sergey Fomel is Wallace E. Pratt Professor of Geophysics at The University of Texas at Austin and the Director of the Texas Consortium for Computational Seismology (TCCS). At UT Austin, he is affiliated with the Bureau of Economic Geology, the Department of Geological Sciences, and the Oden Institute for Computational Engineering and Sciences. Sergey received a PhD in Geophysics from Stanford University in 2001. Previously, he worked at the Institute of Geophysics in Russia (currently Trofimuk Institute of Petroleum Geology and Geophysics), Schlumberger Geco-Prakla, and the Lawrence Berkeley National Laboratory. For his contributions to exploration geophysics, he has been recognized with a number of professional awards, including the J. Clarence Karcher Award from SEG in 2001, Best SEG Poster Presentation Awards in 2007 and 2011, and the Conrad Schlumberger Award from EAGE in 2011.
- Asia (0.51)
- North America > United States > Texas > Travis County > Austin (0.26)
- Geophysics > Seismic Surveying > Seismic Processing (0.73)
- Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling (0.31)
Velocity errors and data noise are inevitable for seismic imaging of field datasets in current production; therefore, it is desirable to improve the seismic images as part of the migration process to mitigate the influence of such errors and noise. To address this, we have developed a new method of adaptive merging migration (AMM). This method can produce migrated sections of equal quality to conventional migration methods given a correct velocity model and noise-free data. Additionally, it can ameliorate the seismic image quality when applied with erroneous migration velocity models or noisy seismic data. AMM employs an efficient recursive Radon transform to generate multiple p-component images, representing migrated sections associated with different local plane slopes. It then adaptively merges the subsections from those p-component images that are less distorted by velocity errors or noise into the whole image. Such merging is implemented by computing adaptive weights followed by a selective stacking. We use three synthetic velocity models and one field dataset to evaluate the AMM performance on isolated Gaussian velocity errors, inaccurate smoothed velocities, velocity errors around high-contrast and short-wavelength interfaces, and noisy seismic data. Numerical tests conducted on both synthetic and field datasets validate that AMM can effectively improve the seismic image quality in the presence of different types of velocity errors and data noise.
- Geophysics > Seismic Surveying > Seismic Processing > Seismic Migration (1.00)
- Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling > Seismic Inversion (0.46)
Facies classification of image logs plays a vital role in reservoir characterization, especially in the heterogeneous and anisotropic carbonate formations of the Brazilian pre-salt region. Although manual classification remains the industry standard for handling the complexity and diversity of image logs, it has notable disadvantages of being time-consuming, labor-intensive, subjective, and non-repeatable. Recent advancements in machine learning offer promising solutions for automation and acceleration. However, previous attempts to train deep neural networks for facies identification have struggled to generalize to new data due to insufficient labeled data and the inherent intricacy of image logs. Additionally, human errors in manual labels further hinder the performance of trained models. To overcome these challenges, we propose adopting the state-of-the-art SwinV2-Unet to provide depthwise facies classification for Brazilian pre-salt acoustic image logs. The training process incorporates transfer learning to mitigate overfitting and confident learning to address label errors. Through a k-fold cross-validation experiment, with each fold spanning over 350 meters, we achieve an impressive macro F1 score of 0.90 for out-of-sample predictions. This significantly surpasses the previous model modified from the widely recognized U-Net, which provides a macro F1 score of 0.68. These findings highlight the effectiveness of the employed enhancements, including the adoption of an improved neural network and an enhanced training strategy. Moreover, our SwinV2-Unet enables highly efficient and accurate facies analysis of the complex yet informative image logs, significantly advancing our understanding of hydrocarbon reservoirs, saving human effort, and improving productivity.
- Geology > Structural Geology > Tectonics > Salt Tectonics (1.00)
- Geology > Geological Subdiscipline (1.00)
- Geology > Rock Type > Sedimentary Rock > Carbonate Rock (0.67)
- Geophysics > Seismic Surveying > Borehole Seismic Surveying (1.00)
- Geophysics > Borehole Geophysics (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)
- Production and Well Operations > Well & Reservoir Surveillance and Monitoring > Borehole imaging and wellbore seismic (1.00)
- (2 more...)
Adaptive laterally constrained inversion of time-domain electromagnetic data using Hierarchical Bayes
Li, Hai (Chinese Academy of Sciences, Chinese Academy of Sciences) | Di, Qingyun (Chinese Academy of Sciences, Chinese Academy of Sciences) | Li, Keying (Chinese Academy of Sciences, Chinese Academy of Sciences, University of Chinese Academy of Sciences)
Laterally constrained inversion (LCI) of time-domain electromagnetic (TEM) data is effective in recovering quasi-layered models, particularly in sedimentary environments. By incorporating lateral constraints, LCI enhances the stability of the inverse problem and improves the resolution of stratified interfaces. However, a limitation of the LCI is the recovery of laterally smooth transitions, even in regions unsupported by the available datasets. Therefore, we have developed an adaptive LCI scheme within a Bayesian framework. Our approach introduces user-defined constraints through a multivariate Gaussian prior, where the variances serve as hyperparameters in a Hierarchical Bayes algorithm. By simultaneously sampling the model parameters and hyperparameters, our scheme allows for varying constraints throughout the model space, selectively preserving lateral constraints that align with the available datasets. We demonstrated the effectiveness of our adaptive LCI scheme through a synthetic example. The inversion results showcase the self-adaptive nature of the strength of constraints, yielding models with smooth lateral transitions while accurately retaining sharp lateral interfaces. An application to field TEM data collected in Laizhou, China, supports the findings from the synthetic example. The adaptive LCI scheme successfully images quasi-layered environments and formations with well-defined lateral interfaces. Moreover, the Bayesian inversion provides a measure of uncertainty, allowing for a comprehensive illustration of the confidence in the inversion results.
- Geology > Mineral (0.93)
- Geology > Sedimentary Geology > Depositional Environment (0.34)
- Oceania > Australia > Western Australia > North West Shelf > Carnarvon Basin > Exmouth Plateau > WA-1-R > Scarborough Field (0.99)
- Europe > Norway (0.91)
- Reservoir Description and Dynamics > Reservoir Characterization > Exploration, development, structural geology (1.00)
- Reservoir Description and Dynamics > Reservoir Simulation > Evaluation of uncertainties (0.93)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (0.79)
- (2 more...)
PINNslope: seismic data interpolation and local slope estimation with physics#xD;informed neural networks
Brandolin, F. (King Abdullah University of Science and Technology (KAUST)) | Ravasi, M. (King Abdullah University of Science and Technology (KAUST)) | Alkhalifah, T. (King Abdullah University of Science and Technology (KAUST))
Interpolation of aliased seismic data constitutes a key step in a seismic processing workflow to obtain high-quality velocity models and seismic images. Building on the idea of describing seismic wavefields as a superposition of local plane waves, we propose to interpolate seismic data by using a physics informed neural network (PINN). In the proposed framework, two feed-forward neural networks are jointly trained using the local plane wave differential equation as well as the available data as two terms in the objective function: a primary network assisted by positional encoding is tasked with reconstructing the seismic data, while an auxiliary, smaller network estimates the associated local slopes. Results on synthetic and field data validate the effectiveness of the proposed method in handling aliased (coarsely sampled) data and data with large gaps. Our method compares favorably against a classic least-squares inversion approach regularized by the local plane-wave equation as well as a PINN-based approach with a single network and precomputed local slopes. We find that introducing a second network to estimate the local slopes while at the same time interpolating the aliased data enhances the overall reconstruction capabilities and convergence behavior of the primary network. Moreover, an additional positional encoding layer embedded as the first layer of the wavefield network confers to the network the ability to converge faster, improving the accuracy of the data term.
- Geophysics > Seismic Surveying > Seismic Processing (1.00)
- Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling (0.34)
Unsupervised Deep Learning Framework for 5D Seismic Denoising and Interpolation
Saad, Omar M. (National Research Institute of Astronomy and Geophysics (NRIAG), King Abdullah University of Science and Technology (KAUST)) | Helmy, Islam (National Research Institute of Astronomy and Geophysics (NRIAG)) | Chen, Yangkang (The University of Texas at Austin)
We propose an unsupervised framework to reconstruct the missing data from the noisy and incomplete five-dimensional (5D) seismic data. The proposed method comprises two main components: a deep learning network and a projection onto convex sets (POCS) method. The model works iteratively, passing the data between the two components and splitting the data into a group of patches using a patching scheme. Specifically, the patching scheme breaks the input data into small segments which are then reshaped to a vector of one dimension feeding the deep learning model. Afterward, POCS is utilized to optimize the output data from the deep learning model, which is proposed to denoise and interpolate the extracted patches. The proposed deep learning model consists of several blocks, that are, fully connected layers, attention block, and several skip connections. Following this, the output of the POCS algorithm is considered as the input of the deep learning model for the following iteration. The proposed model iteratively works in an unsupervised scheme where labeled data is not required. A performance comparison with benchmark methods using several synthetic and field examples shows that the proposed method outperforms the traditional methods.
- North America > United States (0.28)
- Asia (0.28)
Multichannel deconvolution with a high-frequency structural regularization
Wang, Pengfei (China University of Petroleum) | Zhao, Dongfeng (China University of Petroleum) | Niu, Yue (National Engineering Research Center for Oil and Gas Exploration Computer Software) | Li, Guofa (China University of Petroleum) | Gu, Weiwei (China University of Petroleum)
The resolution of seismic data determines the ability to characterize stratigraphic features from observed seismic record. Sparse spike inversion (SSI) as an important processing method can effectively improve the band-limited property of the seismic data. However, the approch ignores the spatial information along seismic traces, which causes the unreliability of the reconstructed high-resolution data. In this article, we have developed a high-frequency structure constrained multichannel deconvolution (HFSC-MD) to alleviate this issue. This method allows the cost function to incorporate high-frequency spatial information in the form of prediction-error filter (PEF), to regularize the components of the result beyond the original frequency. The PEF also called high-frequency structural characterization operator (HFRSC operator), is estimated from the mapping relationship of low and high-frequency components. We adopt the alternating direction method of multipliers (ADMM) to solve the cost function in HFSC-MD. Synthetic and field data demonstrate that the proposed method recovers more reliable high-resolution data, and enriches the reflective structures.
- Geophysics > Seismic Surveying > Seismic Processing (1.00)
- Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling > Seismic Inversion (0.34)
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)