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Keywords: uncertainty quantification
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Proceedings Papers

Paper presented at the SEG/AAPG/SEPM First International Meeting for Applied Geoscience & Energy, September 26–October 1, 2021
Paper Number: SEG-2021-3583705
... simulation applied geoscience application algorithm geophysics uncertainty quantification latent space siahkoohi seismic data pre-processing artificial intelligence machine learning upstream oil & gas posterior distribution neural network sequence interpolation optimization problem...
Proceedings Papers

Paper presented at the SEG/AAPG/SEPM First International Meeting for Applied Geoscience & Energy, September 26–October 1, 2021
Paper Number: SEG-2021-3582868
... higher efficiency as well. We propose a Random Forest (RF) regressor based method using multiple seismic attributes to predict the porosity distribution with uncertainty quantification. The standard deviation of base models’ predictions is used to quantify the regression uncertainty of RF...
Proceedings Papers

Paper presented at the SEG/AAPG/SEPM First International Meeting for Applied Geoscience & Energy, September 26–October 1, 2021
Paper Number: SEG-2021-3584558
...Efficient boundary detection and uncertainty quantification using 1D blocky MT inversion Kaijun Xu*1 and Yaoguo Li2 1 Department of Geophysics, China University of Petroleum (East China), Qingdao, Shandong, China 2 Center for Gravity, Electrical, and Magnetic Studies, Department of Geophysics...
Proceedings Papers

Paper presented at the SEG/AAPG/SEPM First International Meeting for Applied Geoscience & Energy, September 26–October 1, 2021
Paper Number: SEG-2021-3594175
... and uncertainty quantification. upstream oil & gas artificial intelligence full-waveform inversion geophysics algorithm reservoir characterization baseline model uncertainty quantification inversion ddfwi mcmc-based approach difference data machine learning applied geoscience exploration...
Proceedings Papers

Paper presented at the SEG/AAPG/SEPM First International Meeting for Applied Geoscience & Energy, September 26–October 1, 2021
Paper Number: SEG-2021-3594957
... to reconstruct fine details in in estimating density. To address that, incorporating seismic impedance (Figure 7 (a after training on data additional information from surface seismic, neighboring above 340m depth. Therefore, our prediction in the limit wells and offset VSP and uncertainty quantification...
Proceedings Papers

Paper presented at the SEG/AAPG/SEPM First International Meeting for Applied Geoscience & Energy, September 26–October 1, 2021
Paper Number: SEG-2021-3581836
... Uncertainty quantification provides quantitative measures on the reliability of candidate solutions of ill-posed inverse problems. Due to their sequential nature, Monte Carlo sampling methods require large numbers of sampling steps for accurate Bayesian inference and are often computationally...
Proceedings Papers

Paper presented at the SEG International Exposition and Annual Meeting, October 11–16, 2020
Paper Number: SEG-2020-3428150
... Uncertainty quantification for full-waveform inversion provides a probabilistic characterization of the ill-conditioning of the problem, comprising the sensitivity of the solution with respect to the starting model and data noise. This analysis allows to assess the confidence in the candidate...
Proceedings Papers

Paper presented at the SEG International Exposition and Annual Meeting, October 11–16, 2020
Paper Number: SEG-2020-3426670
... Underground formation characterization and uncertainty quantification require the inversion of the subsurface sensing data using probabilistic methods. Markov chain Monte Carlo (MCMC) sampling methods have been extensively used to solve nonlinear inversion problems. However, the computational...
Proceedings Papers

Paper presented at the SEG International Exposition and Annual Meeting, October 11–16, 2020
Paper Number: SEG-2020-3417560
... In inverse problems, uncertainty quantification (UQ) deals with a probabilistic description of the solution nonuniqueness and data noise sensitivity. Setting seismic imaging into a Bayesian framework allows for a principled way of studying uncertainty by solving for the model posterior...
Proceedings Papers

Paper presented at the SEG International Exposition and Annual Meeting, September 15–20, 2019
Paper Number: SEG-2019-3215046
... ABSTRACT In geophysical imaging, uncertainty quantification is crucial for decision making. 4D seismic imaging aims to accurately recover changes that take place within a reservoir. These changes are typically characterized by their magnitude and their extent. We perform a Bayesian inversion...
Proceedings Papers

Paper presented at the SEG International Exposition and Annual Meeting, September 15–20, 2019
Paper Number: SEG-2019-3215537
...: Poster Station 9 Presentation Type: Poster uncertainty quantification inversion iteration artificial intelligence reservoir characterization uncertainty estimation machine learning ensemble kalman filter upstream oil & gas algorithm optimization problem nonlinear seismic inversion...
Proceedings Papers

Paper presented at the SEG International Exposition and Annual Meeting, September 15–20, 2019
Paper Number: SEG-2019-3216455
... motion predictions, and seismic early warnings. Bayesian neural networks provide a practical solution to solve the uncertainty quantification problems in deep learning, i.e., to make AI safe. In this paper, we construct a Bayesian convolutional neural network and implement a stochastic regularized...
Proceedings Papers

Paper presented at the SEG International Exposition and Annual Meeting, September 15–20, 2019
Paper Number: SEG-2019-3215234
... about the output is computationally prohibitive for full multiphysics model simulations. To investigate new strategies for uncertainty quantification, we consider a simple model of two-way, loosely coupled single-phase fluid flow and linear elastic mechanical deformation. Loose coupling allows...
Proceedings Papers

Paper presented at the 2018 SEG International Exposition and Annual Meeting, October 14–19, 2018
Paper Number: SEG-2018-W12-02
..., while advances in modeling, inversion, and computational resources have facilitated time-lapse monitoring, consideration of more rigorous measurement physics, coupled or joint inversion of multiple data types, improved uncertainty quantification, model order reduction, and more routine application...
Proceedings Papers

Paper presented at the 2018 SEG International Exposition and Annual Meeting, October 14–19, 2018
Paper Number: SEG-2018-2997269
... machine learning inverse problem inverse model seg international exposition electromagnetic inverse problem meju model uncertainty model parameter reservoir simulation information inversion model model uncertainty quantification uncertainty quantification geophysical inverse problem upstream...
Proceedings Papers

Paper presented at the 2018 SEG International Exposition and Annual Meeting, October 14–19, 2018
Paper Number: SEG-2018-2998450
...Trans-dimensional geosteering inversion: a data-driven schematic for geophysical interpretation and uncertainty quantification Qiuyang Shen*, Jiefu Chen, Xuqing Wu, Zhu Han, University of Houston; Hanming Wang, Chevron Summary Within this decade, the logging-while-drilling (LWD) resistivity tools...
Proceedings Papers

Paper presented at the 2017 SEG International Exposition and Annual Meeting, September 24–29, 2017
Paper Number: SEG-2017-17777808
...-couple source mechanism. However, recent studies have shown a non-negligible percentage of non-double-couple components of source moment tensors in hydraulic fracturing events. Without uncertainty quantification of the moment tensor solution, it is difficult to determine the reliability of these source...
Proceedings Papers

Paper presented at the 2017 SEG International Exposition and Annual Meeting, September 24–29, 2017
Paper Number: SEG-2017-17731484
... for uncertainty quantification of velocity models and seismic images using low frequency waveform data. We use the field expansion method, a fast Helmholtz solver, that achieves significant computational savings through a reduced parameterization in which we restrict the velocity model to consist of a series...
Proceedings Papers

Paper presented at the 2016 SEG International Exposition and Annual Meeting, October 16–21, 2016
Paper Number: SEG-2016-13879108
... compute risk and uncertainty assessment mean value matrix uncertainty quantification approximation artificial intelligence reconstruction inversion inversion wavefield machine learning van leeuwen posterior distribution standard deviation risk assessment seg seg international exposition...
Proceedings Papers

Paper presented at the 2016 SEG International Exposition and Annual Meeting, October 16–21, 2016
Paper Number: SEG-2016-13875371
... ABSTRACT We study the problem of the moment tensor inversion of a double-couple microseismic source from observed S/P amplitude ratios. The emphasis of this work is on uncertainty quantification that includes the effect of the uncertain event location. We use a Bayesian approach to quantify...

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