Results 161 to 170 of about 600,749 (229)

Learnable Gabor Filters in Attention Unet for Prestack Seismic Inversion

IEEE Geoscience and Remote Sensing Letters
Amplitude variation with angle (AVA) prestack seismic inversion plays a critical role in oil and gas exploration and mineral resource assessment. Recently, deep learning methods, particularly convolutional neural networks (CNNs), have been widely adopted
Yizhen Shan, Yueming Ye, Bangyu Wu
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Stability-enhanced prestack seismic inversion using hybrid orthogonal learning particle swarm optimization

Journal of Petroleum Science and Engineering, 2020
Prestack seismic inversion quantitatively translates seismic data into multiple elastic properties, supporting the exploration of subsurface reservoirs in detail.
Qiang Guo, Cong Luo, Jing Ba
exaly   +2 more sources

Dynamic Characterization of Reservoirs Constrained by Time-Lapse Prestack Seismic Inversion

IEEE Transactions on Geoscience and Remote Sensing, 2021
Dynamic changes of reservoir parameters within a reservoir are usually estimated by a history matching process based on the production data. However, it is difficult to accurately acquire the spatial distribution using production data as the constraints.
Guangtan Huang, Xiaohong Chen 0003, Cong Luo, Yangkang Chen   +3 more
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Three-parameter prestack seismic inversion based on L1-2 minimization

Geophysics, 2019
Prestack inversion has become a common approach in reservoir prediction. At present, the critical issue in the application of seismic inversion is the estimation of elastic parameters in the thin layers and weak reflectors.
Yangkang Chen, Yufeng Wang, Hui Zhou
exaly   +2 more sources

Spark-based intelligent parameter inversion method for prestack seismic data

Neural Computing and Applications, 2018
Seismic exploration is an oil exploration method by utilizing seismic information. Useful reservoir parameter information can be gained through inversion of seismic information to effectively carry out exploration work. Prestack data are characterized by large data size and rich information.
Chengyu Hu, Wenyin Gong, Xuesong Yan
exaly   +2 more sources

Direct reservoir property estimation based on prestack seismic inversion

Journal of Petroleum Science and Engineering, 2018
Reservoir property estimation is an essential part for reservoir characterization. Most commonly-used estimation methods are implemented in two steps, seismic inversion and rock physics inversion.
Tiansheng Chen
exaly   +2 more sources

A Boltzmann machine for high-resolution prestack seismic inversion

Interpretation, 2019
Seismic inversion is one popular approach that aims at predicting some indicative properties to support the geologic interpretation process. Existing inversion techniques indicate weaknesses when dealing with complex geologic area, where the uncertainties arise from the guiding model, which are provided by the interpreters.
Son Dang Thai Phan, Mrinal K. Sen
openaire   +2 more sources

Robust Weakly Supervised Learning Prestack Multitrace Seismic Inversion

IEEE Transactions on Geoscience and Remote Sensing
Prestack seismic inversion is one of the most commonly used methods to describe reservoirs. Deep learning (DL) has received wide attention because of its ability to handle complex nonlinear problems.
Zeyang Liu 0003, Xiaohong Chen 0003, Jingye Li, Siyun Hou, Zihang Li, Guochang Liu   +5 more
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An Improved Anisotropic Markov Random Field Approach for Prestack Seismic Inversion

IEEE Geoscience and Remote Sensing Letters, 2019
This letter presents an improved anisotropic Markov random field (IAMRF) approach for prestack seismic inversion. Instead of using multiple potential functions or adding extra weights on clique potentials as done in existing AMRF approaches, IAMRF removes the effects of anisotropic gradients in subsurface models by means of scaling parameters, which ...
Qiang Guo 0006, Hongbing Zhang, Kuiye Wei, Zhen Li, Zuoping Shang   +4 more
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Deep learning prestack seismic inversion based on multi-seismic features

84th EAGE Annual Conference & Exhibition, 2023
R. Luo, B. Wang, H. Chen, S. Sun, J. Geng   +4 more
openaire   +2 more sources