2D High-resolution Reservoir Characterization by Model-free Seismic Inversion with Sparsity Promotion

2010 ◽  
Author(s):  
D. Tetyukhina ◽  
L. van Vliet ◽  
K. Wapenaar ◽  
S. M. Luthi
Keyword(s):  
High Resolution ◽  
Reservoir Characterization ◽  
Seismic Inversion ◽  
Model Free

scholarly journals SEISMIC TRAVELTIME TOMOGRAPHY APPLIED TO DATA FROM MIRANGA FIELD, RECÔNCAVO BASIN, BRAZIL

2016 ◽  
Vol 34 (3) ◽  
Author(s):  
Vitor Hugo Serravalle Reis Rodrigues ◽  
Amin Bassrei
Keyword(s):  
High Resolution ◽  
Reservoir Characterization ◽  
Seismic Inversion ◽  
Oil Exploration ◽  
Exploration And Exploitation ◽  
Traveltime Tomography ◽  
Seismic Methods ◽  
Global Demand ◽  
Alternative Means

ABSTRACT. The growing global demand for hydrocarbons has tested the limits of oil exploration and exploitation technologies. Among the seismic methods, tomography is an alternative means for high-resolution characterization of reservoirs, and it enables a more efficient...Keywords: reservoir characterization, traveltime tomography, seismic inversion, regularization, Recôncavo Basin. RESUMO. A crescente demanda mundial por hidrocarbonetos tem testado os limites das tecnologias de exploração e explotação de petróleo. Dentro dos métodos sísmicos, a tomografia surge como alternativa de caracterização de alta resolução dos reservatórios,...Palavras-chave: caracterização de reservatórios, tomografia de tempos de trânsito, inversão s´ísmica, regularização, Bacia do Recôncavo.

scholarly journals Multimodal Low Resolution Face and Frontal Gait Recognition from Surveillance Video

Electronics ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 1013
Author(s):  
Sayan Maity ◽  
Mohamed Abdel-Mottaleb ◽  
Shihab S. Asfour
Keyword(s):  
Face Recognition ◽  
High Resolution ◽  
Gait Cycle ◽  
Gait Recognition ◽  
Recognition System ◽  
Multimodal Fusion ◽  
Surveillance Video ◽  
Low Resolution ◽  
Model Free ◽  
Face Images

Biometric identification using surveillance video has attracted the attention of many researchers as it can be applicable not only for robust identification but also personalized activity monitoring. In this paper, we present a novel multimodal recognition system that extracts frontal gait and low-resolution face images from frontal walking surveillance video clips to perform efficient biometric recognition. The proposed study addresses two important issues in surveillance video that did not receive appropriate attention in the past. First, it consolidates the model-free and model-based gait feature extraction approaches to perform robust gait recognition only using the frontal view. Second, it uses a low-resolution face recognition approach which can be trained and tested using low-resolution face information. This eliminates the need for obtaining high-resolution face images to create the gallery, which is required in the majority of low-resolution face recognition techniques. Moreover, the classification accuracy on high-resolution face images is considerably higher. Previous studies on frontal gait recognition incorporate assumptions to approximate the average gait cycle. However, we quantify the gait cycle precisely for each subject using only the frontal gait information. The approaches available in the literature use the high resolution images obtained in a controlled environment to train the recognition system. However, in our proposed system we train the recognition algorithm using the low-resolution face images captured in the unconstrained environment. The proposed system has two components, one is responsible for performing frontal gait recognition and one is responsible for low-resolution face recognition. Later, score level fusion is performed to fuse the results of the frontal gait recognition and the low-resolution face recognition. Experiments conducted on the Face and Ocular Challenge Series (FOCS) dataset resulted in a 93.5% Rank-1 for frontal gait recognition and 82.92% Rank-1 for low-resolution face recognition, respectively. The score level multimodal fusion resulted in 95.9% Rank-1 recognition, which demonstrates the superiority and robustness of the proposed approach.

Application of geostatistical seismic inversion in reservoir characterization of Sapphire gas field, offshore Nile Delta, Egypt

2019 ◽  
Vol 38 (6) ◽  
pp. 474-479
Author(s):  
Mohamed G. El-Behiry ◽  
Said M. Dahroug ◽  
Mohamed Elattar
Keyword(s):  
Reservoir Characterization ◽  
Nile Delta ◽  
Seismic Inversion ◽  
Reservoir Modeling ◽  
Connectivity Analysis ◽  
Gas Field ◽  
Geostatistical Inversion ◽  
Well Data ◽  
Reservoir Connectivity ◽  
Inversion Techniques

Seismic reservoir characterization becomes challenging when reservoir thickness goes beyond the limits of seismic resolution. Geostatistical inversion techniques are being considered to overcome the resolution limitations of conventional inversion methods and to provide an intuitive understanding of subsurface uncertainty. Geostatistical inversion was applied on a highly compartmentalized area of Sapphire gas field, offshore Nile Delta, Egypt, with the aim of understanding the distribution of thin sands and their impact on reservoir connectivity. The integration of high-resolution well data with seismic partial-angle-stack volumes into geostatistical inversion has resulted in multiple elastic property realizations at the desired resolution. The multitude of inverted elastic properties are analyzed to improve reservoir characterization and reflect the inversion nonuniqueness. These property realizations are then classified into facies probability cubes and ranked based on pay sand volumes to quantify the volumetric uncertainty in static reservoir modeling. Stochastic connectivity analysis was also applied on facies models to assess the possible connected volumes. Sand connectivity analysis showed that the connected pay sand volume derived from the posterior mean of property realizations, which is analogous to deterministic inversion, is much smaller than the volumes generated by any high-frequency realization. This observation supports the role of thin interbed reservoirs in facilitating connectivity between the main sand units.

See the Unseen through Target-Oriented Dip-Steered Seismic Resolution

2021 ◽  
Author(s):  
Muhammad Sajid ◽  
Ahmad Riza Ghazali
Keyword(s):  
Reservoir Characterization ◽  
Resolution Enhancement ◽  
Dominant Frequency ◽  
Seismic Inversion ◽  
Seismic Resolution ◽  
Geological Features ◽  
Spectral Enhancement ◽  
Enhancement Method ◽  
Layer Velocity ◽  
Seismic Volumes

Abstract Seismic resolution plays an important role not only in interpretation and reservoir characterization but also in seismic inversion and seismic attributes analysis. The resolution depends on several factors, including seismic frequency bandwidth, dominant frequency, and layer velocity. This paper presents a spectral resolution enhancement approach that is based on Non-stationary Differential Resolution (NSDR) that honors the local structural dip, better preserves amplitude and improves target-oriented seismic interpretation. The proposed technology is applied to both 2D and 3D seismic volumes and findings are compared with the oil industry common spectral enhancement algorithms. We demonstrate the target-oriented dip steering spectral enhancement method on two 3D field datasets and compare the resulting outcome with those obtained by conventional techniques. It is found that thinly layered subsurface geological features with steeply dipping beds are better defined, with artifacts from the conflicting dips removed.

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