S-Wave Velocity Prediction for Pore-Filling Gas Hydrate-Bearing Sediments

2020 ◽  
Author(s):  
X. Liu ◽  
X. Wang ◽  
J. Yang ◽  
H. Liu
Keyword(s):  
Wave Velocity ◽  
Gas Hydrate ◽  
S Wave ◽  
Pore Filling ◽  
Velocity Prediction ◽  
Hydrate Bearing Sediments

scholarly journals The Acoustic Properties of Sandy and Clayey Hydrate-Bearing Sediments

Energies ◽  
2019 ◽  
Vol 12 (10) ◽  
pp. 1825
Author(s):  
Xiao-Hui Wang ◽  
Qiang Xu ◽  
Ya-Nan He ◽  
Yun-Fei Wang ◽  
Yi-Fei Sun ◽  
...  
Keyword(s):  
Wave Velocity ◽  
Gas Hydrate ◽  
Acoustic Properties ◽  
P Wave ◽  
S Wave ◽  
Hydrate Saturation ◽  
New Apparatus ◽  
Sandy Sediments ◽  
Hydrate Bearing Sediments ◽  
And Control

Natural gas hydrates samples are rare and difficult to store and transport at in situ pressure and temperature conditions, resulting in difficulty to characterize natural hydrate-bearing sediments and to identify hydrate accumulation position and saturation at the field scale. A new apparatus was designed to study the acoustic properties of seafloor recovered cores with and without hydrate. To protect the natural frames of recovered cores and control hydrate distribution, the addition of water into cores was performed by injecting water vapor. The results show that hydrate saturation and types of host sediments are the two most important factors that govern the elastic properties of hydrate-bearing sediments. When gas hydrate saturation adds approximately to 5–25%, the corresponding P-wave velocity (Vp) increases from 1.94 to 3.93 km/s and S-wave velocity (Vs) increases from 1.14 to 2.23 km/s for sandy specimens; Vp and Vs for clayey samples are 1.72–2.13 km/s and 1.10–1.32 km/s, respectively. The acoustic properties of sandy sediments can be significantly changed by the formation/dissociation of gas hydrate, while these only minorly change for clayey specimens.

Characterization of elastic properties of near-surface and subsurface deepwater hydrate-bearing sediments

Geophysics ◽  
2013 ◽  
Vol 78 (3) ◽  
pp. D169-D179 ◽  
Author(s):  
Zijian Zhang ◽  
De-hua Han ◽  
Daniel R. McConnell
Keyword(s):  
Wave Velocity ◽  
Elastic Constants ◽  
Gas Hydrate ◽  
Effective Medium Theory ◽  
Pore Space ◽  
Seismic Interpretation ◽  
P Wave ◽  
Near Surface ◽  
Free Gas ◽  
Hydrate Bearing Sediments

Hydrate-bearing sands and shallow nodular hydrate are potential energy resources and geohazards, and they both need to be better understood and identified. Therefore, it is useful to develop methodologies for modeling and simulating elastic constants of these hydrate-bearing sediments. A gas-hydrate rock-physics model based on the effective medium theory was successfully applied to dry rock, water-saturated rock, and hydrate-bearing rock. The model was used to investigate the seismic interpretation capability of hydrate-bearing sediments in the Gulf of Mexico by computing elastic constants, also known as seismic attributes, in terms of seismic interpretation, including the normal incident reflectivity (NI), Poisson’s ratio (PR), P-wave velocity ([Formula: see text]), S-wave velocity ([Formula: see text]), and density. The study of the model was concerned with the formation of gas hydrate, and, therefore, hydrate-bearing sediments were divided into hydrate-bearing sands, hydrate-bearing sands with free gas in the pore space, and shallow nodular hydrate. Although relations of hydrate saturation versus [Formula: see text] and [Formula: see text] are different between structures I and II gas hydrates, highly concentrated hydrate-bearing sands may be interpreted on poststack seismic amplitude sections because of the high NI present. The computations of elastic constant implied that hydrate-bearing sands with free gas could be detected with the crossplot of NI and PR from prestack amplitude analysis, and density may be a good hydrate indicator for shallow nodular hydrate, if it can be accurately estimated by seismic methods.

scholarly journals Research on prediction method of S-wave velocity based on deep learning

2021 ◽  
Vol 2083 (4) ◽  
pp. 042065
Author(s):  
Guojie Yang ◽  
Shuhua Wang
Keyword(s):  
Deep Learning ◽  
Wave Velocity ◽  
Prediction Method ◽  
P Wave ◽  
Learning Technology ◽  
S Wave ◽  
Advantages And Disadvantages ◽  
Wave Prediction ◽  
Wide Range ◽  
Velocity Prediction

Abstract Aiming at the s-wave velocity prediction problem, based on the analysis of the advantages and disadvantages of the empirical formula method and the rock physics modeling method, combined with the s-wave velocity prediction principle, the deep learning method is introduced, and a deep learning-based logging s-wave velocity prediction method is proposed. This method uses a deep neural network algorithm to establish a nonlinear mapping relationship between reservoir parameters (acoustic time difference, density, neutron porosity, shale content, porosity) and s-wave velocity, and then applies it to the s-wave velocity prediction at the well point. Starting from the relationship between p-wave and s-wave velocity, the study explained the feasibility of applying deep learning technology to s-wave prediction and the principle of sample selection, and finally established a reliable s-wave prediction model. The model was applied to s-wave velocity prediction in different research areas, and the results show that the s-wave velocity prediction technology based on deep learning can effectively improve the accuracy and efficiency of s-wave velocity prediction, and has the characteristics of a wide range of applications. It can provide reliable s-wave data for pre-stack AVO analysis and pre-stack inversion, so it has high practical application value and certain promotion significance.

scholarly journals Effects of CH4–CO2 replacement in hydrate-bearing sediments on S-wave velocity and electrical resistivity

2020 ◽  
Vol 82 ◽  
pp. 103506
Author(s):  
Jongwon Jung ◽  
Jae Eun Ryou ◽  
Riyadh I. Al-Raoush ◽  
Khalid Alshibli ◽  
Joo Yong Lee
Keyword(s):  
Electrical Resistivity ◽  
Wave Velocity ◽  
S Wave ◽  
Hydrate Bearing Sediments

Carbonate microporosity aspect ratio and S-wave velocity prediction using 2D/3D digital image analysis and inclusion theory

2015 ◽  
Author(s):  
Irineu de A. Lima Neto ◽  
Roseane M. Missagia ◽  
Marco A. R. de Ceia ◽  
Nathaly L. Archilha ◽  
Lucas C. Oliveira
Keyword(s):  
Image Analysis ◽  
Aspect Ratio ◽  
Wave Velocity ◽  
Digital Image ◽  
Digital Image Analysis ◽  
S Wave ◽  
Velocity Prediction ◽  
Inclusion Theory

scholarly journals OBS Data Analysis to Quantify Gas Hydrate and Free Gas in the South Shetland Margin (Antarctica)

Energies ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 3290 ◽  
Author(s):  
Sha Song ◽  
Umberta Tinivella ◽  
Michela Giustiniani ◽  
Sunny Singhroha ◽  
Stefan Bünz ◽  
...  
Keyword(s):  
Velocity Field ◽  
Wave Velocity ◽  
Gas Hydrate ◽  
P Wave ◽  
Ocean Bottom ◽  
Ocean Bottom Seismometer ◽  
S Wave ◽  
Free Gas ◽  
Hydrate Reservoir ◽  
Gas Hydrate Reservoir

The presence of a gas hydrate reservoir and free gas layer along the South Shetland margin (offshore Antarctic Peninsula) has been well documented in recent years. In order to better characterize gas hydrate reservoirs, with a particular focus on the quantification of gas hydrate and free gas and the petrophysical properties of the subsurface, we performed travel time inversion of ocean-bottom seismometer data in order to obtain detailed P- and S-wave velocity estimates of the sediments. The P-wave velocity field is determined by the inversion of P-wave refractions and reflections, while the S-wave velocity field is obtained from converted-wave reflections received on the horizontal components of ocean-bottom seismometer data. The resulting velocity fields are used to estimate gas hydrate and free gas concentrations using a modified Biot‐Geertsma‐Smit theory. The results show that hydrate concentration ranges from 10% to 15% of total volume and free gas concentration is approximately 0.3% to 0.8% of total volume. The comparison of Poisson’s ratio with previous studies in this area indicates that the gas hydrate reservoir shows no significant regional variations.

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