scholarly journals Simulation of VSP data based on DSI data and estimation of shear wave velocity and elastic Moduli for a well case study

2019 ◽  
Vol 4 (4) ◽  
pp. 138-145
Author(s):  
Babak Sayad Noghretab ◽  
Mohammad Kamal Ghassem-Alaskari
Keyword(s):  
Shear Wave ◽  
Wave Velocity ◽  
High Precision ◽  
Shear Wave Velocity ◽  
Elastic Moduli ◽  
Modeling Method ◽  
Gas Field ◽  
Data Set ◽  
Survey Report ◽  
Vsp Data

The purpose of this article was to generate and compare seismic modeling results with real vertical seismic profiling data (VSP data) based on Dipole Shear Imager (DSI) data in the reservoir zone (Kangan and upper Dalan Formations) of a well in South Pars gas field. Estimation of shear wave velocity (Vs) and density for layers above the reservoir zone, for which; DSI data did not exist, was also done by the applied modeling method to estimate elastic parameters of the layers. In this method, modeling for X-component of the VSP survey was run by utilizing the DSI data set of reservoir zone and the VSP survey report of the studied well with high precision. Computed results for the proposed modeling method led to achieving highly accurate, close to the reality of VSP model around the studied well. According to compression wave velocity (VP) attained from VSP survey reports of the well and Vp/Vs ratio obtained from Dipole Shear Imager (DSI), modeling was done. Afterward, shear wave velocity (Vs) for upper layers of reservoir zone estimated with high precision, then density and elastic moduli for the above layers and the reservoir zone were calculated.

scholarly journals Empirical relationships of shear wave velocity, SPT-N value and vertical effective stress for different soils in Mashhad, Iran

2015 ◽  
Vol 58 (3) ◽  
Author(s):  
Azam Ghazi ◽  
Naser Hafezi Moghadas ◽  
Hosein Sadeghi ◽  
Mohamad Ghafoori ◽  
Gholam Reza Lashkaripur
Keyword(s):  
Shear Wave ◽  
Wave Velocity ◽  
Effective Stress ◽  
Shear Wave Velocity ◽  
Response Analysis ◽  
Empirical Relationships ◽  
Data Set ◽  
Fine Grain ◽  
Geophysical Investigations ◽  
Vertical Effective Stress

<p>Shear wave velocity, V<sub>s</sub>, is one of the important input parameters in seismic response analysis of the ground. Various methods have been examined to measure the soil V<sub>s</sub> directly. Direct measurement of V<sub>s</sub> is time consuming and costly, therefore many researchers have been trying to update empirical relationships between V<sub>s</sub> and other geotechnical properties of soils such as SPT Blow count, SPT-N. In this study the existence of a statistical relationship between V<sub>s</sub>, SPT-N<sub>60 </sub>and vertical effective stress, signa<sub>nu</sub>´, is investigated. Data set we used in this study was gathered from geotechnical and geophysical investigations reports. The data have been extracted from more than 130 numbers of geotechnical boreholes from different parts of Mashhad city. In each borehole the V<sub>s</sub> has been measured by downhole method at two meter intervals. The SPT test also has performed at the same depth. Finally relationships were developed by regression analysis for gravels, sands and fine grain soils. The proposed relationships indicate that V<sub>s</sub> is strongly dependent on signa<sub>nu</sub>´. In this paper the effect of fine percent also is considered on the V<sub>s</sub> estimation.</p>

scholarly journals An integrated shear-wave velocity model for the Groningen gas field, The Netherlands

2017 ◽  
Vol 15 (9) ◽  
pp. 3555-3580 ◽  
Author(s):  
Pauline P. Kruiver ◽  
Ewoud van Dedem ◽  
Remco Romijn ◽  
Ger de Lange ◽  
Mandy Korff ◽  
...  
Keyword(s):  
The Netherlands ◽  
Shear Wave ◽  
Wave Velocity ◽  
Shear Wave Velocity ◽  
Velocity Model ◽  
Gas Field ◽  
Groningen Gas Field

scholarly journals Machine Learning: A Useful Tool in Geomechanical Studies, a Case Study from an Offshore Gas Field

Energies ◽  
2020 ◽  
Vol 13 (14) ◽  
pp. 3528 ◽  
Author(s):  
Seyedalireza Khatibi ◽  
Azadeh Aghajanpour
Keyword(s):  
Machine Learning ◽  
Shear Wave ◽  
Wave Velocity ◽  
Shear Wave Velocity ◽  
Linear Method ◽  
Earth Model ◽  
Learning Approaches ◽  
Gas Field ◽  
In Situ Stresses

For a safe drilling operation with the of minimum borehole instability challenges, building a mechanical earth model (MEM) has proven to be extremely valuable. However, the natural complexity of reservoirs along with the lack of reliable information leads to a poor prediction of geomechanical parameters. Shear wave velocity has many applications, such as in petrophysical and geophysical as well as geomechanical studies. However, occasionally, wells lack shear wave velocity (especially in old wells), and estimating this parameter using other well logs is the optimum solution. Generally, available empirical relationships are being used, while they can only describe similar formations and their validation needs calibration. In this study, machine learning approaches for shear sonic log prediction were used. The results were then compared with each other and the empirical Greenberg–Castagna method. Results showed that the artificial neural network has the highest accuracy of the predictions over the single and multiple linear regression models. This improvement is more highlighted in hydrocarbon-bearing intervals, which is considered as a limitation of the empirical or any linear method. In the next step, rock elastic properties and in-situ stresses were calculated. Afterwards, in-situ stresses were predicted and coupled with a failure criterion to yield safe mud weight windows for wells in the field. Predicted drilling events matched quite well with the observed drilling reports.

scholarly journals Vs30Estimate for Southwest China

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Yan Yu ◽  
Walter J. Silva ◽  
Bob Darragh ◽  
Xiaojun Li
Keyword(s):  
Shear Wave ◽  
Wave Velocity ◽  
Shear Wave Velocity ◽  
Southwest China ◽  
Strong Motion ◽  
Site Classification ◽  
Borehole Data ◽  
Data Set ◽  
Average Shear ◽  
Shear Wave Velocity Profile

Several methods were used to estimateVs30from site profiles with borehole depths of about 20 m for the strong-motion stations located in Southwest China. The methods implemented include extrapolation (constant and gradient), Geomatrix Site Classification correlation with shear-wave velocity, and remote sensing (terrain and topography). The gradient extrapolation is the preferred choice of this study for sites with shear-wave velocity profile data. However, it is noted that the coefficients derived from the California data set are not applicable to sites in Southwest China. Due to the scarcity of borehole profiles data with depth of more than 30 m in Southwest China, 73 Kiknet profiles were used to generate new coefficients for gradient extrapolation. Fortunately, these coefficients provide a reasonable estimate ofVs30for sites in Southwest China. This study showedVs30could be estimated by the time-average shear-wave velocity (average slowness) of only 10 meters of depth. Furthermore, a medianVs30estimate based upon Geomatrix Classification is derived from the results of the gradient extrapolation using a regional calibration of the Geomatrix Classification withVs30. The results of this study can be applied to assignVs30to the sites without borehole data in Southwest China.

scholarly journals Correlation between Shear Wave Velocity and Porosity in Porous Solids and Rocks

2013 ◽  
Vol 2013 ◽  
pp. 1-3
Author(s):  
J. Kováčik ◽  
Š. Emmer
Keyword(s):  
Experimental Data ◽  
Shear Modulus ◽  
Shear Wave ◽  
Wave Velocity ◽  
Power Law ◽  
Shear Wave Velocity ◽  
Porous Solids ◽  
Porous Rocks ◽  
Data Set ◽  
Porosity Dependence

The shear wave velocity dependence on porosity was modelled using percolation theory model for the shear modulus porosity dependence. The obtained model is not a power law dependence (no simple scaling with porosity), but a more complex equation. Control parameters of this equation are shear wave velocity of bulk solid, percolation threshold of the material and the characteristic power law exponent for shear modulus porosity dependence. This model is suitable for all porous materials, mortars and porous rocks filled with liquid or gas. In the case of pores filled with gas the model can be further simplified: The term for the ratio of the gas density to the density of solid material can be omitted in the denominator (the ratio is usually in the range of (10−4, 10−3) for all solids). This simplified equation was then tested on the experimental data set for porous ZnO filled with air. Due to lack of reasonable data the scientists are encouraged to test the validity of proposed model using their experimental data.

Topographically-Derived Near-Surface Shear Wave Velocity Map for Pakistan

2017 ◽  
Vol 11 (02) ◽  
pp. 1650010 ◽  
Author(s):  
Saeed Zaman ◽  
Pennung Warnitchai
Keyword(s):  
Shear Wave ◽  
Wave Velocity ◽  
Shear Wave Velocity ◽  
Site Response ◽  
Response Analysis ◽  
Site Classification ◽  
Site Conditions ◽  
Data Set ◽  
Near Surface ◽  
Topographic Slope

Shear wave velocity ([Formula: see text]) through the uppermost subsurface (30 m) is usually considered an important parameter as it dictates the dynamic behavior of soil and also acts as an input parameter for site response analysis, seismic hazard analysis, and site classification. In majority of seismically active areas across the globe, especially in developing countries like Pakistan, the [Formula: see text] measurements are either not available or if available, they are very limited in number to develop a seismic site-conditions map. In the absence of proper geological studies and geotechnical investigation, the slope-derived method provides a simple solution to map the site-conditions. The current study presents the development of slope-derived [Formula: see text] map on the basis of a correlation between [Formula: see text] and topographic slope for active tectonic regions and its comparison with the [Formula: see text] values at various locations in Pakistan. The topographic slope is calculated from digital elevation model (CDEM) of the Shuttle Radar Topography Mission (SRTM) 30 arc-sec global topographic data set. The [Formula: see text] values comprise of directly available, values calculated/estimated from the standard penetration tests (SPTs [Formula: see text]-value) and primary waves at various locations in Pakistan. [Formula: see text] values at various parts/locations in Pakistan and values from the slope-derived [Formula: see text] map are found to be fairly comparable and based on these results for seismically active areas like Pakistan, slope-derived method can be applied for the first-order site-condition studies.

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