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.

S-wave velocity prediction in unconventional shale reservoirs

Geophysics ◽  
2018 ◽  
Vol 83 (1) ◽  
pp. MR35-MR45 ◽  
Author(s):  
Lev Vernik ◽  
John Castagna ◽  
Sheyore John Omovie
Keyword(s):  
Wave Velocity ◽  
Prediction Method ◽  
Organic Content ◽  
P Wave ◽  
Unconventional Reservoirs ◽  
Hybrid Technique ◽  
S Wave ◽  
P Wave Velocity ◽  
Velocity Prediction ◽  
Shale Reservoirs

In unconventional reservoirs with significant organic content, the Greenberg-Castagna (GC) S-wave velocity prediction method does not yield accurate S-wave velocity predictions, with observed mean errors varying from 6% to 16% in a variety of unconventional reservoirs rich in organic content. This is because kerogen content is not explicitly taken into account in the GC S-wave velocity prediction method. Two alternative approaches for bedding-normal S-wave velocity prediction from P-wave velocity and other well logs in relatively straight holes drilled in unconventional reservoirs are investigated. The first method is purely empirical, requiring minimal information such as P-wave velocity and total organic carbon content. This approach implicitly accounts for compositional and stress effects on mudrock elasticity. The second method can be classified as a hybrid technique, comprising the following three steps: (1) computing a nonkerogen phase P-wave velocity using the Vernik-Kachanov (VK) model, (2) determination of the nonkerogen S-wave velocity from the GC approach, and (3) using a simplified VK model to mix the nonkerogen matrix with nanoporous kerogen to predict the bedding-normal S-wave velocity of the organic mudrock. The second method explicitly takes into account all the variables that control elastic properties of organic mudrock reservoirs. Tests in nine wells from seven different oil and gas shale reservoirs indicate that both methods have prediction accuracy better than 3% error when input data are accurate.

The relationship between acoustic properties and the petrographic character of carbonate rocks

Geophysics ◽  
1984 ◽  
Vol 49 (10) ◽  
pp. 1622-1636 ◽  
Author(s):  
F. Rafavich ◽  
C. H. St. C. Kendall ◽  
T. P. Todd
Keyword(s):  
Wave Velocity ◽  
Carbonate Rocks ◽  
Wave Impedance ◽  
Acoustic Properties ◽  
P Wave ◽  
S Wave ◽  
Seismic Section ◽  
Small Influence ◽  
In Situ Conditions ◽  
Wide Range

Laboratory studies of the detailed relationships between acoustic properties and the petrographic character of brine‐ and air‐saturated carbonate rocks with a wide range of facies, porosities, lithologies, and rock fabrics indicate that porosity is the major factor influencing both P- and S-wave impedance and velocity. Primary lithology and secondary mineralogy have only a small influence on impedance and velocity. Combined use of P- and S-wave velocity data discriminates porosity changes from lithologic changes. All other variables, including pore‐fluid type and petrographic fabric, have no significant influence on velocities. Laboratory measurements of P‐wave velocity under simulated in‐situ conditions reproduce well‐log velocity values reliably. Laboratory porosity‐velocity trends agree with the time‐average equation when the correct matrix velocities are used. Rock property results were used to interpret porosity/lithology variations for an inverted seismic section from the Williston basin. Where well control was available, the porosity/lithology interpretation was found to be in agreement with the subsurface control.

Inverse design of layered periodic wave barriers based on deep learning

2021 ◽  
pp. 146442072110168
Author(s):  
Chen-Xu Liu ◽  
Gui-Lan Yu
Keyword(s):  
Deep Learning ◽  
Activation Function ◽  
Periodic Wave ◽  
P Wave ◽  
Soil Parameters ◽  
S Wave ◽  
Output Frequency ◽  
Great Generality ◽  
Typical Range ◽  
Deep Learning Model

This study presents an approach based on deep learning to design layered periodic wave barriers with consideration of typical range of soil parameters. Three cases are considered where P wave and S wave exist separately or simultaneously. The deep learning model is composed of an autoencoder with a pretrained decoder which has three branches to output frequency attenuation domains for three different cases. A periodic activation function is used to improve the design accuracy, and condition variables are applied in the code layer of the autoencoder to meet the requirements of practical multi working conditions. Forty thousand sets of data are generated to train, validate, and test the model, and the designed results are highly consistent with the targets. The presented approach has great generality, feasibility, rapidity, and accuracy on designing layered periodic wave barriers which exhibit good performance in wave suppression in targeted frequency range.

Numerical and Theoretical Investigation on the Origin of the Multiple Peaks of the H/V Ratio Curve

2021 ◽  
Author(s):  
Wanbo Xiao ◽  
Siqi Lu ◽  
Yanbin Wang
Keyword(s):  
Rayleigh Wave ◽  
Wave Velocity ◽  
Subsurface Layer ◽  
P Wave ◽  
Theoretical Formula ◽  
S Wave ◽  
Multiple Peaks ◽  
Ratio Curve ◽  
Multiple Layer ◽  
Wave Resonance

<p>Despite the popularity of the horizontal to vertical spectral ratio (HVSR) method in site effect studies, the origin of the H/V peaks has been controversial since this method was proposed. Many previous studies mainly focused on the explanation of the first or single peak of the H/V ratio, trying to distinguish between the two hypotheses — the S-wave resonance and ellipticity of Rayleigh wave. However, it is common both in numerical simulations and practical experiments that the H/V ratio exhibits multiple peaks, which is essential to explore the origin of the H/V peaks.</p><p>The cause for the multiple H/V peaks has not been clearly figured out, and once was simply explained as the result of multi subsurface layers. Therefore, we adopted numerical method to simulate the ambient noise in various layered half-space models and calculated the H/V ratio curves for further comparisons. The peak frequencies of the H/V curves accord well with the theoretical frequencies of S-wave resonance in two-layer models, whose frequencies only depend on the S wave velocity and the thickness of the subsurface layer. The same is true for models with varying model parameters. Besides, the theoretical formula of the S-wave resonance in multiple-layer models is proposed and then supported by numerical investigations as in the cases of two-layer models. We also extended the S-wave resonance to P-wave resonance and found that its theoretical frequencies fit well with the V/H peaks, which could be an evidence to support the S-wave resonance theory from a new perspective. By contrast, there are obvious differences between the higher orders of the H/V ratio peaks and the higher orders of Rayleigh wave ellipticity curves both in two-layer and multiple-layer models. The Rayleigh wave ellipticity curves are found to be sensitive to the Poisson’s ratio and the thickness of the subsurface layer, so the variation of the P wave velocity can affect the peak frequencies of the Rayleigh wave ellipticity curves while the H/V peaks show slight change. The Rayleigh wave ellipticity theory is thus proved to be inappropriate for the explanation of the multiple H/V peaks, while the possible effects of the Rayleigh wave on the fundamental H/V peak still cannot be excluded.</p><p>Based on the analyses above, we proposed a new evidence to support the claim that the peak frequencies of the H/V ratio curve, except the fundamental peaks, are caused by S-wave resonance. The relationship between the P-wave resonance and the V/H peaks may also find further application.</p>

Relating Acoustic Anisotropy to Kerogen Content in Unconventional Formations - A Case Study in A Kerogen-Rich Unconventional Carbonate

2021 ◽  
Author(s):  
Yair Gordin ◽  
Thomas Bradley ◽  
Yoav O. Rosenberg ◽  
Anat Canning ◽  
Yossef H. Hatzor ◽  
...  
Keyword(s):  
Wave Velocity ◽  
Water Saturation ◽  
Well Logs ◽  
Rock Properties ◽  
Thermal Maturity ◽  
Burial Diagenesis ◽  
S Wave ◽  
Velocity Anisotropy ◽  
Thermal Maturation ◽  
Wide Range

Abstract The mechanical and petrophysical behavior of organic-rich carbonates (ORC) is affected significantly by burial diagenesis and the thermal maturation of their organic matter. Therefore, establishing Rock Physics (RP) relations and appropriate models can be valuable in delineating the spatial distribution of key rock properties such as the total organic carbon (TOC), porosity, water saturation, and thermal maturity in the petroleum system. These key rock properties are of most importance to evaluate during hydrocarbon exploration and production operations when establishing a detailed subsurface model is critical. High-resolution reservoir models are typically based on the inversion of seismic data to calculate the seismic layer properties such as P- and S-wave impedances (or velocities), density, Poisson's ratio, Vp/Vs ratio, etc. If velocity anisotropy data are also available, then another layer of data can be used as input for the subsurface model leading to a better understanding of the geological section. The challenge is to establish reliable geostatistical relations between these seismic layer measurements and petrophysical/geomechanical properties using well logs and laboratory measurements. In this study, we developed RP models to predict the organic richness (TOC of 1-15 wt%), porosity (7-35 %), water saturation, and thermal maturity (Tmax of 420-435⁰C) of the organic-rich carbonate sections using well logs and laboratory core measurements derived from the Ness 5 well drilled in the Golan Basin (950-1350 m). The RP models are based primarily on the modified lower Hashin-Shtrikman bounds (MLHS) and Gassmann's fluid substitution equations. These organic-rich carbonate sections are unique in their relatively low burial diagenetic stage characterized by a wide range of porosity which decreases with depth, and thermal maturation which increases with depth (from immature up to the oil window). As confirmation of the method, the levels of organic content and maturity were confirmed using Rock-Eval pyrolysis data. Following the RP analysis, horizontal (HTI) and vertical (VTI) S-wave velocity anisotropy were analyzed using cross-dipole shear well logs (based on Stoneley waves response). It was found that anisotropy, in addition to the RP analysis, can assist in delineating the organic-rich sections, microfractures, and changes in gas saturation due to thermal maturation. Specifically, increasing thermal maturation enhances VTI and azimuthal HTI S-wave velocity anisotropies, in the ductile and brittle sections, respectively. The observed relationships are quite robust based on the high-quality laboratory and log data. However, our conclusions may be limited to the early stages of maturation and burial diagenesis, as at higher maturation and diagenesis the changes in physical properties can vary significantly.

Using Seismic Data to Predict Shale Pore Pressure and Overpressure Sweet Spots: A Case Study from the Lower Silurian Longmaxi Formation in Sichuan Basin, China

2021 ◽  
Author(s):  
Sheng Chen ◽  
Qingcai Zeng ◽  
Xiujiao Wang ◽  
Qing Yang ◽  
Chunmeng Dai ◽  
...  
Keyword(s):  
Prediction Model ◽  
Wave Velocity ◽  
Shale Gas ◽  
Seismic Data ◽  
P Wave ◽  
Formation Pressure ◽  
S Wave ◽  
P Wave Velocity ◽  
New Formation ◽  
Sweet Spots

Abstract Practices of marine shale gas exploration and development in south China have proved that formation overpressure is the main controlling factor of shale gas enrichment and an indicator of good preservation condition. Accurate prediction of formation pressure before drilling is necessary for drilling safety and important for sweet spots predicting and horizontal wells deploying. However, the existing prediction methods of formation pore pressures all have defects, the prediction accuracy unsatisfactory for shale gas development. By means of rock mechanics analysis and related formulas, we derived a formula for calculating formation pore pressures. Through regional rock physical analysis, we determined and optimized the relevant parameters in the formula, and established a new formation pressure prediction model considering P-wave velocity, S-wave velocity and density. Based on regional exploration wells and 3D seismic data, we carried out pre-stack seismic inversion to obtain high-precision P-wave velocity, S-wave velocity and density data volumes. We utilized the new formation pressure prediction model to predict the pressure and the spatial distribution of overpressure sweet spots. Then, we applied the measured pressure data of three new wells to verify the predicted formation pressure by seismic data. The result shows that the new method has a higher accuracy. This method is qualified for safe drilling and prediction of overpressure sweet spots for shale gas development, so it is worthy of promotion.

Constraints on the composition of the crust and uppermost mantle in northwestern Canada: Vp/Vs variations along Lithoprobe's SNorCLE transect

2005 ◽  
Vol 42 (6) ◽  
pp. 1205-1222 ◽  
Author(s):  
Gabriela Fernández-Viejo ◽  
Ron M Clowes ◽  
J Kim Welford
Keyword(s):  
Wave Velocity ◽  
Upper Mantle ◽  
Laboratory Data ◽  
High Ratio ◽  
P Wave ◽  
S Wave ◽  
Uppermost Mantle ◽  
Crust And Upper Mantle ◽  
Slave Craton ◽  
Crustal Composition

Shear-wave seismic data recorded along four profiles during the SNoRE 97 (1997 Slave – Northern Cordillera Refraction Experiment) refraction – wide-angle reflection experiment in northwestern Canada are analyzed to provide S-wave velocity (Vs) models. These are combined with previous P-wave velocity (Vp) models to produce cross sections of the ratio Vp/Vs for the crust and upper mantle. The Vp/Vs values are related to rock types through comparisons with published laboratory data. The Slave craton has low Vp/Vs values of 1.68–1.72, indicating a predominantly silicic crustal composition. Higher values (1.78) for the Great Bear and eastern Hottah domains of the Wopmay orogen imply a more mafic than average crustal composition. In the western Hottah and Fort Simpson arc, values of Vp/Vs drop to ∼1.69. These low values continue westward for 700 km into the Foreland and Omineca belts of the Cordillera, providing support for the interpretation from coincident seismic reflection studies that much of the crust from east of the Cordilleran deformation front to the Stikinia terrane of the Intermontane Belt consists of quartzose metasedimentary rocks. Stikinia shows values of 1.78–1.73, consistent with its derivation as a volcanic arc terrane. Upper mantle velocity and ratio values beneath the Slave craton indicate an ultramafic peridotitic composition. In the Wopmay orogen, the presence of low Vp/Vs ratios beneath the Hottah – Fort Simpson transition indicates the presence of pyroxenite in the upper mantle. Across the northern Cordillera, low Vp values and a moderate-to-high ratio in the uppermost mantle are consistent with the region's high heat flow and the possible presence of partial melt.

Nonlinear two‐dimensional elastic inversion of multioffset seismic data

Geophysics ◽  
1987 ◽  
Vol 52 (9) ◽  
pp. 1211-1228 ◽  
Author(s):  
Peter Mora
Keyword(s):  
Wave Equation ◽  
Wave Velocity ◽  
A Priori ◽  
Gaussian Model ◽  
Wave Impedance ◽  
P Wave ◽  
S Wave ◽  
Elastic Wave Equation ◽  
Gradient Direction ◽  
P Wave Velocity

The treatment of multioffset seismic data as an acoustic wave field is becoming increasingly disturbing to many geophysicists who see a multitude of wave phenomena, such as amplitude‐offset variations and shearwave events, which can only be explained by using the more correct elastic wave equation. Not only are such phenomena ignored by acoustic theory, but they are also treated as undesirable noise when they should be used to provide extra information, such as S‐wave velocity, about the subsurface. The problems of using the conventional acoustic wave equation approach can be eliminated via an elastic approach. In this paper, equations have been derived to perform an inversion for P‐wave velocity, S‐wave velocity, and density as well as the P‐wave impedance, S‐wave impedance, and density. These are better resolved than the Lamé parameters. The inversion is based on nonlinear least squares and proceeds by iteratively updating the earth parameters until a good fit is achieved between the observed data and the modeled data corresponding to these earth parameters. The iterations are based on the preconditioned conjugate gradient algorithm. The fundamental requirement of such a least‐squares algorithm is the gradient direction which tells how to update the model parameters. The gradient direction can be derived directly from the wave equation and it may be computed by several wave propagations. Although in principle any scheme could be chosen to perform the wave propagations, the elastic finite‐ difference method is used because it directly simulates the elastic wave equation and can handle complex, and thus realistic, distributions of elastic parameters. This method of inversion is costly since it is similar to an iterative prestack shot‐profile migration. However, it has greater power than any migration since it solves for the P‐wave velocity, S‐wave velocity, and density and can handle very general situations including transmission problems. Three main weaknesses of this technique are that it requires fairly accurate a priori knowledge of the low‐ wavenumber velocity model, it assumes Gaussian model statistics, and it is very computer‐intensive. All these problems seem surmountable. The low‐wavenumber information can be obtained either by a prior tomographic step, by the conventional normal‐moveout method, by a priori knowledge and empirical relationships, or by adding an additional inversion step for low wavenumbers to each iteration. The Gaussian statistics can be altered by preconditioning the gradient direction, perhaps to make the solution blocky in appearance like well logs, or by using large model variances in the inversion to reduce the effect of the Gaussian model constraints. Moreover, with some improvements to the algorithm and more parallel computers, it is hoped the technique will soon become routinely feasible.

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