TitleApplication of M4C P-wave and S-wave Joint Inversion in Shallow Reservoir Identification

2021 ◽  
Author(s):  
Sian Zhu ◽  
Hongtao Chen ◽  
Yongjun Hu ◽  
Feng Yang ◽  
Yubin Feng
Keyword(s):  
Wave Velocity ◽  
Oil And Gas ◽  
Joint Inversion ◽  
Wave Impedance ◽  
P Wave ◽  
Seismic Survey ◽  
Bohai Bay ◽  
S Wave ◽  
Bohai Bay Basin ◽  
Gas Reservoirs

Abstract The genetic mechanism of shallow gas reservoirs is complex, which is usually characterized by shallow burial depth, multiple types, low reserves and wide distribution, so that the inversion based on P-wave data alone may be ambiguous. For shallow reservoir with large lateral variation, it is hard to accurately predict oil and water distribution by conventional P-wave prestack inversion. Marine four-component (M4C) P-wave and S-wave joint inversion can solve the problem effectively. M4C seismic survey collects P-wave and S-wave seismic data. An initial model can be established based on fine structural interpretation of P-wave and S-wave data and S-wave compression pattern matching. It lays a good foundation for subsequent P-wave and S-wave joint inversion. Based on the P-wave seismic record equation proposed by Fatti et al., a seismic record equation from poststack P-wave and S-wave joint inversion was derived according to the relationship among reflection coefficient, P-wave impedance, S-wave impedance and density, then important lithologic parameters (P-wave impedance, S-wave impedance and density) were calculated, and finally the ratio of P-wave velocity to S-wave velocity which is more sensitive to oil and gas was obtained. According to the ratio of P-wave velocity to S-wave velocity, the oil and gas distribution was predicted in shallow Bohai Bay Basin. Application has proven that the inversion can well reflect the fluid distribution, and the coincidence between the inversion results and the drilling data is up to 85%.M4C seismic survey was conducted for the first time to the shallow oil and gas reservoirs with rapid lateral variation in the Bohai Bay Basin, and collected raw P-wave and S-wave seismic data. Based on the data, the precision and reliability of P-wave and S-wave joint inversion was improved. The results provided strong technical support to the reserves and production increase in the Bohai Bay Basin.

scholarly journals Quadri-joint inversion: Method and application to the Big Sky 9C 3D data set in northern Montana

2018 ◽  
Vol 6 (4) ◽  
pp. SN101-SN118 ◽  
Author(s):  
Vincent Clochard ◽  
Bryan C. DeVault ◽  
David Bowen ◽  
Nicolas Delépine ◽  
Kanokkarn Wangkawong
Keyword(s):  
Reservoir Characterization ◽  
Joint Inversion ◽  
Wave Impedance ◽  
P Wave ◽  
Seismic Survey ◽  
Time Shift ◽  
Inversion Method ◽  
S Wave ◽  
Data Set ◽  
Long Term Storage

The Kevin Dome [Formula: see text] storage project, located in northern Montana, attempted to characterize the Duperow Formation as a potential long-term storage zone for injected [Formula: see text]. A multicomponent (9C) seismic survey was acquired for the Big Sky Carbon Sequestration Partnership over a portion of the Kevin Dome using P- and S-wave sources. Prestack migrated PP, PS, SH, and SV data sets were generated. We then applied several stratigraphic inversion workflows using one or several kinds of seismic wavefield at the same time resulting in joint inversions of each data set. The aim of our study is to demonstrate the benefits of doing quadri-joint inversion of PP-, PS-, SH-, and SV-wavefields for the recovery of the elastic earth parameters, especially the S-wave impedance and density. These are crucial parameters because they can help determine lithology and porefill in the reservoir characterization workflow. Because the inversion workflow always uses the original seismic data recorded in its own time domain, it is necessary to compute registration laws between PP-PS-, PP-SH-, and PP-SV-wavefields using a time shift computation procedure (warping) based on inverted S-wave impedances from inversion of a single wavefield. This generated a significant improvement over methods that rely on attempting to match trace waveforms that may have a different phase, frequency content, and polarity. Finally, we wanted to investigate the reliability of the quadri-joint inversion results in the Bakken/Banff Formations, which have less lateral geologic variation than the underlying Duperow target. This interval shares many of the geophysical characterization challenges common to shale reservoirs in other North American basins. We computed geomechanical parameters, such as Poisson’s ratio and Young’s modulus, which are a proxy for brittleness. Comparison of these results with independent laboratory measurements in the Bakken interval demonstrates the superiority of the quadri-joint inversion method to the traditional inversion using P-wave data only.

Poststack, prestack, and joint inversion of P- and S-wave data for Morrow A sandstone characterization

2015 ◽  
Vol 3 (3) ◽  
pp. SZ59-SZ92 ◽  
Author(s):  
Paritosh Singh ◽  
Thomas L. Davis ◽  
Bryan DeVault
Keyword(s):  
Wave Velocity ◽  
Seismic Data ◽  
Joint Inversion ◽  
Wave Impedance ◽  
P Wave ◽  
S Wave ◽  
Wave Data ◽  
Wave Inversion ◽  
Density Maps ◽  
P Wave Velocity

Exploration for oil-bearing Morrow sandstones using conventional seismic data/methods has a startlingly low success rate of only 3%. The S-wave velocity contrast between the Morrow shale and A sandstone is strong compared with the P-wave velocity contrast, and, therefore, multicomponent seismic data could help to characterize these reservoirs. The SV and SH data used in this study are generated using S-wave data from horizontal source and horizontal receiver recording. Prestack P- and S-wave inversions, and joint P- and S-wave inversions, provide estimates of P- and S-wave impedances, and density for characterization of the Morrow A sandstone. Due to the weak P-wave amplitude-versus-angle response at the Morrow A sandstone top, the density and S-wave impedance estimated from joint P- and S-wave inversions were inferior to the prestack S-wave inversion. The inversion results were compared with the Morrow A sandstone thickness and density maps obtained from well logs to select the final impedance and density volume for interpretation. The P-wave impedance estimated from prestack P-wave data, as well as density and S-wave impedance estimated from prestack SV‐wave data were used to identify the distribution, thickness, quality, and porosity of the Morrow A sandstone. The stratal slicing method was used to get the P- and S-wave impedances and density maps. The S-wave impedance characterizes the Morrow A sandstone distribution better than the P-wave impedance throughout the study area. Density estimation from prestack inversion of SV data was able to distinguish between low- and high-quality reservoirs. The porosity volume was estimated from the density obtained from prestack SV-wave inversion. We found some possible well locations based on the interpretation.

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.

Inversion of reflection traveltimes for transverse isotropy

Geophysics ◽  
1995 ◽  
Vol 60 (4) ◽  
pp. 1095-1107 ◽  
Author(s):  
Ilya Tsvankin ◽  
Leon Thomsen
Keyword(s):  
Wave Velocity ◽  
Vertical Velocity ◽  
Joint Inversion ◽  
Transverse Isotropy ◽  
Taylor Series Expansion ◽  
High Sensitivity ◽  
Transversely Isotropic ◽  
P Wave ◽  
S Wave ◽  
Quartic Term

In anisotropic media, the short‐spread stacking velocity is generally different from the root‐mean‐square vertical velocity. The influence of anisotropy makes it impossible to recover the vertical velocity (or the reflector depth) using hyperbolic moveout analysis on short‐spread, common‐midpoint (CMP) gathers, even if both P‐ and S‐waves are recorded. Hence, we examine the feasibility of inverting long‐spread (nonhyperbolic) reflection moveouts for parameters of transversely isotropic media with a vertical symmetry axis. One possible solution is to recover the quartic term of the Taylor series expansion for [Formula: see text] curves for P‐ and SV‐waves, and to use it to determine the anisotropy. However, this procedure turns out to be unstable because of the ambiguity in the joint inversion of intermediate‐spread (i.e., spreads of about 1.5 times the reflector depth) P and SV moveouts. The nonuniqueness cannot be overcome by using long spreads (twice as large as the reflector depth) if only P‐wave data are included. A general analysis of the P‐wave inverse problem proves the existence of a broad set of models with different vertical velocities, all of which provide a satisfactory fit to the exact traveltimes. This strong ambiguity is explained by a trade‐off between vertical velocity and the parameters of anisotropy on gathers with a limited angle coverage. The accuracy of the inversion procedure may be significantly increased by combining both long‐spread P and SV moveouts. The high sensitivity of the long‐spread SV moveout to the reflector depth permits a less ambiguous inversion. In some cases, the SV moveout alone may be used to recover the vertical S‐wave velocity, and hence the depth. Success of this inversion depends on the spreadlength and degree of SV‐wave velocity anisotropy, as well as on the constraints on the P‐wave vertical velocity.

Fractured tight sandstone oil and gas reservoirs: A new play type in the Dongpu depression, Bohai Bay Basin, China

AAPG Bulletin ◽  
2013 ◽  
Vol 97 (3) ◽  
pp. 363-377 ◽  
Author(s):  
Lianbo Zeng ◽  
Hui Su ◽  
Xiaomei Tang ◽  
Yongmin Peng ◽  
Lei Gong
Keyword(s):  
Oil And Gas ◽  
Bohai Bay ◽  
Bohai Bay Basin ◽  
Gas Reservoirs ◽  
Tight Sandstone ◽  
Dongpu Depression ◽  
Oil And Gas Reservoirs

scholarly journals Identifying recharge under subtle ephemeral features in flat-lying semi-arid region using a combined geophysical approach

2020 ◽  
Author(s):  
Brady A. Flinchum ◽  
Eddie Banks ◽  
Michael Hatch ◽  
Okke Batelaan ◽  
Luk Peeters ◽  
...  
Keyword(s):  
Wave Velocity ◽  
Arid Region ◽  
Geophysical Methods ◽  
P Wave ◽  
Seismic Survey ◽  
Small Scale ◽  
S Wave ◽  
Near Surface ◽  
Semi Arid Region ◽  
Semi Arid

Abstract. Identifying and quantifying recharge processes linked to ephemeral surface water features is challenging due to their episodic nature. We use a unique combination of well-established near-surface geophysical methods to provide evidence of a surface and groundwater connection under a small ephemeral recharge feature in a flat, semi-arid region near Adelaide, Australia. We use a seismic survey to obtain P-wave velocity through travel-time tomography and S-wave velocity through the multichannel analysis of surface waves. The ratios between P-wave and S-wave velocities allow us to infer the position of the water table. A separate survey was used to obtain electrical conductivity measurements from time-domain electromagnetics and water contents were acquired by downhole nuclear magnetic resonance. The combined geophysical observations provide evidence to support a groundwater mound underneath a subtle ephemeral feature. Our results suggest that recharge is localized and that small-scale ephemeral features play an important role in groundwater recharge. Furthermore, we show that a combined geophysical approach can provide a unique perspective that helps shape the hydrogeological conceptualization of a semi-arid region.

scholarly journals Hydrothermal Dolomite Paleokarst Reservoir Development in Wolonghe Gasfield, Sichuan Basin, Revealed by Seismic Characterization

Water ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 579
Author(s):  
Bole Gao ◽  
Fei Tian ◽  
Renfang Pan ◽  
Wenhao Zheng ◽  
Rong Li ◽  
...  
Keyword(s):  
Wave Velocity ◽  
Sichuan Basin ◽  
Oil And Gas ◽  
Carbonate Reservoir ◽  
P Wave ◽  
Distribution Model ◽  
S Wave ◽  
Secondary Porosity ◽  
Petrophysical Parameters ◽  
Hydrothermal Dolomite

Hydrothermal dolomite paleokarst reservoir is a type of porous carbonate reservoir, which has a secondary porosity and can store a large amount of oil and gas underground. The reservoir is formed by magnesium-rich hydrothermal fluids during the karstification and later stages of the transformation. Due to the strong heterogeneity and thin thickness of hydrothermal dolomite paleokarst reservoirs, it is a real challenge to characterize the spatial distribution of the reservoirs. In this paper, we studied the hydrothermal dolomite paleokarst reservoir in the Wolonghe gasfield of the eastern Sichuan Basin. First, based on detailed observations of core samples, the characteristics and storage space types of the dolomite reservoir were described. Secondly, the petrophysical parameters of the paleokarst reservoirs were analyzed, and then the indicator factor for the dolomite reservoirs was established. Thirdly, using the time–depth conversion method, the geological characteristics near boreholes were connected with a three-dimensional (3D) seismic dataset. Several petrophysical parameters were predicted by prestack synchronous inversion technology, including the P-wave velocity, S-wave velocity, P-wave impedance, and the hydrothermal dolomite paleokarst reservoir indicator factor. Finally, the hydrothermal dolomite paleokarst reservoirs were quantitatively predicted, and their distribution model was built. The 3D geophysical characterization approach improves our understanding of hydrothermal dolomite paleokarst reservoirs, and can also be applied to other similar heterogeneous reservoirs.

S-wave velocity prediction of marine-continental transitional coal measure rocks using multipole array sonic logs and the rock-matrix modulus extraction method

2019 ◽  
Vol 7 (1) ◽  
pp. T241-T253
Author(s):  
Siqi Wang ◽  
Jianguo Zhang ◽  
Shuai Yin ◽  
Chao Han
Keyword(s):  
Wave Velocity ◽  
Wave Impedance ◽  
Empirical Method ◽  
Computational Method ◽  
P Wave ◽  
Coal Measure ◽  
Rock Matrix ◽  
S Wave ◽  
Matrix Modulus ◽  
Sonic Logs

Accurate prediction of the S-wave velocity of highly heterogeneous coal measure strata using high-resolution logging can effectively identify high-quality reservoirs. We have used multipole array sonic logs to predict the S-wave velocity of coal measure strata based on the conventional empirical method (CEM), multiple regression method (MRM), and rock-matrix modulus extraction (MME) method. Moreover, we used a complex multiple parameter iterative computational method of forward calculation and inversion in the MME method. Our results indicate that the MME method can effectively extract several rock modulus parameters. There are good binomial relationships between the extracted rock modulus parameters ([Formula: see text]) and between the extracted modulus parameters and the P-wave impedance ([Formula: see text]). The average relative errors of the S-wave velocities predicted by the CEM, MRM, and MME methods are 7.58%, 5.64%, and 2.31%, respectively. The MME method can effectively extract and couple effective information from different types of conventional well logs and perform high-precision S-wave time difference prediction.

scholarly journals Identifying recharge under subtle ephemeral features in a flat-lying semi-arid region using a combined geophysical approach

2020 ◽  
Vol 24 (9) ◽  
pp. 4353-4368 ◽  
Author(s):  
Brady A. Flinchum ◽  
Eddie Banks ◽  
Michael Hatch ◽  
Okke Batelaan ◽  
Luk J. M. Peeters ◽  
...  
Keyword(s):  
Surface Water ◽  
Wave Velocity ◽  
Arid Region ◽  
P Wave ◽  
Seismic Survey ◽  
Small Scale ◽  
S Wave ◽  
Geophysical Surveys ◽  
Semi Arid Region ◽  
Semi Arid

Abstract. Identifying and quantifying recharge processes linked to ephemeral surface water features is challenging due to their episodic nature. We use a combination of well-established near-surface geophysical methods to provide evidence of a surface and groundwater connection under a small ephemeral recharge feature in a flat, semi-arid region near Adelaide, Australia. We use a seismic survey to obtain P-wave velocity through travel-time tomography and S-wave velocity through the multichannel analysis of surface waves. The ratios between P-wave and S-wave velocities are used to calculate Poisson's ratio, which allow us to infer the position of the water table. Separate geophysical surveys were used to obtain electrical conductivity measurements from time-domain electromagnetics and water contents from downhole nuclear magnetic resonance. The geophysical observations provide evidence to support a groundwater mound underneath a subtle ephemeral surface water feature. Our results suggest that recharge is localized and that small-scale ephemeral features may play an important role in groundwater recharge. Furthermore, we show that a combined geophysical approach can provide a perspective that helps shape the hydrogeological conceptualization of a semi-arid region.

A strategy for nonlinear elastic inversion of seismic reflection data

Geophysics ◽  
1986 ◽  
Vol 51 (10) ◽  
pp. 1893-1903 ◽  
Author(s):  
Albert Tarantola
Keyword(s):  
Wave Velocity ◽  
Seismic Reflection ◽  
Wave Impedance ◽  
P Wave ◽  
S Wave ◽  
Seismic Reflection Data ◽  
S Waves ◽  
Wave Velocities ◽  
Reflection Data ◽  
P Wave Velocity

The problem of interpretation of seismic reflection data can be posed with sufficient generality using the concepts of inverse theory. In its roughest formulation, the inverse problem consists of obtaining the Earth model for which the predicted data best fit the observed data. If an adequate forward model is used, this best model will give the best images of the Earth’s interior. Three parameters are needed for describing a perfectly elastic, isotropic, Earth: the density ρ(x) and the Lamé parameters λ(x) and μ(x), or the density ρ(x) and the P-wave and S-wave velocities α(x) and β(x). The choice of parameters is not neutral, in the sense that although theoretically equivalent, if they are not adequately chosen the numerical algorithms in the inversion can be inefficient. In the long (spatial) wavelengths of the model, adequate parameters are the P-wave and S-wave velocities, while in the short (spatial) wavelengths, P-wave impedance, S-wave impedance, and density are adequate. The problem of inversion of waveforms is highly nonlinear for the long wavelengths of the velocities, while it is reasonably linear for the short wavelengths of the impedances and density. Furthermore, this parameterization defines a highly hierarchical problem: the long wavelengths of the P-wave velocity and short wavelengths of the P-wave impedance are much more important parameters than their counterparts for S-waves (in terms of interpreting observed amplitudes), and the latter are much more important than the density. This suggests solving the general inverse problem (which must involve all the parameters) by first optimizing for the P-wave velocity and impedance, then optimizing for the S-wave velocity and impedance, and finally optimizing for density. The first part of the problem of obtaining the long wavelengths of the P-wave velocity and the short wavelengths of the P-wave impedance is similar to the problem solved by present industrial practice (for accurate data interpretation through velocity analysis and “prestack migration”). In fact, the method proposed here produces (as a byproduct) a generalization to the elastic case of the equations of “prestack acoustic migration.” Once an adequate model of the long wavelengths of the P-wave velocity and of the short wavelengths of the P-wave impedance has been obtained, the data residuals should essentially contain information on S-waves (essentially P-S and S-P converted waves). Once the corresponding model of S-wave velocity (long wavelengths) and S-wave impedance (short wavelengths) has been obtained, and if the remaining residuals still contain information, an optimization for density should be performed (the short wavelengths of impedances do not give independent information on density and velocity independently). Because the problem is nonlinear, the whole process should be iterated to convergence; however, the information from each parameter should be independent enough for an interesting first solution.

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