scholarly journals Prestack Inversion Identification of Dolomite Reservoirs in the Fourth Member of the Sinian Dengying Formation in Moxi Area, Sichuan Basin, SW China

Geofluids ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Yong Wu ◽  
Xuxu Wang ◽  
Lu Zhou ◽  
Chongyang Han ◽  
Lianjin Zhang ◽  
...  
Keyword(s):  
Wave Velocity ◽  
Sichuan Basin ◽  
Velocity Ratio ◽  
Wave Impedance ◽  
S Wave ◽  
Prestack Inversion ◽  
Fluid Factor ◽  
Dengying Formation ◽  
Gas Bearing ◽  
Fourth Member

The dolomite reservoir of the fourth member of Dengying Formation in Moxi area of Sichuan Basin is thin, is fast in lateral variation, and has P-impedance difference from the surrounding rock; it is difficult to identify and predict the dolomite reservoir and fluid properties by conventional poststack seismic inversion. Through the correlation analysis of core test data and logging P-S-wave velocity, this work proposed a formula to calculate the shear wave velocity in different porosity ranges and solved the issue that some wells in the study area have no S-wave logging data. AVO forward analysis reveals that whether the gas reservoir of dolomite reservoir is located at the top of the fourth member of Dengying Formation is the main factor affecting the variation of AVO type. Through cross-plotting analysis of elastic parameters, it is found that P-S-wave velocity ratio and fluid factor are sensitive parameters to gas-bearing property of dolomite reservoir in the study area. By comparing the inversion results of prestack parameters such as density, P-wave impedance, S-wave impedance, P-S-wave velocity ratio, and fluid factor, it is found that the gas-bearing prediction of dolomite reservoir by using P-S-wave velocity ratio and fluid factor obtained from simultaneous prestack inversion had the highest coincidence rate with actual drilling data. At last, according to the distribution characteristics of fluid factor and P-S-wave velocity ratio, the favorable gas-bearing areas of dolomite reservoir in the fourth member of Dengying Formation in the study area are finely predicted, and the next favorable exploration areas were pointed out.

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.

scholarly journals Assessing uncertainties in high-resolution, multifrequency receiver-function inversion: A comparison with borehole data

Geophysics ◽  
2018 ◽  
Vol 83 (3) ◽  
pp. KS11-KS22 ◽  
Author(s):  
Nicola Piana Agostinetti ◽  
Alberto Malinverno
Keyword(s):  
Wave Velocity ◽  
Receiver Function ◽  
Joint Inversion ◽  
Seismic Station ◽  
Velocity Ratio ◽  
Earth Model ◽  
Elastic Model ◽  
S Wave ◽  
Borehole Data ◽  
Sonic Log

We use teleseismic P-to-S converted waves from a permanent station to estimate the uncertainties in a 1D elastic model of the shallow crust (0–7 km depth) obtained from the inversion of receiver function (RF) data. Our earth model consists of layers with a constant S-wave velocity [Formula: see text] and P- to S-wave velocity ratio ([Formula: see text]). We apply a Bayesian formulation and transdimensional Monte Carlo sampling to compute the posterior uncertainties of the earth model. The model uncertainties rely on a realistic representation of the data uncertainties, and we estimate directly from the stacking of the teleseismic data, a full-error covariance matrix. To explore the effect of the number of teleseismic events and the RF frequency content, we compare the results of inverting a single RF computed for a cut-off filter frequency of 4 Hz with the joint inversion of four RFs computed from independent ensembles in a larger pool of events for cut-off frequencies of 0.5, 1, 2, and 4 Hz. The inversion results are compared with the lithostratigraphy and sonic-log measurements from a 7 km deep borehole drilled near the seismic station. The inversion of a single RF results in larger uncertainties in the recovered [Formula: see text] profile and in the depth to seismic discontinuities compared with the multifrequency inversion. Moreover, the multifrequency inversion predicts more accurately the depth to a velocity inversion at approximately 6 km below the surface and matches more closely the borehole sonic-log data. Our results indicate that RF data can be used to map shallow (3–5 km depth) crustal interfaces with uncertainties in the order of 300–500 m, whereas uncertainties are consistently smaller (<300 m) for interfaces in the top kilometer.

Three-parameter prestack seismic inversion based on L1-2 minimization

Geophysics ◽  
2019 ◽  
Vol 84 (5) ◽  
pp. R753-R766 ◽  
Author(s):  
Lingqian Wang ◽  
Hui Zhou ◽  
Yufeng Wang ◽  
Bo Yu ◽  
Yuanpeng Zhang ◽  
...  
Keyword(s):  
Wave Velocity ◽  
Seismic Inversion ◽  
Thin Layers ◽  
P Wave ◽  
S Wave ◽  
Reservoir Prediction ◽  
Linear Inversion ◽  
Prestack Inversion ◽  
The Difference ◽  
Predictive Filtering

Prestack inversion has become a common approach in reservoir prediction. At present, the critical issue in the application of seismic inversion is the estimation of elastic parameters in the thin layers and weak reflectors. To improve the resolution and the accuracy of the inversion results, we introduced the difference of [Formula: see text] and [Formula: see text] norms as a nearly unbiased approximation of the sparsity of a vector, denoted as the [Formula: see text] norm, to the prestack inversion. The nonconvex penalty function of the [Formula: see text] norm can be decomposed into two convex subproblems via the difference of convex algorithm, and each subproblem can be solved efficiently by the alternating direction method of multipliers. Compared with the [Formula: see text] norm regularization, the [Formula: see text] minimization can reconstruct reflectivities more accurately. In addition, the [Formula: see text]-[Formula: see text] predictive filtering was introduced to guarantee the lateral continuity of the location and the amplitude of the reflectivity series. The generalized linear inversion and [Formula: see text]-[Formula: see text] predictive filtering are combined for stable elastic impedance inversion results, and three parameters of P-wave velocity, S-wave velocity, and density can be inverted with the Bayesian linearized amplitude variation with offset inversion. The inversion results of synthetic and real seismic data demonstrate that the proposed method can effectively improve the resolution and accuracy of the inversion results.

P-to-S-wave velocity ratio in organic shales

Geophysics ◽  
2019 ◽  
Vol 84 (6) ◽  
pp. MR205-MR222 ◽  
Author(s):  
Sheyore John Omovie ◽  
John P. Castagna
Keyword(s):  
Wave Velocity ◽  
Pore Space ◽  
Velocity Ratio ◽  
Organic Content ◽  
Theoretical Modeling ◽  
S Wave ◽  
Lower Velocity ◽  
Wave Velocities ◽  
Ultrasonic Measurements

In situ P- and S-wave velocity measurements in a variety of organic-rich shales exhibit P-to-S-wave velocity ratios that are significantly lower than lithologically similar fully brine-saturated shales having low organic content. It has been hypothesized that this drop could be explained by the direct influence of kerogen on the rock frame and/or by the presence of free hydrocarbons in the pore space. The correlation of hydrocarbon saturation with total organic content in situ makes it difficult to separate these possible mechanisms using log data alone. Theoretical bounding equations, using pure kerogen as an end-member component without associated gas, indicate that kerogen reduces the P- and S-wave velocities but does not in general reduce their ratio enough to explain the observed low velocity ratio. The theoretical modeling is consistent with ultrasonic measurements on organic shale core samples that indicate no dependence of velocity ratios on the kerogen volume alone. Sonic log measurements of P- and S-wave velocities in seven organic-rich shale formations deviate significantly (typically more than 5%) from the Greenberg-Castagna empirical brine-saturated shale trend toward lower velocity ratios. In these formations, and on core measurements, Gassmann fluid substitution to 100% brine saturation yields velocity ratios consistent with the Greenberg-Castagna velocity trend for fully brine-saturated shales, despite the high organic content. These sonic and ultrasonic measurements, as well as theoretical modeling, suggest that the velocity ratio reduction in organic shales is best explained by the presence of free hydrocarbons.

High‐resolution crosswell imaging of a west Texas carbonate reservoir: Part 5—Core analysis

Geophysics ◽  
1995 ◽  
Vol 60 (3) ◽  
pp. 712-726 ◽  
Author(s):  
Richard C. Nolen‐Hoeksema ◽  
Zhijing Wang ◽  
Jerry M. Harris ◽  
Robert T. Langan
Keyword(s):  
Wave Velocity ◽  
Field Experiments ◽  
Velocity Ratio ◽  
Carbonate Reservoir ◽  
P Wave ◽  
Core Analysis ◽  
S Wave ◽  
Traveltime Tomography ◽  
West Texas ◽  
P Wave Velocity

We conducted a core analysis program to provide supporting data to a series of crosswell field experiments being carried out in McElroy Field by Stanford University’s Seismic Tomography Project. The objective of these experiments is to demonstrate the use of crosswell seismic profiling for reservoir characterization and for monitoring [Formula: see text] flooding. For these west Texas carbonates, we estimate that [Formula: see text] saturation causes P‐wave velocity to change by −1.9% (pooled average, range = −6.3 to +0.1%), S‐wave velocity by +0.6% (range = 0 to 2.7%), and the P‐to‐S velocity ratio by −2.4% (range = −6.4 to −0.3%). When we compare these results to the precisions we can expect from traveltime tomography (about ±1% for P‐ and S‐wave velocity and about ±2% for the P‐to‐S velocity ratio), we conclude that time‐lapse traveltime tomography is sensitive enough to resolve changes in the P‐wave velocity, S‐wave velocity, and P‐to‐S velocity ratio that result from [Formula: see text] saturation. We concentrated here on the potential for [Formula: see text] saturation to affect seismic velocities. The potential for [Formula: see text] saturation to affect other seismic properties, not discussed here, may prove to be more significant (e.g., P‐wave and S‐wave impedance).

Bayesian linearized AVO inversion

Geophysics ◽  
2003 ◽  
Vol 68 (1) ◽  
pp. 185-198 ◽  
Author(s):  
Arild Buland ◽  
Henning Omre
Keyword(s):  
Wave Velocity ◽  
Posterior Distribution ◽  
Acoustic Impedance ◽  
Velocity Ratio ◽  
P Wave ◽  
Inversion Method ◽  
Inversion Algorithm ◽  
S Wave ◽  
Data Set ◽  
Avo Inversion

A new linearized AVO inversion technique is developed in a Bayesian framework. The objective is to obtain posterior distributions for P‐wave velocity, S‐wave velocity, and density. Distributions for other elastic parameters can also be assessed—for example, acoustic impedance, shear impedance, and P‐wave to S‐wave velocity ratio. The inversion algorithm is based on the convolutional model and a linearized weak contrast approximation of the Zoeppritz equation. The solution is represented by a Gaussian posterior distribution with explicit expressions for the posterior expectation and covariance; hence, exact prediction intervals for the inverted parameters can be computed under the specified model. The explicit analytical form of the posterior distribution provides a computationally fast inversion method. Tests on synthetic data show that all inverted parameters were almost perfectly retrieved when the noise approached zero. With realistic noise levels, acoustic impedance was the best determined parameter, while the inversion provided practically no information about the density. The inversion algorithm has also been tested on a real 3‐D data set from the Sleipner field. The results show good agreement with well logs, but the uncertainty is high.

scholarly journals Experimental Study on Petrophysical Properties as a Tool to Identify Pore Fluids in Tight-Rock Reservoirs

2021 ◽  
Vol 9 ◽  
Author(s):  
Rupeng Ma ◽  
Jing Ba ◽  
José Carcione ◽  
Maxim Lebedev ◽  
Changsheng Wang
Keyword(s):  
Wave Velocity ◽  
Poisson’S Ratio ◽  
Velocity Ratio ◽  
Data Distribution ◽  
Poisson's Ratio ◽  
Petrophysical Properties ◽  
Pore Fluids ◽  
S Wave ◽  
Wave Velocities ◽  
Lame Constant

The petrophysical properties can be proper indicators to identify oil and gas reservoirs, since the pore fluids have significant effects on the wave response. We have performed ultrasonic measurements on two sets of tight siltstones and dolomites at partial saturation. P- and S-wave velocities are obtained by the pulse transmission technique, while attenuation is calculated using the centroid-frequency shift and spectral-ratio methods. The fluid sensitivities of different properties (i.e., P- and S-wave velocities, impedances and attenuation, Poisson's ratio, density, and their combinations) are quantitatively analyzed by considering the data distribution, based on the crossplot technique. The result shows that the properties (P- to S-wave velocity and attenuation ratios, Poisson's ratio, and first to second Lamé constant ratio) with high fluid-sensitivity indicators successfully distinguish gas from oil and water, unlike oil from water. Moreover, siltstones and dolomites can be identified on the basis of data distribution areas. Ultrasonic rock-physics templates of the P- to S-wave velocity ratio vs. the product of first Lamé constant with density obtained with a poroelastic model, considering the structural heterogeneity and patchy saturation, are used to predict the saturation and porosity, which are in good agreement with the experimental data at different porosity ranges.

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.

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