Time-lapse difference inversion based on the modified reflectivity method with differentiable Hyper-Laplacian blocky constraint

Geophysics ◽  
2021 ◽  
pp. 1-121
Author(s):  
Wei Tang ◽  
Jingye Li ◽  
Wenbiao Zhang ◽  
Jian Zhang ◽  
Weiheng Geng ◽  
...  
Keyword(s):  
Seismic Data ◽  
Time Lapse ◽  
Inversion Method ◽  
Transmission Losses ◽  
Bayesian Theorem ◽  
Reflectivity Method ◽  
Fluid Properties ◽  
Internal Multiples ◽  
Fine Tune

Time-lapse (TL) seismic has great potential in monitoring and interpreting time-varying variations in reservoir fluid properties during hydrocarbon exploitation. Obtaining the disparities of reservoir elastic parameters by inversion is essential for TL reservoir monitoring. Conventional TL inversion is carried out by stages without coupling processing and leads to inaccuracy of the results. We directly use the differences in seismic data responses from different vintages, namely difference inversion, to improve the results credibility. It may reduce the deviations of the subtraction of base and monitor inversions in traditional methods. Moreover, most existing studies involving pre-stack inversion methods use the Zoeppritz equation or its approximants, which failed to consider the wave propagation effects. Here, we propose a new TL difference inversion based on the modified reflectivity method (MRM), the internal multiples and transmission losses are taken into consideration to fine-tune the characterization of the seismic wave propagating underground. The new method is modified on the basis of reflectivity method (RM) making it feasible in TL difference inversion, and derived from the Bayesian theorem. For further delineating the boundaries between layers, the differentiable Hyper-Laplacian blocky constraint (DHLBC) is introduced into the prior information of Bayesian framework, which heightens the sparseness in the vertical gradients of inversion results and leads to sharp interlayer boundaries of difference parameters. The synthetic and field data examples demonstrate that the proposed TL difference inversion method has clear advantages in accuracy and resolution compared to Zoeppritz method and MRM without DHLBC.

Amplitude variation with offset inversion using the reflectivity method

Geophysics ◽  
2016 ◽  
Vol 81 (4) ◽  
pp. R185-R195 ◽  
Author(s):  
Hongxing Liu ◽  
Jingye Li ◽  
Xiaohong Chen ◽  
Bo Hou ◽  
Li Chen
Keyword(s):  
Wave Velocity ◽  
P Wave ◽  
Transmission Losses ◽  
Amplitude Variation ◽  
Data Set ◽  
Amplitude Variation With Offset ◽  
Avo Inversion ◽  
Propagation Effects ◽  
Reflectivity Method ◽  
Internal Multiples

Most existing amplitude variation with offset (AVO) inversion methods are based on the Zoeppritz’s equation or its approximations. These methods assume that the amplitude of seismic data depends only on the reflection coefficients, which means that the wave-propagation effects, such as geometric spreading, attenuation, transmission loss, and multiples, have been fully corrected or attenuated before inversion. However, these requirements are very strict and can hardly be satisfied. Under a 1D assumption, reflectivity-method-based inversions are able to handle transmission losses and internal multiples. Applications of these inversions, however, are still time-consuming and complex in computation of differential seismograms. We have evaluated an inversion methodology based on the vectorized reflectivity method, in which the differential seismograms can be calculated from analytical expressions. It is computationally efficient. A modification is implemented to transform the inversion from the intercept time and ray-parameter domain to the angle-gather domain. AVO inversion is always an ill-posed problem. Following a Bayesian approach, the inversion is stabilized by including the correlation of the P-wave velocity, S-wave velocity, and density. Comparing reflectivity-method-based inversion with Zoeppritz-based inversion on a synthetic data and a real data set, we have concluded that reflectivity-method-based inversion is more accurate when the propagation effects of transmission losses and internal multiples are not corrected. Model testing has revealed that the method is robust at high noise levels.

Time-lapse acoustic impedance variations during CO2 injection in Weyburn oilfield, Canada

Geophysics ◽  
2019 ◽  
Vol 85 (1) ◽  
pp. M1-M13 ◽  
Author(s):  
Yichuan Wang ◽  
Igor B. Morozov
Keyword(s):  
Oil Recovery ◽  
Seismic Data ◽  
Acoustic Impedance ◽  
Time Lapse ◽  
Time Shift ◽  
Co2 Injection ◽  
Inversion Method ◽  
Data Sets ◽  
Storage Site ◽  
Data Set

For seismic monitoring injected fluids during enhanced oil recovery or geologic [Formula: see text] sequestration, it is useful to measure time-lapse (TL) variations of acoustic impedance (AI). AI gives direct connections to the mechanical and fluid-related properties of the reservoir or [Formula: see text] storage site; however, evaluation of its subtle TL variations is complicated by the low-frequency and scaling uncertainties of this attribute. We have developed three enhancements of TL AI analysis to resolve these issues. First, following waveform calibration (cross-equalization) of the monitor seismic data sets to the baseline one, the reflectivity difference was evaluated from the attributes measured during the calibration. Second, a robust approach to AI inversion was applied to the baseline data set, based on calibration of the records by using the well-log data and spatially variant stacking and interval velocities derived during seismic data processing. This inversion method is straightforward and does not require subjective selections of parameterization and regularization schemes. Unlike joint or statistical inverse approaches, this method does not require prior models and produces accurate fitting of the observed reflectivity. Third, the TL AI difference is obtained directly from the baseline AI and reflectivity difference but without the uncertainty-prone subtraction of AI volumes from different seismic vintages. The above approaches are applied to TL data sets from the Weyburn [Formula: see text] sequestration project in southern Saskatchewan, Canada. High-quality baseline and TL AI-difference volumes are obtained. TL variations within the reservoir zone are observed in the calibration time-shift, reflectivity-difference, and AI-difference images, which are interpreted as being related to the [Formula: see text] injection.

AVO attribute inversion for finely layered reservoirs

Geophysics ◽  
2006 ◽  
Vol 71 (3) ◽  
pp. C25-C36 ◽  
Author(s):  
Alexey Stovas ◽  
Martin Landrø ◽  
Per Avseth
Keyword(s):  
Seismic Data ◽  
Rock Physics ◽  
Time Lapse ◽  
Oil Saturation ◽  
Seismic Properties ◽  
Seismic Input ◽  
Fluid Properties ◽  
Random Variability ◽  
Net To Gross ◽  
Avo Attributes

Assuming that a turbidite reservoir can be approximated by a stack of thin shale-sand layers, we use standard amplitude variaiton with offset (AVO) attributes to estimate net-to-gross (N/G) and oil saturation. Necessary input is Gassmann rock-physics properties for sand and shale, as well as the fluid properties for hydrocarbons. Required seismic input is AVO intercept and gradient. The method is based upon thin-layer reflectivity modeling. It is shown that random variability in thickness and seismic properties of the thin sand and shale layers does not change significantly the AVO attributes at the top and base of the turbidite-reservoir sequence. The method is tested on seismic data from offshore Brazil. The results show reasonable agreement between estimated and observed N/G and oil saturation. The methodology can be developed further for estimating changes in pay thickness from time-lapse seismic data.

Bayesian linearized petrophysical AVO inversion

Geophysics ◽  
2018 ◽  
Vol 83 (3) ◽  
pp. M1-M13 ◽  
Author(s):  
Xiaozheng Lang ◽  
Dario Grana
Keyword(s):  
Seismic Data ◽  
Elastic Moduli ◽  
Water Saturation ◽  
Forward Model ◽  
Inversion Method ◽  
Data Set ◽  
Critical Porosity ◽  
Explicit Analytical Solution ◽  
Fluid Properties ◽  
Model Linearization

Seismic reservoir characterization aims to provide a 3D model of rock and fluid properties based on measured seismic data. Petrophysical properties, such as porosity, mineral volumes, and water saturation, are related to elastic properties, such as velocity and impedance, through a rock-physics model. Elastic attributes can be obtained from seismic data through seismic modeling. Estimation of the properties of interest is an inverse problem; however, if the forward model is nonlinear, computationally demanding inversion algorithms should be adopted. We have developed a linearized forward model, based on a convolutional model and a new amplitude variation with offset approximation that combined Gray’s linearization of the reflectivity coefficients with Gassmann’s equation and Nur’s critical porosity model. Physical relations between the saturated elastic moduli and the matrix elastic moduli, fluid bulk modulus, and porosity are almost linear, and the model linearization can be obtained by computing the first-order Taylor series approximation. The inversion method for the estimation of the reservoir properties of interest is then developed in the Bayesian framework. If we assume that the distributions of the prior model and error term are Gaussian, then the explicit analytical solution of the posterior distribution of rock and fluid properties can be analytically derived. Our method has first been validated on synthetic seismic data and then applied to a 2D seismic section extracted from a real data set acquired in the Norwegian Sea.

Integrated simulation to seismic and seismic reservoir characterization in a CO2 EOR monitoring application

2020 ◽  
Vol 39 (9) ◽  
pp. 668-678
Author(s):  
Alan Mur ◽  
César Barajas-Olalde ◽  
Donald C. Adams ◽  
Lu Jin ◽  
Jun He ◽  
...  
Keyword(s):  
Oil Recovery ◽  
Reservoir Simulation ◽  
Seismic Data ◽  
Reservoir Characterization ◽  
Rock Physics ◽  
Time Lapse ◽  
Seismic Signal ◽  
Inversion Method ◽  
Data Sets ◽  
Production Effects

Understanding the behavior of CO2 injected into a reservoir and delineating its spatial distribution are fundamentally important in enhanced oil recovery (EOR) and CO2 capture and sequestration activities. Interdisciplinary geoscience collaboration and well-defined workflows, from data acquisition to reservoir simulation, are needed to effectively handle the challenges of EOR fields and envisioned future commercial-scale sites for planned and incidental geologic CO2 storage. Success of operations depends on decisions that are based on good understanding of geologic formation heterogeneities and fluid and pressure movements in the reservoir over large areas over time. We present a series of workflow steps that optimize the use of available data to improve and integrate the interpretation of facies, injection, and production effects in an EOR application. First, we construct a simulation-to-seismic model supported by rock physics to model the seismic signal and signal quality needed for 4D monitoring of fluid and pressure changes. Then we use Bayesian techniques to invert the baseline and monitor seismic data sets for facies and impedances. To achieve a balance between prior understanding of the reservoir and the recorded time-lapse seismic data, we invert the seismic data sets by using multiple approaches. We first invert the seismic data sets independently, exploring sensible parameter scenarios. With the resulting realizations, we develop a shared prior model to link the reservoir facies geometry between seismic vintages upon inversion. Then we utilize multirealization analysis methods to quantify the uncertainties of our predictions. Next, we show how data may be more deeply interrogated by using the facies inversion method to invert prestack seismic differences directly for production effects. Finally, we show and discuss the feedback loop for updating the static and dynamic reservoir simulation model to highlight the integration of geophysical and engineering data within a single model.

Characterization of gas hydrate systems on the Hikurangi margin (New Zealand) through geostatistical seismic and petrophysical inversion

Geophysics ◽  
2021 ◽  
pp. 1-60
Author(s):  
Francesco Turco ◽  
Leonardo Azevedo ◽  
Dario Grana ◽  
Gareth J. Crutchley ◽  
Andrew R. Gorman
Keyword(s):  
New Zealand ◽  
Gas Hydrate ◽  
Seismic Data ◽  
Effective Means ◽  
Rock Physics ◽  
Seismic Inversion ◽  
Inversion Method ◽  
Seismic Reflection Data ◽  
Reflection Data

Quantitative characterization of gas hydrate systems on continental margins from seismic data is challenging, especially in regions where no well logs are available. However, probabilistical seismic inversion provides an effective means for constraining the physical properties of subsurface strata in such settings and analyzing the variability related to the results. We apply a workflow for the characterization of two deep-water gas hydrate reservoirs east of New Zealand, where high concentrations of gas hydrate have been inferred previously. We estimate porosity and gas hydrate saturation in the reservoirs from multi-channel seismic data through a two-step procedure based on geostatistical seismic and Bayesian petrophysical inversion built on a rock physics model for gas hydrate-bearing marine sediments. We found that the two reservoirs together host between 2.45 × 105 m3 and 1.72 × 106 m3 of gas hydrate, with the best estimate at 9.68 × 105 m3. This estimate provides a first-order assessment for further gas hydrate evaluations in the region. The two-step statistically based seismic inversion method is an effective approach for characterizing gas hydrate systems from long-offset seismic reflection data.

Elastic inverse scattering for fluid variation with time-lapse seismic data

Geophysics ◽  
2015 ◽  
Vol 80 (2) ◽  
pp. WA61-WA67 ◽  
Author(s):  
Zhaoyun Zong ◽  
Xingyao Yin ◽  
Guochen Wu ◽  
Zhiping Wu
Keyword(s):  
Shear Modulus ◽  
Inverse Scattering ◽  
Seismic Data ◽  
Scattering Theory ◽  
Time Lapse ◽  
Monitoring Data ◽  
Prestack Inversion ◽  
Fluid Factor ◽  
Reference Medium ◽  
The Difference

Elastic inverse-scattering theory has been extended for fluid discrimination using the time-lapse seismic data. The fluid factor, shear modulus, and density are used to parameterize the reference medium and the monitoring medium, and the fluid factor works as the hydrocarbon indicator. The baseline medium is, in the conception of elastic scattering theory, the reference medium, and the monitoring medium is corresponding to the perturbed medium. The difference in the earth properties between the monitoring medium and the baseline medium is taken as the variation in the properties between the reference medium and perturbed medium. The baseline and monitoring data correspond to the background wavefields and measured full fields, respectively. And the variation between the baseline data and monitoring data is taken as the scattered wavefields. Under the above hypothesis, we derived a linearized and qualitative approximation of the reflectivity variation in terms of the changes of fluid factor, shear modulus, and density with the perturbation theory. Incorporating the effect of the wavelet into the reflectivity approximation as the forward solver, we determined a practical prestack inversion approach in a Bayesian scheme to estimate the fluid factor, shear modulus, and density changes directly with the time-lapse seismic data. We evaluated the examples revealing that the proposed approach rendered the estimation of the fluid factor, shear modulus, and density changes stably, even with moderate noise.

Stochastic inversion of pressure and saturation changes from time-lapse AVO data

Geophysics ◽  
2006 ◽  
Vol 71 (5) ◽  
pp. C81-C92 ◽  
Author(s):  
Helene Hafslund Veire ◽  
Hilde Grude Borgos ◽  
Martin Landrø
Keyword(s):  
Stochastic Model ◽  
Seismic Data ◽  
Synthetic Data ◽  
Time Lapse ◽  
Bayesian Framework ◽  
Reservoir Modeling ◽  
Data Set ◽  
The North ◽  
Fluid Saturation ◽  
Likelihood Model

Effects of pressure and fluid saturation can have the same degree of impact on seismic amplitudes and differential traveltimes in the reservoir interval; thus, they are often inseparable by analysis of a single stacked seismic data set. In such cases, time-lapse AVO analysis offers an opportunity to discriminate between the two effects. We quantify the uncertainty in estimations to utilize information about pressure- and saturation-related changes in reservoir modeling and simulation. One way of analyzing uncertainties is to formulate the problem in a Bayesian framework. Here, the solution of the problem will be represented by a probability density function (PDF), providing estimations of uncertainties as well as direct estimations of the properties. A stochastic model for estimation of pressure and saturation changes from time-lapse seismic AVO data is investigated within a Bayesian framework. Well-known rock physical relationships are used to set up a prior stochastic model. PP reflection coefficient differences are used to establish a likelihood model for linking reservoir variables and time-lapse seismic data. The methodology incorporates correlation between different variables of the model as well as spatial dependencies for each of the variables. In addition, information about possible bottlenecks causing large uncertainties in the estimations can be identified through sensitivity analysis of the system. The method has been tested on 1D synthetic data and on field time-lapse seismic AVO data from the Gullfaks Field in the North Sea.

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