Joint target‐oriented wave‐equation inversion of multiple time‐lapse seismic data sets

2009 ◽  
Author(s):  
Gboyega Ayeni ◽  
Biondo Biondi
Keyword(s):  
Wave Equation ◽  
Seismic Data ◽  
Time Lapse ◽  
Data Sets ◽  
Multiple Time

Wave‐equation inversion of time‐lapse seismic data sets

2011 ◽  
Author(s):  
Gboyega Ayeni ◽  
Biondo Biondi
Keyword(s):  
Wave Equation ◽  
Seismic Data ◽  
Time Lapse ◽  
Data Sets

Common-focus point-based target-oriented imaging approach for continuous seismic reservoir monitoring

Geophysics ◽  
2018 ◽  
Vol 83 (4) ◽  
pp. M41-M48 ◽  
Author(s):  
Hongwei Liu ◽  
Mustafa Naser Al-Ali
Keyword(s):  
Seismic Data ◽  
Rock Physics ◽  
Time Lapse ◽  
Velocity Estimation ◽  
Estimation Methods ◽  
Data Sets ◽  
Data Set ◽  
Velocity Models ◽  
Reservoir Monitoring ◽  
Focus Point

The ideal approach for continuous reservoir monitoring allows generation of fast and accurate images to cope with the massive data sets acquired for such a task. Conventionally, rigorous depth-oriented velocity-estimation methods are performed to produce sufficiently accurate velocity models. Unlike the traditional way, the target-oriented imaging technology based on the common-focus point (CFP) theory can be an alternative for continuous reservoir monitoring. The solution is based on a robust data-driven iterative operator updating strategy without deriving a detailed velocity model. The same focusing operator is applied on successive 3D seismic data sets for the first time to generate efficient and accurate 4D target-oriented seismic stacked images from time-lapse field seismic data sets acquired in a [Formula: see text] injection project in Saudi Arabia. Using the focusing operator, target-oriented prestack angle domain common-image gathers (ADCIGs) could be derived to perform amplitude-versus-angle analysis. To preserve the amplitude information in the ADCIGs, an amplitude-balancing factor is applied by embedding a synthetic data set using the real acquisition geometry to remove the geometry imprint artifact. Applying the CFP-based target-oriented imaging to time-lapse data sets revealed changes at the reservoir level in the poststack and prestack time-lapse signals, which is consistent with the [Formula: see text] injection history and rock physics.

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.

scholarly journals Time-lapse difference static correction using prestack crosscorrelations: 4D seismic image enhancement case from Ketzin

Geophysics ◽  
2014 ◽  
Vol 79 (6) ◽  
pp. B243-B252 ◽  
Author(s):  
Peter Bergmann ◽  
Artem Kashubin ◽  
Monika Ivandic ◽  
Stefan Lüth ◽  
Christopher Juhlin
Keyword(s):  
Seismic Data ◽  
Time Lapse ◽  
Data Sets ◽  
Storage Site ◽  
Data Set ◽  
Near Surface ◽  
Static Correction ◽  
Seismic Image ◽  
Static Corrections ◽  
4D Seismic

A method for static correction of time-lapse differences in reflection arrival times of time-lapse prestack seismic data is presented. These arrival-time differences are typically caused by changes in the near-surface velocities between the acquisitions and had a detrimental impact on time-lapse seismic imaging. Trace-to-trace time shifts of the data sets from different vintages are determined by crosscorrelations. The time shifts are decomposed in a surface-consistent manner, which yields static corrections that tie the repeat data to the baseline data. Hence, this approach implies that new refraction static corrections for the repeat data sets are unnecessary. The approach is demonstrated on a 4D seismic data set from the Ketzin [Formula: see text] pilot storage site, Germany, and is compared with the result of an initial processing that was based on separate refraction static corrections. It is shown that the time-lapse difference static correction approach reduces 4D noise more effectively than separate refraction static corrections and is significantly less labor intensive.

Subspace Ensemble Randomized Maximum Likelihood with Local Analysis for Time-Lapse-Seismic-Data Assimilation

SPE Journal ◽  
2021 ◽  
Vol 26 (02) ◽  
pp. 1011-1031
Author(s):  
Gilson Moura Silva Neto ◽  
Ricardo Vasconcellos Soares ◽  
Geir Evensen ◽  
Alessandra Davolio ◽  
Denis José Schiozer
Keyword(s):  
Maximum Likelihood ◽  
Data Assimilation ◽  
Seismic Data ◽  
Large Data ◽  
Time Lapse ◽  
Large Data Sets ◽  
Local Analysis ◽  
Data Sets ◽  
Reservoir Models ◽  
Randomized Maximum Likelihood

Summary Time-lapse-seismic-data assimilation has been drawing the reservoir-engineering community's attention over the past few years. One of the advantages of including this kind of data to improve the reservoir-flow models is that it provides complementary information compared with the wells' production data. Ensemble-based methods are some of the standard tools used to calibrate reservoir models using time-lapse seismic data. One of the drawbacks of assimilating time-lapse seismic data involves the large data sets, mainly for large reservoir models. This situation leads to high-dimensional problems that demand significant computational resources to process and store the matrices when using conventional and straightforward methods. Another known issue associated with the ensemble-based methods is the limited ensemble sizes, which cause spurious correlations between the data and the parameters and limit the degrees of freedom. In this work, we propose a data-assimilation scheme using an efficient implementation of the subspace ensemble randomized maximum likelihood (SEnRML) method with local analysis. This method reduces the computational requirements for assimilating large data sets because the number of operations scales linearly with the number of observed data points. Furthermore, by implementing it with local analysis, we reduce the memory requirements at each update step and mitigate the effects of the limited ensemble sizes. We test two local analysis approaches: one distance-based approach and one correlation-based approach. We apply these implementations to two synthetic time-lapse-seismic-data-assimilation cases, one 2D example, and one field-scale application that mimics some of the real-field challenges. We compare the results with reference solutions and with the known ensemble smoother with multiple data assimilation (ES-MDA) using Kalman gain distance-based localization. The results show that our method can efficiently assimilate time-lapse seismic data, leading to updated models that are comparable with other straightforward methods. The correlation-based local analysis approach provided results similar to the distance-based approach, with the advantage that the former can be applied to data and parameters that do not have specific spatial positions.

Discrimination between pressure and fluid saturation changes from marine multicomponent time‐lapse seismic data

Geophysics ◽  
2003 ◽  
Vol 68 (5) ◽  
pp. 1592-1599 ◽  
Author(s):  
Martin Landrø ◽  
Helene Hafslund Veire ◽  
Kenneth Duffaut ◽  
Nazih Najjar
Keyword(s):  
Pore Pressure ◽  
Seismic Data ◽  
Water Saturation ◽  
Synthetic Data ◽  
Time Lapse ◽  
Seismic Survey ◽  
Data Sets ◽  
Amplitude Variation With Offset ◽  
Fluid Saturation ◽  
Pressure Changes

Explicit expressions for computation of saturation and pressure‐related changes from marine multicomponent time‐lapse seismic data are presented. Necessary input is PP and PS stacked data for the baseline seismic survey and the repeat survey. Compared to earlier methods based on PP data only, this method is expected to be more robust since two independent measurements are used in the computation. Due to a lack of real marine multicomponent time‐lapse seismic data sets, the methodology is tested on synthetic data sets, illustrating strengths and weaknesses of the proposed technique. Testing ten scenarios for various changes in pore pressure and fluid saturation, we find that it is more robust for most cases to use the proposed 4D PP/PS technique instead of a 4D PP amplitude variation with offset (AVO) technique. The fit between estimated and “real” changes in water saturation and pore pressure were good for most cases. On the average, we find that the deviation in estimated saturation changes is 8% and 0.3 MPa for the estimated pore pressure changes. For PP AVO, we find that the corresponding average errors are 9% and 1.0 MPa. In the present method, only 4D PP and PS amplitude changes are used in the calculations. It is straightforward to include use of 4D traveltime shifts in the algorithm and, if reliable time shifts can be measured, this will most likely further stabilize the presented method.

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