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.

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.

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.

Q-compensated acoustic impedance inversion of attenuated seismic data: Numerical and field-data experiments

Geophysics ◽  
2018 ◽  
Vol 83 (6) ◽  
pp. R553-R567 ◽  
Author(s):  
Xintao Chai ◽  
Genyang Tang ◽  
Fangfang Wang ◽  
Hanming Gu ◽  
Xinqiang Wang
Keyword(s):  
Seismic Data ◽  
Acoustic Impedance ◽  
Field Data ◽  
Reservoir Characterization ◽  
Rock Properties ◽  
Inversion Method ◽  
Data Sets ◽  
Anelastic Attenuation ◽  
Text Filtering ◽  
Frequency Components

Acoustic impedance (AI) inversion is of great interest because it extracts information regarding rock properties from seismic data and has successful applications in reservoir characterization. During wave propagation, anelastic attenuation and dispersion always occur because the subsurface is not perfectly elastic, thereby diminishing the seismic resolution. AI inversion based on the convolutional model requires that the input data be free of attenuation effects; otherwise, low-resolution results are inevitable. The intrinsic instability that occurs while compensating for the anelastic effects via inverse [Formula: see text] filtering is notorious. The gain-limit inverse [Formula: see text] filtering method cannot compensate for strongly attenuated high-frequency components. A nonstationary sparse reflectivity inversion (NSRI) method can estimate the reflectivity series from attenuated seismic data without the instability issue. Although AI is obtainable from an inverted reflectivity series through recursion, small inaccuracies in the reflectivity series can result in large perturbations in the AI result because of the cumulative effects. To address these issues, we have developed a [Formula: see text]-compensated AI inversion method that directly retrieves high-resolution AI from attenuated seismic data without prior inverse [Formula: see text] filtering based on the theory of NSRI and AI inversion. This approach circumvents the intrinsic instability of inverse [Formula: see text] filtering by integrating the [Formula: see text] filtering operator into the convolutional model and solving the inverse problem iteratively. This approach also avoids the ill-conditioned nature of the recursion scheme for transforming an inverted reflectivity series to AI. Experiments on a benchmark Marmousi2 model validate the feasibility and capabilities of our method. Applications to two field data sets verify that the inversion results generated by our approach are mostly consistent with the well logs.

Stochastic nonlinear inversion of seismic data for the estimation of petroelastic properties using the ensemble smoother and data reparameterization

Geophysics ◽  
2018 ◽  
Vol 83 (3) ◽  
pp. M25-M39 ◽  
Author(s):  
Mingliang Liu ◽  
Dario Grana
Keyword(s):  
Seismic Data ◽  
Reservoir Characterization ◽  
Computational Cost ◽  
Rock Physics ◽  
Inversion Method ◽  
Nonlinear Inversion ◽  
Seismic Velocities ◽  
Reservoir Properties ◽  
Data Set ◽  
Ensemble Smoother

We have developed a new stochastic nonlinear inversion method for seismic reservoir characterization studies to jointly estimate elastic and petrophysical properties and to quantify their uncertainty. Our method aims to estimate multiple reservoir realizations of the entire set of reservoir properties, including seismic velocities, density, porosity, mineralogy, and saturation, by iteratively updating the initial ensemble of models based on the mismatch between their seismic response and the measured seismic data. The initial models are generated using geostatistical methods and the geophysical forward operators include rock-physics relations and a seismic forward model. The optimization is achieved using an iterative ensemble-based algorithm, namely, the ensemble smoother with multiple data assimilation, in which each iteration is based on a Bayesian updating step. The advantages of the proposed method are that it can be applied to nonlinear inverse problems and it can provide an ensemble of solutions from which we can quantify the uncertainty of the model properties of interest. To reduce the computational cost of the inversion, we perform the optimization in a lower dimensional data space reparameterized by singular value decomposition. The proposed methodology is validated on a synthetic case in which the set of petroelastic properties is recovered with satisfactory accuracy. Then, we applied the inversion method to a real seismic data set from the Norne field in the Norwegian Sea.

Time-lapse joint inversion of crosswell DC resistivity and seismic data: A numerical investigation

Geophysics ◽  
2012 ◽  
Vol 77 (4) ◽  
pp. D141-D157 ◽  
Author(s):  
M. Karaoulis ◽  
A. Revil ◽  
J. Zhang ◽  
D. D. Werkema
Keyword(s):  
Oil Recovery ◽  
Seismic Data ◽  
Seismic Velocity ◽  
Joint Inversion ◽  
Time Lapse ◽  
Dc Resistivity ◽  
Data Sets ◽  
Time Step ◽  
First Case ◽  
Over Time

Time-lapse joint inversion of geophysical data is required to image the evolution of oil reservoirs during production and enhanced oil recovery, [Formula: see text] sequestration, geothermal fields during production, and to monitor the evolution of contaminant plumes. Joint inversion schemes reduce space-related artifacts in filtering out noise that is spatially uncorrelated, and time-lapse inversion algorithms reduce time-related artifacts in filtering out noise that is uncorrelated over time. There are several approaches that are possible to perform the joint inverse problem. In this work, we investigate the structural crossgradient (SCG) joint inversion approach and the crosspetrophysical (CP) approach, which are appropriate for time-lapse problems. In the first case, the inversion scheme looks for models with structural similarities. In the second case, we use a direct relationship between the geophysical parameters. Time-lapse inversion is performed with an actively time-constrained (ATC) approach. In this approach, the subsurface is defined as a space-time model. All the snapshots are inverted together assuming a regularization of the sequence of snapshots over time. First, we showed the advantage of combining the SCG or CP inversion approaches and the ATC inversion by using a synthetic problem corresponding to crosshole seismic and DC-resistivity data and piecewise constant resistivity and seismic velocity distributions. We also showed that the combined SCG/ATC approach reduces the presence of artifacts with respect to individual inversion of the resistivity and seismic data sets, as well as with respect to the joint inversion of both data sets at each time step. We also performed a synthetic study using a secondary oil recovery problem. The combined CP/ATC approach was successful in retrieving the position of the oil/water encroachment front.

scholarly journals Time-Lapse Seismic Monitoring of Onshore Reservoirs in Niger Delta, Field ‘K’ as a Case Study

2017 ◽  
Vol 1 (1) ◽  
pp. 23-27 ◽  
Author(s):  
A. Ogbamikhumi ◽  
T. Tralagba ◽  
E. E. Osagiede
Keyword(s):  
Seismic Data ◽  
Acoustic Impedance ◽  
Niger Delta ◽  
Rock Physics ◽  
Time Lapse ◽  
Seismic Survey ◽  
Impedance Change ◽  
Data Set ◽  
Reservoir Fluid ◽  
4D Seismic

Field ‘K’ is a mature field in the coastal swamp onshore Niger delta, which has been producing since 1960. As a huge producing field with some potential for further sustainable production, field monitoring is therefore important in the identification of areas of unproduced hydrocarbon. This can be achieved by comparing production data with the corresponding changes in acoustic impedance observed in the maps generated from base survey (initial 3D seismic) and monitor seismic survey (4D seismic) across the field. This will enable the 4D seismic data set to be used for mapping reservoir details such as advancing water front and un-swept zones. The availability of good quality onshore time-lapse seismic data for Field ‘K’ acquired in 1987 and 2002 provided the opportunity to evaluate the effect of changes in reservoir fluid saturations on time-lapse amplitudes. Rock physics modelling and fluid substitution studies on well logs were carried out, and acoustic impedance change in the reservoir was estimated to be in the range of 0.25% to about 8%. Changes in reservoir fluid saturations were confirmed with time-lapse amplitudes within the crest area of the reservoir structure where reservoir porosity is 0.25%. In this paper, we demonstrated the use of repeat Seismic to delineate swept zones and areas hit with water override in a producing onshore reservoir.

Case study: Seismic resolution and reservoir characterization of thin sands using multiattribute analysis and bandwidth extension in the Daqing field, China

2020 ◽  
Vol 8 (1) ◽  
pp. T89-T102
Author(s):  
David Mora ◽  
John Castagna ◽  
Ramses Meza ◽  
Shumin Chen ◽  
Renqi Jiang
Keyword(s):  
Seismic Data ◽  
Reservoir Characterization ◽  
Statistical Significance ◽  
Physical Property ◽  
Seismic Attributes ◽  
Oil Field ◽  
Thin Layers ◽  
Inversion Method ◽  
Bandwidth Extension ◽  
Multiattribute Analysis

The Daqing field, located in the Songliao Basin in northeastern China, is the largest oil field in China. Most production in the Daqing field comes from seismically thin sand bodies with thicknesses between 1 and 15 m. Thus, it is not usually possible to resolve Daqing reservoirs using only conventional seismic data. We have evaluated the effectiveness of seismic multiattribute analysis of bandwidth extended data in resolving and making inferences about these thin layers. Multiattribute analysis uses statistical methods or neural networks to find relationships between well data and seismic attributes to predict some physical property of the earth. This multiattribute analysis was applied separately to conventional seismic data and seismic data that were spectrally broadened using sparse-layer inversion because this inversion method usually increases the vertical resolution of the seismic. Porosity volumes were generated using target porosity logs and conventional seismic attributes, and isofrequency volumes were obtained by spectral decomposition. The resulting resolution, statistical significance, and accuracy in the determination of layer properties were higher for the predictions made using the spectrally broadened volume.

A time-lapse seismic repeatability test using the P-Cable high-resolution 3D marine acquisition system

2020 ◽  
Vol 39 (7) ◽  
pp. 480-487
Author(s):  
Patrick Smith ◽  
Brandon Mattox
Keyword(s):  
High Resolution ◽  
Seismic Data ◽  
Low Cost ◽  
Low Frequency ◽  
Time Lapse ◽  
Seismic Signal ◽  
Residual Energy ◽  
Acquisition System ◽  
Positioning Errors ◽  
Seismic Surveying

The P-Cable high-resolution 3D marine acquisition system tows many short, closely separated streamers behind a small source. It can provide 3D seismic data of very high temporal and spatial resolution. Since the system is containerized and has small dimensions, it can be deployed at short notice and relatively low cost, making it attractive for time-lapse seismic reservoir monitoring. During acquisition of a 3D high-resolution survey in the Gulf of Mexico in 2014, a pair of sail lines were repeated to form a time-lapse seismic test. We processed these in 2019 to evaluate their geometric and seismic repeatability. Geometric repetition accuracy was excellent, with source repositioning errors below 10 m and bin-based receiver positioning errors below 6.25 m. Seismic data comparisons showed normalized root-mean-square difference values below 10% between 40 and 150 Hz. Refinements to the acquisition system since 2014 are expected to further improve repeatability of the low-frequency components. Residual energy on 4D difference seismic data was low, and timing stability was good. We conclude that the acquisition system is well suited to time-lapse seismic surveying in areas where the reservoir and time-lapse seismic signal can be adequately imaged by small-source, short-offset, low-fold data.

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