Synthetic versus real time‐lapse seismic data at the Sleipner CO2 injection site

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.

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Time-lapse processing of 2D seismic profiles with testing of static correction methods at the CO2 injection site Ketzin (Germany)

Journal of Applied Geophysics ◽

10.1016/j.jappgeo.2011.05.005 ◽

2011 ◽

Vol 75 (1) ◽

pp. 124-139 ◽

Cited By ~ 26

Author(s):

Peter Bergmann ◽

Can Yang ◽

Stefan Lüth ◽

Christopher Juhlin ◽

Calin Cosma

Keyword(s):

Injection Site ◽

Time Lapse ◽

Co2 Injection ◽

Seismic Profiles ◽

Static Correction

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Evaluation of a resistivity model derived from time-lapse well logging of a pilot-scale CO2 injection site, Nagaoka, Japan

International Journal of Greenhouse Gas Control ◽

10.1016/j.ijggc.2012.11.002 ◽

2013 ◽

Vol 12 ◽

pp. 288-299 ◽

Cited By ~ 11

Author(s):

Takahiro Nakajima ◽

Ziqiu Xue

Keyword(s):

Injection Site ◽

Time Lapse ◽

Well Logging ◽

Pilot Scale ◽

Co2 Injection ◽

Resistivity Model

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Rock-physics-based double-difference inversion for CO2 saturation and porosity at the Cranfield CO2 injection site

Interpretation ◽

10.1190/int-2014-0123.1 ◽

2015 ◽

Vol 3 (2) ◽

pp. SM23-SM35

Author(s):

Russell W. Carter ◽

Kyle T. Spikes

Keyword(s):

Seismic Data ◽

Rock Physics ◽

Time Lapse ◽

Reservoir Rock ◽

Well Logs ◽

Double Difference ◽

Co2 Injection ◽

Text Data ◽

Physics Model ◽

Rock Physics Model

Large-scale subsurface injection of [Formula: see text] has the potential to reduce emissions of atmospheric [Formula: see text] and improve oil recovery. Studying the effects of injected [Formula: see text] on the elastic properties of the saturated reservoir rock can help to improve long-term monitoring effectiveness and accuracy at locations undergoing [Formula: see text] injection. We used two vintages of existing 3D surface seismic data and well logs to probabilistically invert for the [Formula: see text] saturation and porosity at the Cranfield reservoir using a double-difference approach. The first step of this work was to calibrate the rock-physics model to the well-log data. Next, the baseline and time-lapse seismic data sets were inverted for acoustic impedance [Formula: see text] using a high-resolution basis pursuit inversion technique. The reservoir porosity was derived statistically from the rock-physics model based on the [Formula: see text] estimates inverted from the baseline survey. The porosity estimates were used in the double-difference routine as the fixed initial model from which [Formula: see text] saturation was then estimated from the time-lapse [Formula: see text] data. Porosity was assumed to remain constant between survey vintages; therefore, the changes between the baseline and time-lapse [Formula: see text] data may be inverted for [Formula: see text] saturation from the injection activities using the calibrated rock-physics model. Comparisons of inverted and measured porosity from well logs indicated quite accurate results. Estimates of [Formula: see text] saturation found less accuracy than the porosity estimates.

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Near-surface real-time seismic imaging using parsimonious interferometry

Scientific Reports ◽

10.1038/s41598-021-86531-5 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Sherif M. Hanafy ◽

Hussein Hoteit ◽

Jing Li ◽

Gerard T. Schuster

Keyword(s):

Fluid Flow ◽

Real Time ◽

Temporal Resolution ◽

Seismic Imaging ◽

Time Lapse ◽

Rock Properties ◽

Recording Time ◽

Near Surface ◽

Arrival Times ◽

Order Of Magnitude

AbstractResults are presented for real-time seismic imaging of subsurface fluid flow by parsimonious refraction and surface-wave interferometry. Each subsurface velocity image inverted from time-lapse seismic data only requires several minutes of recording time, which is less than the time-scale of the fluid-induced changes in the rock properties. In this sense this is real-time imaging. The images are P-velocity tomograms inverted from the first-arrival times and the S-velocity tomograms inverted from dispersion curves. Compared to conventional seismic imaging, parsimonious interferometry reduces the recording time and increases the temporal resolution of time-lapse seismic images by more than an order-of-magnitude. In our seismic experiment, we recorded 90 sparse data sets over 4.5 h while injecting 12-tons of water into a sand dune. Results show that the percolation of water is mostly along layered boundaries down to a depth of a few meters, which is consistent with our 3D computational fluid flow simulations and laboratory experiments. The significance of parsimonious interferometry is that it provides more than an order-of-magnitude increase of temporal resolution in time-lapse seismic imaging. We believe that real-time seismic imaging will have important applications for non-destructive characterization in environmental, biomedical, and subsurface imaging.

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A Machine Learning-Based Seismic Data Compression and Interpretation Using a Novel Shifted-Matrix Decomposition Algorithm

Applied Sciences ◽

10.3390/app11114874 ◽

2021 ◽

Vol 11 (11) ◽

pp. 4874

Author(s):

Milan Brankovic ◽

Eduardo Gildin ◽

Richard L. Gibson ◽

Mark E. Everett

Keyword(s):

Real Time ◽

Seismic Data ◽

Matrix Decomposition ◽

Original Data ◽

Velocity Estimation ◽

Singular Vectors ◽

Well Stimulation ◽

Marine Seismic ◽

Value Decomposition ◽

Seismic Data Compression

Seismic data provides integral information in geophysical exploration, for locating hydrocarbon rich areas as well as for fracture monitoring during well stimulation. Because of its high frequency acquisition rate and dense spatial sampling, distributed acoustic sensing (DAS) has seen increasing application in microseimic monitoring. Given large volumes of data to be analyzed in real-time and impractical memory and storage requirements, fast compression and accurate interpretation methods are necessary for real-time monitoring campaigns using DAS. In response to the developments in data acquisition, we have created shifted-matrix decomposition (SMD) to compress seismic data by storing it into pairs of singular vectors coupled with shift vectors. This is achieved by shifting the columns of a matrix of seismic data before applying singular value decomposition (SVD) to it to extract a pair of singular vectors. The purpose of SMD is data denoising as well as compression, as reconstructing seismic data from its compressed form creates a denoised version of the original data. By analyzing the data in its compressed form, we can also run signal detection and velocity estimation analysis. Therefore, the developed algorithm can simultaneously compress and denoise seismic data while also analyzing compressed data to estimate signal presence and wave velocities. To show its efficiency, we compare SMD to local SVD and structure-oriented SVD, which are similar SVD-based methods used only for denoising seismic data. While the development of SMD is motivated by the increasing use of DAS, SMD can be applied to any seismic data obtained from a large number of receivers. For example, here we present initial applications of SMD to readily available marine seismic data.

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