Estimation of thickness and velocity changes of injected carbon dioxide layers from prestack time-lapse seismic data

Geophysics ◽  
2009 ◽  
Vol 74 (2) ◽  
pp. O17-O28 ◽  
Author(s):  
Amir Ghaderi ◽  
Martin Landrø
Keyword(s):  
Carbon Dioxide ◽  
Seismic Analysis ◽  
Time Lapse ◽  
Time Shift ◽  
Amplitude Analysis ◽  
Velocity Change ◽  
Shift Analysis ◽  
Velocity Changes ◽  
Single Formula ◽  
4D Seismic

In this study, we bring together the two main categories of time-lapse seismic analysis — amplitude analysis and time-shift analysis — to estimate simultaneously the changes in thickness and velocity of a 4D seismic anomaly. The methodology is applied to time-lapse seismic monitoring of carbon dioxide [Formula: see text] storage at Sleipner field, Norway, that shows significant 4D effects. The 4D anomalies resulting from [Formula: see text] injection appear as a multilayer reflection pattern within the relatively shallow Utsira Sand. This multireflective appearance within the sand layer is interpreted as [Formula: see text] layers trapped below thin shale layers. Because most of the [Formula: see text] layers are believed to be thin [Formula: see text], the interference between top and base of these layers needs to be taken into account in 4D seismic analysis. By studying the reflected event from a horizon below the Utsira Sand, we estimate 4D traveltime shifts caused by the presence of the [Formula: see text] layer above thishorizon. We then combine these traveltime shifts with measured amplitude changes for the top and base of the [Formula: see text] layer to estimate velocity and thickness changes for the thin [Formula: see text] layer. In 1999, after three years of injection, the most likely velocity change was around [Formula: see text] and the thickness of the [Formula: see text] layer was around [Formula: see text]. In 2001, the corresponding velocity change and thickness estimates were [Formula: see text] and [Formula: see text], respectively. Finally, in 2002, the most likely velocity change was [Formula: see text] and the thickness of the [Formula: see text] layer was [Formula: see text]. It is not straightforward to apply this method to a stack of [Formula: see text] layers because 4D time shifts below the Utsira Sand only provide information about the average time shift for all layers. The amplitude information for each individual [Formula: see text] layer cannot be resolved without knowing the velocity change within each layer. However, our result from a single [Formula: see text] layer may be used to constrain the velocity changes for the multilayer [Formula: see text] case.

Quantitative estimation of compaction and velocity changes using 4D impedance and traveltime changes

Geophysics ◽  
2004 ◽  
Vol 69 (4) ◽  
pp. 949-957 ◽  
Author(s):  
Martin Landrø ◽  
Jan Stammeijer
Keyword(s):  
Seismic Data ◽  
Quantitative Estimation ◽  
Time Lapse ◽  
Seismic Velocities ◽  
Compaction Process ◽  
Empirical Relationships ◽  
Velocity Change ◽  
Reservoir Rocks ◽  
Velocity Changes ◽  
4D Seismic

In some hydrocarbon reservoirs, severe compaction of the reservoir rocks is observed. This compaction is caused by production, and it is often associated with changes in the overburden. Time‐lapse (or 4D) seismic data are used to monitor this compaction process. Since the compaction causes changes in both layer thickness and seismic velocities, it is crucial to distinguish between the two effects. Two new seismic methods for monitoring compacting reservoirs are introduced, one based on measured seismic prestack traveltime changes, and the other based on poststack traveltime and amplitude changes. In contrast to earlier methods, these methods do not require additional empirical relationships, such as, for instance, a velocity‐porosity relationship. The uncertainties in estimates for compaction and velocity change are expressed in terms of errors in the traveltime and amplitude measurements. These errors are directly related to the quality and repeatability of time‐lapse seismic data. For a reservoir at 3000‐m depth with 9 m of compaction, and assuming a 4D timeshift error of 0.5 ms at near offset and 2 ms at far offset, we find relative uncertainty in the compaction estimate of approximately 50–60% using traveltime information only.

scholarly journals Unsupervised machine learning for time-lapse seismic studies and reservoir monitoring

2021 ◽  
pp. 1-59
Author(s):  
Marwa Hussein ◽  
Robert R. Stewart ◽  
Deborah Sacrey ◽  
David H. Johnston ◽  
Jonny Wu
Keyword(s):  
Machine Learning ◽  
Reservoir Simulation ◽  
Seismic Data ◽  
Water Saturation ◽  
Seismic Analysis ◽  
Time Lapse ◽  
Seismic Attributes ◽  
Rock Properties ◽  
Unsupervised Machine Learning ◽  
4D Seismic

Time-lapse (4D) seismic analysis plays a vital role in reservoir management and reservoir simulation model updates. However, 4D seismic data are subject to interference and tuning effects. Being able to resolve and monitor thin reservoirs of different quality can aid in optimizing infill drilling or locating bypassed hydrocarbons. Using 4D seismic data from the Maui field in the offshore Taranaki basin of New Zealand, we generate typical seismic attributes sensitive to reservoir thickness and rock properties. We find that spectral instantaneous attributes extracted from time-lapse seismic data illuminate more detailed reservoir features compared to those same attributes computed on broadband seismic data. We develop an unsupervised machine learning workflow that enables us to combine eight spectral instantaneous seismic attributes into single classification volumes for the baseline and monitor surveys using self-organizing maps (SOM). Changes in the SOM natural clusters between the baseline and monitor surveys suggest production-related changes that are caused primarily by water replacing gas as the reservoir is being swept under a strong water drive. The classification volumes also facilitate monitoring water saturation changes within thin reservoirs (ranging from very good to poor quality) as well as illuminating thin baffles. Thus, these SOM classification volumes show internal reservoir heterogeneity that can be incorporated into reservoir simulation models. Using meaningful SOM clusters, geobodies are generated for the baseline and monitor SOM classifications. The recoverable gas reserves for those geobodies are then computed and compared to production data. The SOM classifications of the Maui 4D seismic data seems to be sensitive to water saturation change and subtle pressure depletions due to gas production under a strong water drive.

scholarly journals Correlation and Calibration of 4D Seismic Vintages for Effective Postproduction Intervention

2021 ◽  
pp. 35-46
Author(s):  
B. T. Ojo ◽  
M. T. Olowokere ◽  
M. I. Oladapo
Keyword(s):  
Seismic Data ◽  
Cross Correlation ◽  
Time Lapse ◽  
Time Shift ◽  
Accurate Analysis ◽  
Hydrocarbon Production ◽  
Matching Process ◽  
Future Production ◽  
Correlation Threshold ◽  
4D Seismic

Poor or low data quality usually has an adverse effect on the quantitative usage of (4D) seismic data for accurate analysis. Repeatability of 4D Seismic or time-lapse survey is considered as a vital tool for effective, potent, and impressive monitoring of productivity of reservoirs. Inconsistencies and disagreement of ‘time-lapse’ data will greatly affect the accuracy and outcome of research when comparing two or more seismic surveys having low repeatability. Correlation is a statistic procedure that measures the linear relation between all points of two variables. Error due to acquisition and processing must be checked for before interpretation in order to minimize exploration failure and the number of dry holes drilled. The seismic data available for this study comprises of 779 crosslines and 494 inlines. The 4D seismic data consisting of the base Seismic shot in 1998 before production and the monitor Seismic shot in 2010 at different stages of hydrocarbon production were cross correlated to ascertain repeatability between the two vintages. A global average matching process was applied while phase and time shift were estimated using the Russell-Liang technique. Two pass full shaping filters were applied for the phase matching. Maximum and minimum ‘cross-correlation’ are 0.85 (85%) and 0.60 (60%) respectively. Statistics of the ‘cross-correlation’ shift show standard deviation  (0.3), variance (0.12), and root mean square (0.78). For high percentage repeatability and maximum correlations, the requested correlation threshold is 0.7 but 1 and 0.99 were obtained for the first and the second matching respectively.  Conclusively, the overall results show that there is high repeatability between the 4D seismic data used and the data can be employed conveniently for accurate ‘time-lapse’ (future) production monitoring and investigation on the field.

Time-lapse image-domain tomography using adjoint-state methods

Geophysics ◽  
2013 ◽  
Vol 78 (4) ◽  
pp. A29-A33 ◽  
Author(s):  
Jeffrey Shragge ◽  
Tongning Yang ◽  
Paul Sava
Keyword(s):  
Seismic Velocity ◽  
Time Lapse ◽  
Velocity Estimation ◽  
Data Sets ◽  
Velocity Change ◽  
Image Domain ◽  
Adjoint State ◽  
The Difference ◽  
Monitor Image ◽  
4D Seismic

Adjoint-state methods (ASMs) have proven successful for calculating the gradients of the functionals commonly found in geophysical inverse problems. The 3D ASM image-domain tomography (IDT) formulation of the seismic velocity estimation problem highlights imperfections in migrated image volumes and, using appropriate penalty functions (e.g., differential semblance), forms an objective function that can be minimized using standard optimization approaches. For time-lapse (4D) seismic scenarios, we show that the 3D ASM-IDT approach can be extended to multiple (e.g., baseline and monitor) data sets and offers high-quality estimates of subsurface velocity change. We discuss two different penalty operators that lead to absolute and relative 4D inversion strategies. The absolute approach uses the difference of two independent 3D inversions to estimate a 4D model perturbation (i.e., slowness squared). The relative approach inverts for the model perturbation that optimally matches the monitor image to the baseline image — even if migrated energy is imperfectly focused. Both approaches yield good 4D slowness estimates; however, we assert that the relative approach is more robust given the ubiquitous presence of nonrepeatable 4D acquisition noise and imperfect baseline model estimates.

scholarly journals Short Period Motion Events On Variegated Glacier as Observed By Automatic Photography and Seismic Methods

1986 ◽  
Vol 8 ◽  
pp. 82-89 ◽  
Author(s):  
W.D. Harrison ◽  
C.F. Raymond ◽  
P. MacKeith
Keyword(s):  
Time Resolution ◽  
Time Lapse ◽  
Motion Events ◽  
Velocity Change ◽  
Temporal And Spatial Patterns ◽  
Short Period ◽  
Temporal And Spatial ◽  
Velocity Changes ◽  
Period Motion

A network of automated time-lapse cameras was deployed on Variegated Glacier, Alaska, to establish the temporal and spatial patterns of velocity change at a one-day time resolution. Results from the summers of 1979, 1980, and 1981 are presented; a surge occurred in 1982 and 1983. The principal velocity variations were pulses of increased speed, lasting about one day and referred to as “early”- or “late”-season motion events. The former recurred quasi-periodically on the upper part of the glacier in the early part of the melt season; the latter occurred later in the summer and were correlated with major storms or melting. Supplemental information about the occurrence of motion events was obtained from monitoring of seismic activity. Evidence for several other types of velocity changes was found.

Long-offset time-lapse seismic: Tested on the Valhall LoFS data

Geophysics ◽  
2011 ◽  
Vol 76 (2) ◽  
pp. O1-O13 ◽  
Author(s):  
Hossein Mehdi Zadeh ◽  
Martin Landrø ◽  
Olav Inge Barkved
Keyword(s):  
Synthetic Data ◽  
Maximum Amplitude ◽  
Time Lapse ◽  
Velocity Change ◽  
Seismic Properties ◽  
The North ◽  
Long Offset ◽  
The North Sea ◽  
Offset Time ◽  
Velocity Changes

Conventional time-lapse seismic has been less successful for stiff-rock reservoir monitoring, such as carbonates. This is mainly because of the negligible time-lapse changes in the seismic properties. Therefore, we propose to use long-offset time-lapse seismic as an alternative method to estimate small velocity changes. More specifically, we monitor the maximum amplitude offset that is beyond critical offset. The properties of the maximum amplitude offset are similar to critical offset, except that they appear for longer offsets and are frequency dependent. Increased frequency reduces the gap between this offset and the critical offset. We find that the maximum amplitude offset is a function of overburden and reservoir velocity and practically independent of density. This method requires a velocity increase across the interface that is to be analyzed. This criterion usually is satisfied for stiff-rock reservoirs. Also, by long-offset acquisition, we mean typically 1 to 2 km beyond the critical offset for typical depths. The method is tested on the Valhall chalk field in the North Sea. The predicted velocity change using this method is in-line with an independent acoustic impedance study. The velocity changes quantitatively match reasonably well with the synthetic data.

Modeling and analysis of compaction-induced traveltime shifts for multicomponent seismic data

Geophysics ◽  
2012 ◽  
Vol 77 (6) ◽  
pp. T221-T237 ◽  
Author(s):  
Steven Shawn Smith ◽  
Ilya Tsvankin
Keyword(s):  
Time Lapse ◽  
P Wave ◽  
Time Shift ◽  
S Wave ◽  
Modeling And Analysis ◽  
Velocity Anisotropy ◽  
Induced Stress ◽  
Induced Velocity ◽  
Processing Techniques ◽  
4D Seismic

Modeling of time shifts associated with time-lapse (4D) seismic surveys is helpful in evaluating reservoir depressurization and inverting for subsurface stress. Using coupled geomechanical and full-waveform seismic modeling, we study the influence of compaction-induced stress and strain around a simplified reservoir on compressional (P), shear (S), and mode-converted (PS) waves. We estimate compaction-induced time shifts and analyze their dependence on reflector depth and pressure drop inside the reservoir. Time shifts between synthetic baseline and monitor surveys are obtained by processing techniques that are potentially applicable to field data. Although P-wave time-shift lags for reflectors in the overburden are indicative of induced anisotropy, they are two to three times smaller than S-wave time-shift leads for reflectors beneath the reservoir. We also investigate the contributions of the deviatoric and volumetric stains to the time shifts for all three modes. Time shifts for S- and PS-waves are strongly influenced by elevated volumetric and deviatoric strains inside the reservoir. Almost constant S-wave time shifts for a range of offsets and source locations indicate that the contribution of stress-induced velocity anisotropy to shear-wave signatures is weak because the symmetry is close to elliptical. Our modeling also shows that mild tilt of a rectangular reservoir, or its replacement with an elliptically shaped reservoir of the same aspect ratio, has little influence on time shifts. Potentially, the developed methodology can be applied to estimate compaction-induced stress fields using simple compartmentalized reservoir models.

Sign in / Sign up

Export Citation Format

Share Document