Assimilation of Time-Lapse Temperature Observations and 4D-Seismic Data With the EnKF in SAGD Petroleum Reservoirs

2015 ◽  
Vol 54 (03) ◽  
pp. 164-182 ◽  
Author(s):  
Yevgeniy Zagayevskiy ◽  
Clayton Vernon Deutsch
Keyword(s):  
Seismic Data ◽  
Petroleum Reservoirs ◽  
Time Lapse ◽  
Temperature Observations ◽  
4D Seismic

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.

Faster Convergence in Seismic History Matching by Dividing and Conquering the Unknowns

SPE Journal ◽  
2010 ◽  
Vol 15 (04) ◽  
pp. 1077-1088 ◽  
Author(s):  
F.. Sedighi ◽  
K.D.. D. Stephen
Keyword(s):  
Regression Analysis ◽  
Seismic Data ◽  
History Matching ◽  
Time Lapse ◽  
Business Decisions ◽  
Order Of Magnitude ◽  
Reservoir Simulation Model ◽  
The Uk ◽  
Pressure Changes ◽  
4D Seismic

Summary Seismic history matching is the process of modifying a reservoir simulation model to reproduce the observed production data in addition to information gained through time-lapse (4D) seismic data. The search for good predictions requires that many models be generated, particularly if there is an interaction between the properties that we change and their effect on the misfit to observed data. In this paper, we introduce a method of improving search efficiency by estimating such interactions and partitioning the set of unknowns into noninteracting subspaces. We use regression analysis to identify the subspaces, which are then searched separately but simultaneously with an adapted version of the quasiglobal stochastic neighborhood algorithm. We have applied this approach to the Schiehallion field, located on the UK continental shelf. The field model, supplied by the operator, contains a large number of barriers that affect flow at different times during production, and their transmissibilities are highly uncertain. We find that we can successfully represent the misfit function as a second-order polynomial dependent on changes in barrier transmissibility. First, this enables us to identify the most important barriers, and, second, we can modify their transmissibilities efficiently by searching subgroups of the parameter space. Once the regression analysis has been performed, we reduce the number of models required to find a good match by an order of magnitude. By using 4D seismic data to condition saturation and pressure changes in history matching effectively, we have gained a greater insight into reservoir behavior and have been able to predict flow more accurately with an efficient inversion tool. We can now determine unswept areas and make better business decisions.

Fluid dynamics at the base of hydrate-bearing sediments at the Vestnesa Ridge inferred from 5 years of high-resolution 4D seismic surveying

2020 ◽  
Author(s):  
Malin Waage ◽  
Stefan Bünz ◽  
Kate Waghorn ◽  
Sunny Singhorha ◽  
Pavel Serov
Keyword(s):  
High Resolution ◽  
Gas Hydrate ◽  
Seismic Data ◽  
Time Lapse ◽  
Stability Zone ◽  
Seismic Surveys ◽  
Free Gas ◽  
4D Seismic ◽  
Hydrate Bearing Sediments ◽  
Hydrate Stability

<p>The transition from gas hydrate to gas-bearing sediments at the base of the hydrate stability zone (BHSZ) is commonly identified on seismic data as a bottom-simulating reflection (BSR). At this boundary, phase transitions driven by thermal effects, pressure alternations, and gas and water flux exist. Sedimentation, erosion, subsidence, uplift, variations in bottom water temperature or heat flow cause changes in marine gas hydrate stability leading to expansion or reduction of gas hydrate accumulations and associated free gas accumulations. Pressure build-up in gas accumulations trapped beneath the hydrate layer may eventually lead to fracturing of hydrate-bearing sediments that enables advection of fluids into the hydrate layer and potentially seabed seepage. Depletion of gas along zones of weakness creates hydraulic gradients in the free gas zone where gas is forced to migrate along the lower hydrate boundary towards these weakness zones. However, due to lack of “real time” data, the magnitude and timescales of processes at the gas hydrate – gas contact zone remains largely unknown. Here we show results of high resolution 4D seismic surveys at a prominent Arctic gas hydrate accumulation – Vestnesa ridge - capturing dynamics of the gas hydrate and free gas accumulations over 5 years. The 4D time-lapse seismic method has the potential to identify and monitor fluid movement in the subsurface over certain time intervals. Although conventional 4D seismic has a long history of application to monitor fluid changes in petroleum reservoirs, high-resolution seismic data (20-300 Hz) as a tool for 4D fluid monitoring of natural geological processes has been recently identified.<br><br>Our 4D data set consists of four high-resolution P-Cable 3D seismic surveys acquired between 2012 and 2017 in the eastern segment of Vestnesa Ridge. Vestnesa Ridge has an active fluid and gas hydrate system in a contourite drift setting near the Knipovich Ridge offshore W-Svalbard. Large gas flares, ~800 m tall rise from seafloor pockmarks (~700 m diameter) at the ridge axis. Beneath the pockmarks, gas chimneys pierce the hydrate stability zone, and a strong, widespread BSR occurs at depth of 160-180 m bsf. 4D seismic datasets reveal changes in subsurface fluid distribution near the BHSZ on Vestnesa Ridge. In particular, the amplitude along the BSR reflection appears to change across surveys. Disappearance of bright reflections suggest that gas-rich fluids have escaped the free gas zone and possibly migrated into the hydrate stability zone and contributed to a gas hydrate accumulation, or alternatively, migrated laterally along the BSR. Appearance of bright reflection might also indicate lateral migration, ongoing microbial or thermogenic gas supply or be related to other phase transitions. We document that faults, chimneys and lithology constrain these anomalies imposing yet another control on vertical and lateral gas migration and accumulation. These time-lapse differences suggest that (1) we can resolve fluid changes on a year-year timescale in this natural seepage system using high-resolution P-Cable data and (2) that fluids accumulate at, migrate to and migrate from the BHSZ over the same time scale.</p>

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.

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.

The combined effects of pressure and cementation on 4D seismic data

Geophysics ◽  
2015 ◽  
Vol 80 (2) ◽  
pp. WA135-WA148 ◽  
Author(s):  
Matthew Saul ◽  
David Lumley
Keyword(s):  
Pore Pressure ◽  
Seismic Data ◽  
Time Pressure ◽  
Time Lapse ◽  
Reservoir Rock ◽  
Elastic Theory ◽  
Rock Properties ◽  
Baseline Survey ◽  
Core Data ◽  
4D Seismic

Time-lapse seismology has proven to be a useful method for monitoring reservoir fluid flow, identifying unproduced hydrocarbons and injected fluids, and improving overall reservoir management decisions. The large magnitudes of observed time-lapse seismic anomalies associated with strong pore pressure increases are sometimes not explainable by velocity-pressure relationships determined by fitting elastic theory to core data. This can lead to difficulties in interpreting time-lapse seismic data in terms of physically realizable changes in reservoir properties during injection. It is commonly assumed that certain geologic properties remain constant during fluid production/injection, including rock porosity and grain cementation. We have developed a new nonelastic method based on rock physics diagnostics to describe the pressure sensitivity of rock properties that includes changes in the grain contact cement, and we applied the method to a 4D seismic data example from offshore Australia. We found that water injection at high pore pressure may mechanically weaken the poorly consolidated reservoir sands in a nonelastic manner, allowing us to explain observed 4D seismic signals that are larger than can be predicted by elastic theory fits to the core data. A comparison of our new model with the observed 4D seismic response around a large water injector suggested a significant mechanical weakening of the reservoir rock, consistent with a decrease in the effective grain contact cement from 2.5% at the time/pressure of the preinjection baseline survey, to 0.75% at the time/pressure of the monitor survey. This approach may enable more accurate interpretations and future predictions of the 4D signal for subsequent monitor surveys and improve 4D feasibility and interpretation studies in other reservoirs with geomechanically similar rocks.

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.

Machine learning to reduce cycle time for time-lapse seismic data assimilation into reservoir management

2019 ◽  
Vol 7 (3) ◽  
pp. SE123-SE130
Author(s):  
Yang Xue ◽  
Mariela Araujo ◽  
Jorge Lopez ◽  
Kanglin Wang ◽  
Gautam Kumar
Keyword(s):  
Machine Learning ◽  
Data Assimilation ◽  
Seismic Data ◽  
Reservoir Management ◽  
Time Lapse ◽  
Water Flooding ◽  
Reservoir Models ◽  
Reservoir Property ◽  
Property Changes ◽  
4D Seismic

Time-lapse (4D) seismic is widely deployed in offshore operations to monitor improved oil recovery methods including water flooding, yet its value for enhanced well and reservoir management is not fully realized due to the long cycle times required for quantitative 4D seismic data assimilation into dynamic reservoir models. To shorten the cycle, we have designed a simple inversion workflow to estimate reservoir property changes directly from 4D attribute maps using machine-learning (ML) methods. We generated tens of thousands of training samples by Monte Carlo sampling from the rock-physics model within reasonable ranges of the relevant parameters. Then, we applied ML methods to build the relationship between the reservoir property changes and the 4D attributes, and we used the learnings to estimate the reservoir property changes given the 4D attribute maps. The estimated reservoir property changes (e.g., water saturation changes) can be used to analyze injection efficiency, update dynamic reservoir models, and support reservoir management decisions. We can reduce the turnaround time from months to days, allowing early engagements with reservoir engineers to enhance integration. This accelerated data assimilation removes a deterrent for the acquisition of frequent 4D surveys.

Seismic History Matching of Nelson Using Time-Lapse Seismic Data: An Investigation of 4D Signature Normalization

2011 ◽  
Vol 14 (05) ◽  
pp. 621-633 ◽  
Author(s):  
Alireza Kazemi ◽  
Karl D. Stephen ◽  
Asghar Shams
Keyword(s):  
Seismic Data ◽  
History Matching ◽  
Time Lapse ◽  
Production Data ◽  
Similar Degree ◽  
Full Field ◽  
Matching Process ◽  
Production History ◽  
Induced Changes ◽  
4D Seismic

Summary History matching of a reservoir model is always a difficult task. In some fields, we can use time-lapse (4D) seismic data to detect production-induced changes as a complement to more conventional production data. In seismic history matching, we predict these data and compare to observations. Observed time-lapse data often consist of relative measures of change, which require normalization. We investigate different normalization approaches, based on predicted 4D data, and assess their impact on history matching. We apply the approach to the Nelson field in which four surveys are available over 9 years of production. We normalize the 4D signature in a number of ways. First, we use predictions of 4D signature from vertical wells that match production, and we derive a normalization function. As an alternative, we use crossplots of the full-field prediction against observation. Normalized observations are used in an automatic-history-matching process, in which the model is updated. We analyze the results of the two normalization approaches and compare against the case of just using production data. The result shows that when we use 4D data normalized to wells, we obtain 49% reduced misfit along with 36% improvement in predictions. Also over the whole reservoir, 8 and 7% reduction of misfits for 4D seismic are obtained in history and prediction periods, respectively. When we use only production data, the production history match is improved to a similar degree (45%), but in predictions, the improvement is only 25% and the 4D seismic misfit is 10% worse. Finding the unswept areas in the reservoir is always a challenge in reservoir management. By using 4D data in history matching, we can better predict reservoir behavior and identify regions of remaining oil.

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