Integrating time‐lapse seismic and flow simulation to map saturation changes: A reservoir monitoring case study

2000 ◽  
Author(s):  
Madhumita Sengupta ◽  
Gary Mavko ◽  
Tapan Mukerji
Keyword(s):  
Flow Simulation ◽  
Time Lapse ◽  
Reservoir Monitoring

Quantifying subresolution saturation scales from time‐lapse seismic data: A reservoir monitoring case study

Geophysics ◽  
2003 ◽  
Vol 68 (3) ◽  
pp. 803-814 ◽  
Author(s):  
Madhumita Sengupta ◽  
Gary Mavko ◽  
Tapan Mukerji
Keyword(s):  
Seismic Response ◽  
Seismic Data ◽  
Flow Simulation ◽  
Time Lapse ◽  
Seismic Survey ◽  
Spatial Frequencies ◽  
Reservoir Monitoring ◽  
Offset Time ◽  
Flow Simulator

The goal of this paper is to interpret and analyze time‐lapse seismic data quantitatively to better understand subsurface fluid saturations and saturation scales. We present a case study of a time‐lapse seismic survey. Water and gas were injected into an oil‐producing reservoir, and repeat seismic surveys were collected to monitor the subsurface fluids over a period of 14 years. In this study, we show that the subresolution spatial distribution of fluids, not captured by traditional flow simulators can impact the seismic response. Although there is a good qualitative match between the fluid changes predicted by the flow simulator and the fluid changes interpreted from the seismic data, the simulator predicts smooth saturation profiles that do not quantitatively match the time‐lapse seismic changes. We find that downscaling smooth saturation outputs from the flow simulator to a more realistic patchy distribution is required to provide a good quantitative match with the near‐ and far‐offset time‐lapse data, even though the fine details in the saturation distribution are below seismic resolution. We downscaled the smooth saturations from the simulator by incorporating high spatial frequencies from well logs and constraining the saturations to the total mass balance predicted by the flow simulator. The computed seismic response of the downscaled saturation distributions matched the real time‐lapse seismic data much better than the saturation distributions taken directly from the simulator. This study demonstrates the feasibility of using seismic and well‐log data to constrain subblock saturation scales, unobtainable from flow simulation alone. This important result has the potential to significantly impact and enhance the applicability of seismic data in reservoir monitoring.

An LWD Open-Hole Sigma for Time-Lapse Reservoir Monitoring - Case Study From South East Asia

2014 ◽  
Author(s):  
Khairul Azhar Abu Bakar ◽  
Nur Atiqah Hassan ◽  
Thanapala S.Murugesu ◽  
Han Mei ◽  
Samie Sok Foon Lee ◽  
...  
Keyword(s):  
East Asia ◽  
Time Lapse ◽  
South East Asia ◽  
Reservoir Monitoring ◽  
Open Hole

Time-lapse full-waveform inversion as a reservoir-monitoring tool — A North Sea case study

2016 ◽  
Vol 35 (10) ◽  
pp. 850-858 ◽  
Author(s):  
Erik Hicks ◽  
Henning Hoeber ◽  
Marianne Houbiers ◽  
Séverine Pannetier Lescoffit ◽  
Andrew Ratcliffe ◽  
...  
Keyword(s):  
North Sea ◽  
Waveform Inversion ◽  
Time Lapse ◽  
Full Waveform Inversion ◽  
Monitoring Tool ◽  
Full Waveform ◽  
Reservoir Monitoring

Cross‐equalization data processing for time‐lapse seismic reservoir monitoring: A case study from the Gulf of Mexico

Geophysics ◽  
2001 ◽  
Vol 66 (4) ◽  
pp. 1015-1025 ◽  
Author(s):  
J. E. Rickett ◽  
D. E. Lumley
Keyword(s):  
Gulf Of Mexico ◽  
Data Processing ◽  
Seismic Data ◽  
Matched Filter ◽  
Time Lapse ◽  
Data Sets ◽  
Reservoir Monitoring ◽  
The Cross ◽  
Processing Flow

Nonrepeatable noise, caused by differences in vintages of seismic acquisition and processing, can often make comparison and interpretation of time‐lapse 3‐D seismic data sets for reservoir monitoring misleading or futile. In this Gulf of Mexico case study, the major causes of nonrepeatable noise in the data sets are the result of differences in survey acquisition geometry and binning, temporal and spatial amplitude gain, wavelet bandwidth and phase, differential static time shifts, and relative mispositioning of imaged reflection events. We attenuate these acquisition and processing differences by developing and applying a cross‐equalization data processing flow for time‐lapse seismic data. The cross‐equalization flow consists of regridding the two data sets to a common grid; applying a space and time‐variant amplitude envelope balance; applying a first pass of matched filter corrections for global amplitude, bandwidth, phase and static shift corrections, followed by a dynamic warp to align mispositioned events; and, finally, running a second pass of constrained space‐variant matched filter operators. Difference sections obtained by subtracting the two data sets after each step of the cross‐equalization processing flow show a progressive reduction of nonrepeatable noise and a simultaneous improvement in time‐lapse reservoir signal.

scholarly journals Evaluating the utility of time-lapse imaging in the estimation of post-mortem interval: An Australian case study

2019 ◽  
Vol 1 ◽  
pp. 204-210 ◽  
Author(s):  
Alyson Wilson ◽  
Stanley Serafin ◽  
Dilan Seckiner ◽  
Rachel Berry ◽  
Xanthé Mallett
Keyword(s):  
Time Lapse ◽  
Post Mortem ◽  
Post Mortem Interval ◽  
Australian Case ◽  
Time Lapse Imaging

Dynamic Petrophysics-Applications of Time-Lapse Reservoir Monitoring in Saudi Arabia

2005 ◽  
Author(s):  
Shouxiang Mark Ma ◽  
Raghu Ramamoorthy ◽  
Abdulrasool Al-Hajari ◽  
Oscar Kelder ◽  
Ashok Srivastava
Keyword(s):  
Saudi Arabia ◽  
Time Lapse ◽  
Reservoir Monitoring

Spatially limited tomographic inversion for time-lapse oil reservoir monitoring

2002 ◽  
Vol 33 (1) ◽  
pp. 18-22
Author(s):  
Toshiyuki Yokota ◽  
Akio Nishida ◽  
Shigeharu Mizohata ◽  
Sunao Muraoka
Keyword(s):  
Time Lapse ◽  
Oil Reservoir ◽  
Tomographic Inversion ◽  
Reservoir Monitoring
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