Assessing the value of time‐lapse seismic data in joint inversion for reservoir parameter estimation in an oil reservoir subjected to water flooding recovery: A synthetic example

2009 ◽  
Author(s):  
Armando Sena ◽  
Paul Stoffa ◽  
Mrinal Sen ◽  
Roustam Seif
Keyword(s):  
Parameter Estimation ◽  
Seismic Data ◽  
Joint Inversion ◽  
Time Lapse ◽  
Water Flooding ◽  
Value Of Time ◽  
Oil Reservoir ◽  
Reservoir Parameter

scholarly journals Joint Inversion of Time-Lapse Seismic Data

2019 ◽  
Author(s):  
Cesar Barajas-Olalde ◽  
Donald Adams ◽  
Lu Jin ◽  
Jun He ◽  
Nicholas Kalenze ◽  
...  
Keyword(s):  
Seismic Data ◽  
Joint Inversion ◽  
Time Lapse

Time‐lapse joint inversion of DC and seismic data

2011 ◽  
Author(s):  
M. Karaoulis ◽  
A. Revil ◽  
D. D. Werkema
Keyword(s):  
Seismic Data ◽  
Joint Inversion ◽  
Time Lapse

scholarly journals Direct Reservoir Parameter Estimation Using Joint Inversion ofMarine Seismic AVA&CSEM Data

2005 ◽  
Author(s):  
G. Michael Hoversten ◽  
Florence Cassassuce ◽  
Erika Gasperikova ◽  
Gregory A. Newman ◽  
Yoram Rubin ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Joint Inversion ◽  
Reservoir Parameter

Remaining oil thickness inversion of time-lapse seismic data in water flooding reservoir

2019 ◽  
Author(s):  
Guangyi Hu ◽  
Xianwen Zhang ◽  
Tingen Fan ◽  
Huilai Zhang ◽  
Zongjun Wang ◽  
...  
Keyword(s):  
Seismic Data ◽  
Time Lapse ◽  
Water Flooding ◽  
Remaining Oil ◽  
Oil Thickness

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.

Reservoir Parameter Estimation Using Seismic Avo & Marine Em Joint Inversion

2004 ◽  
Author(s):  
G. M. Hoversten ◽  
F. Cassassuce ◽  
G. A. Newman
Keyword(s):  
Parameter Estimation ◽  
Joint Inversion ◽  
Reservoir Parameter

Direct reservoir parameter estimation using joint inversion of seismic AVO & marine CSEM data

2004 ◽  
Author(s):  
G. Michael Hoversten ◽  
Florence Cassassuce ◽  
Gregory A. Newman
Keyword(s):  
Parameter Estimation ◽  
Joint Inversion ◽  
Marine Csem ◽  
Reservoir Parameter

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.

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