scholarly journals History-matching flow simulations and time-lapse seismic data from the Sleipner CO2 plume

2010 ◽  
Vol 7 (1) ◽  
pp. 1171-1182 ◽  
Author(s):  
R. A. Chadwick ◽  
D. J. Noy
Keyword(s):  
Seismic Data ◽  
History Matching ◽  
Time Lapse ◽  
Flow Simulations ◽  
Co2 Plume

Adjoint-Based History Matching of Structural Models Using Production and Time-Lapse Seismic Data

2013 ◽  
Author(s):  
S. Kahrobaei ◽  
G. M. van Essen ◽  
J. F. M. Van Doren ◽  
P. M. J. Van Den Hof ◽  
J. D. Jansen
Keyword(s):  
Seismic Data ◽  
History Matching ◽  
Structural Models ◽  
Time Lapse

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.

Reprocessing of time‐lapse seismic data improves seismic history matching

1999 ◽  
Author(s):  
Xuri Huang ◽  
Thaddeus Charles Jones ◽  
Albert Berni
Keyword(s):  
Seismic Data ◽  
History Matching ◽  
Time Lapse

Adjoint-based History-Matching of Production and Time-lapse Seismic Data

2012 ◽  
Author(s):  
Gijs Van Essen ◽  
Eduardo Jimenez ◽  
Justyna Katarzyna Przybysz-jarnut ◽  
Lior Horesh ◽  
Sippe G. Douma ◽  
...  
Keyword(s):  
Seismic Data ◽  
History Matching ◽  
Time Lapse

A Comparison of Stochastic Data-Integration Algorithms for the Joint History Matching of Production and Time-Lapse Seismic Data

2011 ◽  
Author(s):  
Long Jin ◽  
Faruk Omer Alpak ◽  
Paul van den Hoek ◽  
Carlos Pirmez ◽  
Tope Fehintola ◽  
...  
Keyword(s):  
Data Integration ◽  
Seismic Data ◽  
History Matching ◽  
Time Lapse ◽  
Integration Algorithms ◽  
Stochastic Data
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