Joint stochastic inversion of geophysical data for reservoir parameter estimation

2003 ◽  
Author(s):  
Jinsong Chen ◽  
G. Michael Hoversten
Keyword(s):  
Parameter Estimation ◽  
Geophysical Data ◽  
Stochastic Inversion ◽  
Reservoir Parameter

Reservoir parameter estimation using a hybrid neural network

2000 ◽  
Vol 26 (8) ◽  
pp. 869-875 ◽  
Author(s):  
F. Aminzadeh ◽  
Jacob Barhen ◽  
C.W. Glover ◽  
N.B. Toomarian
Keyword(s):  
Neural Network ◽  
Parameter Estimation ◽  
Hybrid Neural Network ◽  
Reservoir Parameter

Data-domain correlation approach for joint hydrogeologic inversion of time-lapse hydrogeologic and geophysical data

Geophysics ◽  
2009 ◽  
Vol 74 (6) ◽  
pp. F127-F140 ◽  
Author(s):  
Timothy C. Johnson ◽  
Roelof J. Versteeg ◽  
Hai Huang ◽  
Partha S. Routh
Keyword(s):  
Parameter Estimation ◽  
Hydraulic Conductivity ◽  
Strong Correlation ◽  
Geophysical Methods ◽  
Time Lapse ◽  
Geophysical Data ◽  
Correlation Operator ◽  
Parameter Estimations ◽  
Lack Of Information ◽  
Information Gap

Inverse estimations of hydrogeologic properties often are highly uncertain because of the expense of collecting hydrogeologic data and the subsequent lack of information. Geophysical data potentially can help fill this information gap because geophysical methods can survey large areas remotely and relatively inexpensively. However, geophysical data are difficult to incorporate into hydrogeologic parameter estimations primarily because of a lack of knowledge concerning the petrophysical relationships between hydrogeologic and geophysical parameters. A method can be used that allows time-lapse geophysical data to be incorporated directly into a hydrogeologic parameter estimation when a strong correlation exists between changes in geophysical and hydrogeologic properties. This approach bypasses the need for an explicit petrophysical transform by formulating the geophysical part of the hydrogeologic inversion in terms of a data-domain correlation operator. A synthetic electrical resistivity monitoring application is used to estimate the hydraulic conductivity distribution. Including time-lapse resistivity data to supplement sparse hydrologic data appears to improve greatly the resolution of hydraulic conductivity in this case. More generally, the formulation and results suggest that geophysical monitoring data can be incorporated effectively into a hydrogeologic parameter estimation using a data-domain correlation operator, assuming a strong correlation exists between changes in hydrogeologic and geophysical properties.

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
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