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Conference papers
A Derivative Free Optimization method for reservoir characterization inverse problem
Abstract : Reservoir characterization inverse problem aims at building reservoir models consistent with available production and seismic data for better forecasting of the production of a field. These observed data (pressures, oil/water/gas rates at the wells and 4D seismic data) are compared with simulated data to determine unknown petrophysical properties of the reservoir. The underlying optimization problem is usually formulated as the minimization of a least-squares objective function composed of two terms : the production data and the seismic data mismatch. In practice, this problem is often solved by nonlinear optimization methods, such as Sequential Quadratic Programming methods with derivatives approximated by finite differences. In applications involving 4D seismic data, the use of the classical Gauss-Newton algorithm is often infeasible because the computation of the Jacobian matrix is CPU time consuming and its storage is impossible for large datasets like seismic-related ones. Consequently, this optimization problem requires dedicated techniques: derivatives are not available, the associated forward problems are CPU time consuming and some constraints may be introduced to handle a priori information. We propose a derivative free optimization method under constraints based on trust region approach coupled with local quadratic interpolating models of the cost function and of non linear constraints. Results obtained with this method on a synthetic reservoir application with the joint inversion of production data and 4D seismic data are presented. Its performance is compared with a classical SQP method (quasi-Newton approach based on classical BFGS approximation of the Hessian of the objective function with derivatives approximated by finite differences) in terms of number of simulations of the forward problem.
Cited literature [29 references]
https://hal-ifp.archives-ouvertes.fr/hal-02284365
Contributor : Pascale Mallaret <>
Submitted on : Wednesday, September 11, 2019 - 4:45:48 PM
Last modification on : Tuesday, January 12, 2021 - 1:58:02 PM
Long-term archiving on: : Friday, February 7, 2020 - 4:43:36 PM
Contributor : Pascale Mallaret <>
Submitted on : Wednesday, September 11, 2019 - 4:45:48 PM
Last modification on : Tuesday, January 12, 2021 - 1:58:02 PM
Long-term archiving on: : Friday, February 7, 2020 - 4:43:36 PM
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