Skip to Main content Skip to Navigation
Journal articles

Optimal well-placement using probabilistic learning

Abstract : A new method based on manifold sampling is presented for formulating and solving the optimal well-placement problem in an uncertain reservoir. The method addresses the compounded computational challenge associated with statistical sampling at each iteration of the optimization process. An estimation of the joint probability density function between well locations and production levels is achieved using a small number of expensive function calls to a reservoir simulator. Additional realizations of production levels, conditioned on well locations and required for evaluating the probabilistic objective function, are then obtained by sampling this jpdf without recourse to the reservoir simulator
Complete list of metadatas

Cited literature [28 references]  Display  Hide  Download

https://hal-upec-upem.archives-ouvertes.fr/hal-01703255
Contributor : Christian Soize <>
Submitted on : Wednesday, February 7, 2018 - 4:09:40 PM
Last modification on : Saturday, April 4, 2020 - 10:31:09 AM
Long-term archiving on: : Saturday, May 5, 2018 - 1:29:02 PM

File

publi-2018-DEDA-ghanem-soize-t...
Files produced by the author(s)

Identifiers

Collections

Citation

Roger Ghanem, Christian Soize, Charanraj Thimmisetty. Optimal well-placement using probabilistic learning. Data-Enabled Discovery and Applications, 2018, 2 (1), ⟨10.1007/s41688-017-0014-x⟩. ⟨hal-01703255⟩

Share

Metrics

Record views

681

Files downloads

1142