Optimal well-placement using probabilistic learning

Roger Ghanem (1) , Christian Soize (2) , Charanraj Thimmisetty (3)

1 USC - University of Southern California
2 MSME - Laboratoire de Modélisation et Simulation Multi Echelle
3 LLNL - Lawrence Livermore National Laboratory

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

Domaines: Sciences de l'ingénieur [physics]; Probabilités [math.PR]; Statistiques [math.ST]; Mécanique [physics.med-ph]

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⟩