000			02963nab a2200253 4500
005			20260520004419.0
008			260224s2013 xxu ing
041			_aInglés
100	1		_aHan, Choongyong _940403
100	1		_aWallis, John _953242
100	1		_aSarma, Pallav _953243
100	1		_aLi, Gary _953244
100	1		_aSchrader, Mark L. _953245
100	1		_aChen, Wen _941014
245	0	0	_aAdaptation of the CPR Preconditioner for Efficient Solution of the Adjoint Equation
260			_cabr. 2013
270			_a28/05/2013 ; 28/05/2013
300			_a6 p. ; 207-213
520			_aTranscripción del resumen del autor: It is well known that the adjoint approach is the most efficient approach for gradient calculation, and it can be used with gradient-based optimization techniques to solve various optimization problems, such as the production-optimization problem and the history-matching problem. The adjoint equation to be solved in the approach is a linear equation formed with the "transpose" of the Jacobian matrix from a fully implicit reservoir simulator. For a large and/or complex reservoir model, generalized preconditioners often prove impractical for solving the adjoint equation. Preconditioners specialized for reservoir simulation, such as constrained pressure residual (CPR), exploit properties of the Jacobian matrix to accelerate convergence, so they cannot be applied directly to the adjoint equation. To overcome this challenge, we have developed a new two-stage preconditioner for efficient solution of the adjoint equation by adaptation of the CPR preconditioner (named CPRA: CPR preconditioner for adjoint equation). The CPRA preconditioner has been coupled with an algebraic multigrid (AMG) linear solver and implemented in Chevron's extended applications reservoir simulator (CHEARS(R)). The AMG solver is well known for its outstanding capability to solve the pressure equation of complex reservoir models; solving the linear system with the "transpose" of the pressure matrix is one of the two stages of construction of the CPRA preconditioner. Through test cases, we have confirmed that the CPRA/AMG solver with generalized minimal residual (GMRES) acceleration solves the adjoint equation very efficiently with a reasonable number of linear-solver iterations. Adjoint simulations to calculate the gradients with the CPRA/AMG solver take approximately the same amount of time (at most) as do the corresponding CPR/AMG forward simulations. Accuracy of the solutions has also been confirmed by verifying the gradients against solutions with a direct solver. A production-optimization case study for a real field using the CPRA/AMG solver has further validated its accuracy, efficiency, and the capability to perform long-term optimization for large, complex reservoir models at low computational cost.
581			_a2
773	0		_tSPE Journal _g18
942			_cARTICULO
999			_c187717 _d187717