A New Algorithm for Automatic History Matching

Chen, Wen H.; Gavalas, George R.; Seinfeld, John H.; Wasserman, M.L.

doi:10.2118/4545-pa

Cited by 235 publications

(87 citation statements)

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“…Procedures of this type entail the application of optimal control theory and have their roots in the calculus of variations [6,35]. Adjoint-based optimization techniques have been used in a reservoir simulation setting both for history matching (see, e.g., [8,10,23,31,34]) and for production optimization. Much of the early work on their use for optimization of oil recovery was performed by Ramirez and coworkers, who considered the optimization of several different enhanced oil recovery (EOR) processes [25,26,32].…”

Section: Introductionmentioning

confidence: 99%

Adjoint formulation and constraint handling for gradient-based optimization of compositional reservoir flow

et al. 2014

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An adjoint formulation for the gradient-based optimization of oil-gas compositional reservoir simulation problems is presented. The method is implemented within an automatic differentiation-based compositional flow simulator (Stanford's AD-GPRS). The development of adjoint procedures for general compositional problems is much more challenging than for oil-water problems due to the increased complexity of the code and the underlying physics. The treatment of nonlinear constraints, an example of which is a maximum gas rate specification in injection or production wells, when the control variables are well bottom-hole pressures, poses a particular challenge. Two approaches for handling these constraints are presented -a formal treatment within the optimizer, and a simpler heuristic treatment in the forward model. The relationship between discrete and continuous adjoint formulations is also elucidated. duced) relative to reference solutions range from 4.2% to 11.6%. The heuristic treatment of nonlinear constraints is shown to offer a cost-effective means for obtaining feasible solutions, which are in some cases better than those obtained using the formal constraint handling procedure.

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Section: Introductionmentioning

confidence: 99%

Adjoint formulation and constraint handling for gradient-based optimization of compositional reservoir flow

et al. 2014

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“…He et al (1997) further extended Carter's work to three dimensions approximately. For single-phase flow problems, Chen et al (1974) and Chavent et al (1975) proposed a method which was regarded as what we call the adjoint method now. Li et al (2003) presented the first formulation of the adjoint method for three-phase flow problems and pointed out that the coefficient matrix of the adjoint equations is simply the transpose of the Newton-Raphson Jacobian matrix used in a fully implicit reservoir simulator.…”

Section: Gradient-based Algorithmsmentioning

confidence: 99%

A review of closed-loop reservoir management

Hou

Zhou

Zhang³

et al. 2015

Pet. Sci.

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The closed-loop reservoir management technique enables a dynamic and real-time optimal production schedule under the existing reservoir conditions to be achieved by adjusting the injection and production strategies. This is one of the most effective ways to exploit limited oil reserves more economically and efficiently. There are two steps in closed-loop reservoir management: automatic history matching and reservoir production optimization. Both of the steps are large-scale complicated optimization problems. This paper gives a general review of the two basic techniques in closed-loop reservoir management; summarizes the applications of gradient-based algorithms, gradient-free algorithms, and artificial intelligence algorithms; analyzes the characteristics and application conditions of these optimization methods; and finally discusses the emphases and directions of future research on both automatic history matching and reservoir production optimization.

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“…(7), with respect to the spline coefficients We_x,_X, _x --1,2,...,Nxs and _y --1,2,...,Nys, subject to the pressure equation (12). To obtain an algorithm to solve this problem two steps are required.…”

Section: Ll History Matching Algorithmmentioning

confidence: 99%