A New Algorithm for Automatic History Matching
Authors: Chen W.H. , Gavalas G.R. , Seinfeld J.H. , Wasserman M.L. 
Published: December 2007

History-matching problems, in which reservoir parameters are to be estimated from well pressure parameters are to be estimated from well pressure data, are formulated as optimal control problems. The necessary conditions for optimality lead naturally to gradient optimization methods for determining the optimal parameter estimates. the key feature of the approach is that reservoir properties are considered as continuous functions properties are considered as continuous functions of position rather than as uniform in a certain number of zones. The optimal control approach is illustrated on a hypothetical reservoir and on an actual Saudi Arabian reservoir, both characterized by single-phase flow. A significant saving in computing time over conventional constant-zone gradient optimization methods is demonstrated.

Introduction

The process of determining in a mathematical reservoir model unknown parameter valuessuch as permeability and porositythat give the closest permeability and porositythat give the closest fit of measured and calculated pressures is commonly called "history matching." In principle, one would like an automatic routine for history matching, applicable to simulators of varying complexity, one that does not require inordinate amounts of computing time to achieve a set of parameter estimates. In recent years a number of authors have investigated the subject of history matching. All the reported approaches involve dividing the reservoir into a number of zones, in each of which the properties to be estimated are assumed to be uniform. (These zones may, in fact, correspond to the spatial grid employed for the finite-difference solution of the simulator.) Then the history-matching problem becomes that of determining the parameter problem becomes that of determining the parameter values in each of, say, N zones, k1, k2, ..., kN, in such a way that some measure (usually a sum of squares) of the deviation between calculated and observed pressures is minimized. A typical measure of deviation pressures is minimized. A typical measure of deviation is(1)

where p obs (j, ti) and p cal (j, ti) are the observed and calculated pressures at the jth well, which is at location j=(xj, yj), j = 1,2,......, M, and where we have n1 measurements at Well 1 at n1 different times, n2 measurements at Well 2 at n2 different times, . . ., and nM measurements at Well M at nM different times. To carry out the minimization of Eq. 1 with respect to the vector k, most methods rely on some type of gradient optimization procedure that requires computation of the gradient of J with respect to each ki, i = 1, 2, . . ., N. The calculation of J/ ki usually requires, in turn, that one obtain the sensitivity coefficients, p cal/ ki, i = 1, 2, . . ., N; i.e., the first partial derivative of pressure with respect to each parameter. The sensitivity coefficients can be computed, in principle, in several ways. 1. Make a simulator base run with all N parameters at their initial values. Then, perturbing each parameter a small amount, make an additional simulator run for each parameter in the system. parameter in the system. SPEJ

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