Improved Reservoir Characterization Using Automatic History Matching Procedures
Authors: Tan Thomas B. , Kalogerakis N. 
Published: March 2010

Abstract
The primary task in reservoir history matching is the estimation of reservoir parameters such as porosities and permeabilities so that the model predictions match as closely as possible the field measurements. In actual practice, prior estimates of the most probable values of the parameters are usually available. Bayesian estimation was used to incorporate this prior information into a 3-D 3-Phase reservoir simulator with automatic history matching capabilities. In addition, minimum and maximum constraints were imposed on the parameter values.
A case study is presented where the postulated reservoir model is quite different from the actual reservoir. It is shown how parameter estimates obtained by automatic history matching may be used to help modify the postulated model These included changes to reservoir boundaries and detection of impermeable barriers within the reservoir. Estimates of predicted in-place hydrocarbon volumes were also compared with actual values. The final reservoir description is shown to be a significant improvement over the initial postulated model.

Introduction
In the history matching phase of a reservoir simulation study, the problem is to develop a grid cell model that represents the geological structure of the underground reservoir and to specify the distribution of reservoir parameters such as porosity and permeability so that the model predictions match the observed field data. We(1) have recently developed an efficient three-dimensional three-phase reservoir simulator with automatic history matching capability. Using an industry standard problem to generate artificial observation data, it was shown that the model was able to restore the original value of the permeability and porosity. We have also demonstrated how the model can be used to provide quantitative estimates of the reliability of the calculated parameters, and thus to identify the zones that have a significant effect on the match of the field data(2). The postulated grid cell models used in the evaluation of the automatic history matching procedure were identical to the actual models used to generate the artificial observation data through random noise was added to represent measurement errors in the field.
However, in an actual field study, the postulated grid cell model may not accurately represent the reservoir description since the geological interpretation of seismic data and well samples may be in error. In addition, the variation in rock properties will be such that the postulated grid cell model will not have enough detail to represent it. Several researchers(3, 4) have attempted to use very fine grids with hundreds of thousands of cells to model reservoir heterogeneities. Unfortunately, with current computer hardware and numerical techniques, it is still not possible to use many thousands of parameter to describe the variation in porosity and permeability in reservoir rock both areally and vertically. We have to resort to a zonation approach whereby the reservoir is divided into a relatively small number of zones In each of which the parameters have constant values that have to be estimated. The size, shape and arrangement of these zones in tile postulated model will definitely be quite different than the true reservoir distribution.