Integrating PCA and Streamline Information for History Matching Channelized Reservoirs
Authors: Wilson Adam 
Published: February 2016

This article, written by Special Publications Editor Adam Wilson, contains highlights of paper SPE 170636, “Integration of Principal-Component Analysis and Streamline Information for the History Matching of Channelized Reservoirs,” by C. Chen, SPE, Shell International Exploration and Production; G. Gao, SPE, Shell Global Solutions US; J. Honorio, Massachusetts Institute of Technology; P. Gelderblom, SPE, Shell Global Solutions International; E. Jimenez, Qatar Shell GTL; and T. Jaakkola, Massachusetts Institute of Technology, prepared for the 2014 SPE Annual Technical Conference and Exhibition, Amsterdam, 27–29 October. The paper has not been peer reviewed.

Although principal-component analysis (PCA) has been applied widely to reduce the number of parameters characterizing a reservoir, its disadvantages are well-recognized. A work flow was proposed to integrate cumulative-distribution-function-based PCA (CDF-PCA) and streamline information for assisted history matching on a two-facies channelized reservoir. The CDF-PCA was developed to reconstruct reservoir models by use of only a few hundred principal components. It inherits the advantage of PCA to capture the main features or trends of spatial correlations among properties, and, more importantly, it can properly correct the smoothing effect of PCA.

Introduction

Both object-based and multipointstatistics-based models generate relatively more geologically realistic channel bodies compared with conventional two-point geostatistics-based techniques. However, conditioning such models to production data and correctly sampling the posterior probability distribution are challenging problems. One of the major challenges is that the number of para meters to be tuned during history matching is too large to be handled effectively by available history- matching work flows, especially when the adjoint gradient is unavailable. Another challenge is that the models obtained after history matching generally violate or distort the geological and geostatistical characteristics of the original or prior models.

One of the authors’ major objectives was to reconstruct channelized geological and reservoir models with much fewer uncertain parameters so that the models can be conditioned to production data through automatic/assisted history matching with use of model-based derivative-free optimization algorithms. The geological and reservoir models obtained by history matching have to capture major flow dynamics and are also constrained by the histogram of lithology distribution and the correlations between reservoir properties of the original model. The following four important questions must be addressed when regenerating geological realizations using new parameters:

Are new parameters able to regenerate both static models and dynamic models?

Are the models regenerated by new parameters still geologically realistic and relevant? In other words, do the new geological and reservoir models satisfy geologists’ requirements?

Are the realizations generated from sampling the probability distribution of these new uncertain parameters equivalent to (but not biased from) the realizations generated from sampling the prior probability distribution?

Is it feasible to history match production data by tuning these new parameters?