ESTIMATION OF LITHOFACIES PROPORTIONS USING WELL AND WELL TEST DATA

A crucial step of the two commonly used geostatistical methods for mod eling heterogeneous reservoirs: the sequential indicator simulation an d the truncated Gaussian simulation is the estimation of the lithofaci es local proportion (or probability density) functions. Well-test deri ved permeabilities show good correlation with lithofacies proportions around wells. Integrating well and well-test data in estimating lithof acies proportions could permit the building of more realistic models o f reservoir heterogeneity. However this integration is difficult becau se of the different natures and measurement scales of these two types of data. This paper presents a two step approach to integrating well a nd well-test data into heterogeneous reservoir modeling. First lithofa cies proportions in well-lest investigation areas are estimated using a new kriging algorithm called KISCA. KISCA consists in kriging jointl y the proportions of all lithofacies in a well-test investigation area so that the corresponding well-test derived permeability is respected through a weighted power averaging of lithofacies permeabilities. Far multiple well-tests, an iterative process is used in KISCA to account for their interaction. After this, the estimated proportions are comb ined with lithofacies indicators at wells for estimating proportion (o r probability density) functions over the entire reservoir field using a classical kriging method. Some numerical examples were considered t o test the proposed method for estimating lithofacies proportions. In addition, a synthetic lithofacies reservoir model was generated and a well-test simulation was performed. The comparison between the experim ental and estimated proportions in the well-test investigation area de monstrates the validity of the proposed method.