https://ifp.hal.science/hal-02078968Blanc, G.G.BlancIFPEN - IFP Energies nouvelles - IFPEN - IFP Energies nouvellesHu, L. Y.L. Y.HuIFPEN - IFP Energies nouvelles - IFPEN - IFP Energies nouvellesNoetinger, B.B.NoetingerIFPEN - IFP Energies nouvelles - IFPEN - IFP Energies nouvellesEstimation of Lithofacies Proportions Using Well and Well Test DataHAL CCSD1998[PHYS] Physics [physics]Sciences, EDP2019-03-25 17:05:392019-03-28 04:17:412019-03-27 09:03:09enJournal articleshttps://ifp.hal.science/hal-02078968/document10.2516/ogst:1998014application/pdf1A crucial step of the two commonly used geostatistical methods for modeling heterogeneous reservoirs : the sequential indicator simulation and the truncated Gaussian simulation is the estimation of the lithofacies local proportion (or probability density) functions. Well-test derived permeabilities show good correlation with lithofacies proportions around wells. Integrating well and well-test data in estimating lithofacies proportions could permit the building of more realistic models of reservoir heterogeneity. However this integration is difficult because of the different natures and measurement scales of these two types of data. This paper presents a two step approach to integrating well and well-test data into heterogeneous reservoir modeling. First lithofacies proportions in well-test investigation areas are estimated using a new kriging algorithm called KISCA. KISCA consists in kriging jointly 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. For multiple well-tests, an iterative process is used in KISCA to account for their interaction. After this, the estimated proportions are combined with lithofacies indicators at wells for estimating proportion (or probability density) functions over the entire reservoir field using a classical kriging method. Some numerical examples were considered to 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 experimental and estimated proportions in the well-test investigation area demonstrates the validity of the proposed method.