Calibrating Multi-Point Geostatistical Models Using Pressure Transient Data

Abstract

Transient well test data conveys significant information about the subsurface heterogeneities in terms of some variations in the well test pressure response curves. It is therefore important to enhance the use of the well test data for building a validated geological model to include the effective reservoir heterogeneities that are reflected on the well test plots. In this work, we present a novel geoengineering workflow for geologically consistent updating of the geostatistical facies models using pressure transient data.

We use Multi-Point Statistical (or Geostatistical) simulations (MPS) with conditioning hard and soft data to generate the geostatistical realizations that can preserve the spatial connectivity of the facies. Static model transient tests are then generated using high resolution numerical simulations. The results are compared with the measured well test data for an inversion. The inversion step involves a geologically consistent Probability Perturbation Method (PPM) for perturbing the geostatistical models which are combined with a Gaussian Process (GP) modeling approach for finding the optimum spatial distribution of the facies and the other unknown model parameters. Conditional two-dimensional models of a low-energy anastomosing channelized model are considered in this study. The results show that using such an approach the spatial variation of the facies is maintained and the transitions across the facies boundaries are consistently preserved. In this paper, the geostatistical models are updated simultaneously with other unknown model parameters, including the PPM’s parameter (r), facies permeabilities and the non-Darcy D-factor. This multidimensional inversion is efficiently performed by GP in less than 100 compositional simulations. The novelty of this work is to efficiently use the well test data for updating the static models in a fluvial reservoir using a perturbation of the geological models. Moreover, for the first time, a novel optimization method is combined with PPM to consistently update the model parameters with a limited reservoir simulation budget.