Pilot Block Method Methodology to Calibrate Stochastic Permeability Fields to Dynamic Data |
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Authors: | Mickaele Le Ravalec-Dupin |
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Institution: | 1.IFP,Rueil-Malmaison Cedex,France |
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Abstract: | In the present paper, a new geostatistical parameterization technique is introduced for solving inverse problems, either in
groundwater hydrology or petroleum engineering. The purpose of this is to characterize permeability at the field scale from
the available dynamic data, that is, data depending on fluid displacements. Thus, a permeability model is built, which yields
numerical flow answers similar to the data collected. This problem is often defined as an objective function to be minimized.
We are especially focused on the possibility to locally change the permeability model, so as to further reduce the objective
function. This concern is of interest when dealing with 4D-seismic data. The calibration phase consists of selecting sub-domains
or pilot blocks and of varying their log-permeability averages. The permeability model is then constrained to these fictitious
block-data through simple cokriging. In addition, we estimate the prior probability density function relative to the pilot
block values and incorporate this prior information into the objective function. Therefore, variations in block values are
governed by the optimizer while accounting for nearby point and block-data. Pilot block based optimizations provide permeability
models respecting point-data at their locations, spatial variability models inferred from point-data and dynamic data in a
least squares sense. A synthetic example is presented to demonstrate the applicability of the proposed matching methodology. |
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Keywords: | |
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