Porosity and Permeability Estimation by Gradient-Based History Matching Using Time-Lapse Seismic Data

摘要

A method based on the Gauss-Newton optimization technique for continuous model updating with respect to 4D seismic data is presented. The study uses a commercial finite difference black oil reservoir simulator and a standard rock physics model to predict seismic amplitudes as a function of porosity and permeabilities. The main objective of the study is to test the feasibility of using 4D seismic data as input to reservoir parameter estimation problems. The algorithm written for this study, which was initially developed for the estimation of saturation and pressure changes from time-lapse seismic data, consists of three parts: the reservoir simulator, the rock physics petro-elastic model, and the optimization algorithm. The time-lapse seismic data are used for observation purposes. In our example, a simulation model generated the seismic data, then the model was modified after this the algorithm was used to fit the data generated in the previous step. History matching of reservoir behavior is difficult because of the problem is not unique. More than one solution exists that matches the available data. Therefore, empirical knowledge about rock types from laboratory measurements are used to constraint the inversion process. The Gauss-Newton inversion reduces the misfit between observed and calculated time-lapse seismic amplitudes. With this method, it is possible to estimate porosity and permeability distributions from time-lapse data. Since these parameters are estimated for every single grid cell in the reservoir model, the number of model parameters is high, and therefore the problem will be underdetermined. Therefore, a good fit with the observation data is not necessary for a good estimation of the unknown reservoir properties. The methods for reducing the number of unknown parameters and the associated uncertainties is discussed.