Reservoir parameter estimation constrained to pressure transients, performance history and distributed saturation data

This work deals with the problem of estimating the distributions of permeability and porosity in a petroleum reservoir by matching the dynamic behavior. The dynamic data is in the form of field measurements from well testing, production history, interpreted 4-D seismic information, and other data such as correlations between permeability and porosity, geostatistics in the form of a variogram model and the inference of large scale geological structure.; The issue was posed as an inverse problem and solved by using nonlinear parameter estimation. The procedure developed here is capable of processing all the information simultaneously and this results in a fast and efficient method. The procedure is also able to determine the uncertainty associated with the estimated permeability and porosity fields.; The behavior of the reservoir was modeled with a finite difference numerical simulator because of the requirement of a mathematical model that is sufficiently complex to accommodate all the types of the dynamic data we used. This allowed us to apply the approach to heterogeneous reservoirs, multiphase flow and multiple well problems.; The key issue in the procedure is in the efficient computation of the derivatives of the field observations with respect to parameters that define the distributions of permeability and porosity in the reservoir. The algorithms developed here to compute these derivatives, referred to as sensitivity coefficients, were found to be extremely fast, and were generalized to a wide variety of different parameter types. Examples of different parameter types that may be estimated by this approach include: (a) individual block permeabilities and porosities; (b) geological objects such as channels and faults; (c) pilot points that form the basis of a kriged distribution; and (d) seismic attenuation values from 3-D seismic images.; An important conclusion of this work is that the value of each piece of information does not reside in its isolated use but in the value it adds to integrated analysis of the complete set of information. Thus data that traditionally was considered to be of low information content for reservoir characterization becomes useful and enhances the value of the data set as a whole.