This work discusses the development and implementation of a procedure to condition a stochastic channel to well- test pressure data and well observations of the channel thickness and the depth of the top of the channel. The stochastic channel is defined by a set of geometric random variables (referred to as geometric model parameters) that describe the location, size and shape. Channel and non- channel permeability and porosity are treated as random variables. These four random variables plus the geomet- ric parameters comprise the complete set of model param- eters. Multiple conditional realizations of the geometric parameters and rock properties are generated to evaluate the uncertainty in model parameters and the reduction in uncertainty obtained by conditioning to well-test pressure data.

Keywords:
artificial intelligence, levenberg-marquardt algorithm, gridblock, reservoir characterization, sensitivity coefficient, objective function, conditional realization, permeability, model parameter, conditioning
Subjects:
Well & Reservoir Surveillance and Monitoring, Reservoir Characterization, Reservoir Fluid Dynamics, Reservoir Simulation, Formation Evaluation & Management, Information Management and Systems, Geologic modeling, Pressure transient analysis, Drillstem/well testing
Copyright 1999, Society of Petroleum Engineers
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