Prediction of Oil Production with Confidence Intervals

Monday, January 7, 2002 - 2:00pm - 3:00pm
Keller 3-180
James Glimm (University at Albany (SUNY))
We present a prediction methodology for reservoir oil poduction rates which assesses uncertainty and yields confidence intervals associated with its prediction. The methodology combines new developments in the traditional areas of upscaling and history matching with a new theory for numerical solution errors and with Bayesian inference. We present recent results of coworkers and of ourselves.

A remarkable development in upscaling allows reduction in computational work by factors of more than 10,000 compared to simulations using detailed geological models, while preserving good fidelity to the oil cut curves. We formulate history matching probabilistically to allow quantitative estimates of prediction uncertainty. A probability model is constructed for numerical solution errors. This error analysis establishes the accuracy of fit to be demanded by the history match. It defines a Bayesian posterior probability for the unknown geology.

The error model is both simple and robust. It is simple in that it can be described by a small number of readily understood parameters, and it is robust in the sense that these parameters have been shown to be independent of the geology correlation length, in a simulation study based on 500 fine and coarse grid simulations. The error is roughly proportional to the mesh size or the upscaling ratio of the coarse to fine grids.

The significance of our methods is their ability to predict the risk, or uncertainty associated with production rate forecasts, and not just the production rates themselves. The latter feature of this method, which is not standard, is very useful for evaluation of decision alternatives.