RANKING OF GEOSTATISTICAL RESERVOIR MODELS AND UNCERTAINTY ASSESSMENT USING REAL TIME PRESSURE DATA

A novel approach to analyze the quasi continuous pressure data for ranking high resolution geostatistical reservoir models and uncertainty assessment has been presented. Real time monitoring of pressures through permanent down hole gauges is a recent development. A robust procedure to use the enormous amount of data recorded by theses monitoring systems has been proposed and tested on a synthetic case.

Geostatistical simulations involve generating multiple equi probable fine scale depictions of the reservoir heterogeneity each honoring the data available. The simulated pressure responses from these realizations could be quite different, yet the responses are not completely random. In spite of their differences, there are patterns, which occur in theses simulated responses. Such patterns could be extracted out by means of a mathematical tool called Principal Component Analysis.

The classical face recognition technique is then used to rank the geostatistical reservoir models to identify bounding cases. Appropriate quantiles from the ranked models are then used for the assessment of uncertainty in the flow performance. The simulated pressure data from the multiple realizations is analogous to B_training set of facesB_ while the recorded or the historical data is the B_faceB_, which needs to be recognized form the training set. The method attempts to identify the geostatistical reservoir models, which show reasonable match in the dominating patterns in the simulated pressure data with the recorded pressure data. This approach mimics the B_face recognitionB_ or the B_voice recognitionB_ technique, which are already being successfully applied in their respective domains. This methodology could only be applied for the purpose due to the availability of the large amount of pressure data recorded by the down hole pressure recorders.