TUPREP is a cooperative industry/university research project organized at the end of 1988 to conduct research and development in the areas of welltesting, reservoir characterization and reservoir simulation. TUPREP formally began operation on January 1989 with membership commitments from four companies.

The 2009 TUPREP board meeting was held on Friday, May 8, 2009. The next meeting will be held in May 2010 and the information and registration forms for the meeting will be sent to members in April 2010.

Current TUPREP projects include the following:

• Ensemble Kalman Filter (EnKF): Although the standard theoretical underpinnings of EnKF rest on Bayesian updating with Gaussian priors, we show that the EnKF update equations can also be derived as an approximation to the Gauss-Newton method which uses an “average” sensitivity matrix. This suggests that for highly nonlinear, non-Gaussian problems, EnKF may not provide an appropriate characterization of uncertainty and that some form of iteration is required. By viewing EnKF through the lens of optimization, instead of Monte Carlo sampling, we derive iterative EnKF procedures for nonlinear, non-Gaussian problems.

• Applications of the Expectation Maximization Algorithm: The characterization of errors in measured data is important if one wishes to condition reservoir models to diverse data sets (e.g., production and seismic) because measurement/processing errors determine the proper relative weights of the data. In the literature, the measurement error for each data type is often estimated by some smoothing technique in the whole data domain, which often over-smoothes the data (particularly around points where the underlying true data changes sharply) and results in over estimation of the measurement error. We introduce a new procedure for ”measurement” error estimation. The method is based on a modified EM (Expectation-Maximization) algorithm combined with a moving polynomial fit and provides an estimate of the mean and covariance of errors in observed data. The procedure avoids smoothing over discontinuities.

• Injection/Falloff/Production Test: We introduced the concept of using an injection production falloff test for the in situ estimation of relative permeabilities. The test consists of three periods, (i) injection of water into an oil reservoir, (ii) a falloff test and (iii) a producing period. The producing period is critical as it yields production data that reflects changes in sandface mobility and is thus highly sensitive to the parameters used to model relative permeability curves.

• Injection/Falloff Test: We pursue analytical solutions for the pressure response during injection and falloff for horizontal and restricted-entry vertical wells considering the temperature effect.

• Production Optimization: By adjusting the controls of smart wells, we maximize the Net Present Value with geological uncertainty. We just started this project.

Numerous technical papers have been written based on TUPREP  research. Thirty PhD Dissertations and twelve Masters Theses have been completed under the auspices of TUPREP.

Last updated: August 19, 2009