Abstract

Determining water saturations in thin-bedded turbidites using wire-line logs is difficult; errors in Sw calculation frequently result in uneconomical completions. Consequently, current Brushy Canyon completion decisions employ expensive core information to provide an acceptable indicator of oil saturation in order to compensate for the Sw calculation problem. Completion decisions can be improved and less core data is needed using a new method that correlates wire-line logs with core measured bulk volume oil (MS°).

A neural network was trained and tested using density and neutron porosity plus shallow and deep resistivity logs as input variables. The neural network was trained to predict the MS° product from whole core analysis.

The trained and tested neural network was then used to estimate MS° in 34 additional Brushy Canyon wells that were not used in the training, but had the same four wire-line logs. The summed bulk volume oil of the wells was plotted versus the first year’s total production. The plot suggests that EMS° greater than 20,000 units will usually result in an economical new well or reentry completion. Statistics from the MS° log improved the production correlation.

During the course of optimizing the neural network architecture, valuable insights into network architecture design were gained. For this type of study, less complex architectures produced robust testing results, indicating that the solution, though non-linear, is still reasonably simple.

The method should be useful when evaluating behind-pipe completion opportunities in the Brushy Canyon interval of the Delaware Sands in the Permian Basin. Re-completion costs are lower than new well costs; thus thin zones with high values of MS° are potential targets.

This work was presented at the SPE 2001 Permian Basin Oil & Gas Recovery Conference as SPE No. 70041. Permission to present this copyrighted material was granted by the SPE.