Chapter 5. Technology to Manage Large Digital Datasets

Development of the Hugoton field model has required automated processing of large data volumes at several steps, including prediction of lithofacies from geophysical well logs in numerous wells based on a neural network trained on log-facies associations observed in cored wells, generation of geologic controlling variables (depositional environment indicator and relative position in cycle) from a tops dataset, and computation of porosities corrected for mineralogical variations between facies and for washouts. In addition, we have developed code for batch processing the predicted facies and corrected porosities at the wells to estimate water saturations and original gas in place using petrophysical transforms and height above free-water level, providing a quickly computed measure of the plausibility of the geomodel. This chapter describes the data management and faciesprediction tools developed for this project, which we have provided in the form of two Excel workbooks and an Excel add-in. Having automation tools allowed us to efficiently handle large volumes of data and the ability to perform the multiple iterations required to test preliminary and intermediate algorithms and solutions.