Improved Reservoir Permeability Models From Flow Units and Soft Computing Techniques: A Case Study, Suria and Reforma-Libertad Fields, Colombia

Abstract

Abstract This paper presents a methodology that looks to solve the inverse problem of predicting reservoir properties on uncored intervals/wells, using soft computing techniques (neural networks and fuzzy logic), multivariate statistical analysis and hydraulic flow unit concepts. Our methodology to improve the prediction of permeability in Suria and Reforma-Libertad fields in Colombia is the following: Data quality control. We apply multivariate statistical analysis for quality control of core and log data: 95% confidence ellipses and Q-Q plots are used for that purpose.Rock type identification. We use poral geometry analysis to identify rock types in cored wells. Then, fuzzy logic, core and log variables are used to develop a rock type model to be used in solving the inverse problem, predict the rock type in uncored intervals/wells.Hydraulic flow unit classification. For that purpose, we use the technique based on a modified Kozeny-Carmen equation to calculate the reservoir quality index, RQI=0.0314(K/φ) ½, flow zone indicator, FZI=RQI/(φ/(1-φ)) and φz=φ/(1-φ). The basic idea of hydraulic flow unit (HFU) classification is to identify classes that form unit-slope straight lines on a log-log plot of RQI vs. φz with similar but not identical FZI value. Each class or hydraulic flow unit has a mean FZI value at the intercept with φz=1, and a maximum and a minimum FZI values. We use log data and the fuzzy logic rock type variable to develop a neural network FZI model to be used in solving the inverse problem, predict FZI in uncored intervals/wells. The HFU for each uncored interval is determined with its FZI value that falls on a range between maximum and minimum values of FZI. Finally, permeability is calculated knowing its porosity and FZI values. In the literature, the HFU is first determined by Bayesian inference assigning a probability distribution of log values to each HFU and identifying to which population the given set of log readings most likely belong. Then, permeability is estimated from porosity and mean FZI values ignoring the scatter data for each HFU. Permeability estimations from our approach are compared from other conventional techniques to demonstrate that this is a better way to get confident permeability models and to show how to used soft computing techniques to improve reservoir description.