Permeability Prediction in Uncored Wells Using an Advanced Statistical Technique
Novikri, Irvan *1
(1) Pore Volume Assessment Division, Exploration Resource Assessment Department, ARAMCO, Dhahran, Saudi Arabia.
Predicting permeability in uncored wells based on core data is very important in building 3D geological models for hydrocarbon in-place assessment and flow simulation. Traditionally permeability has been computed from porosity-permeability equations, which are derived empirically from routine core analysis data. Recently, advanced statistical techniques have become popular in which permeability models can be built in wells with sufficient core coverage using routine core analysis and wireline log data. Models built using an advanced statistical technique called the k-nearest neighbor clustering or Multi-Resolution Graph-based Clustering is presented in this paper. The model is then used to predict permeability in nearby uncored wells across the same reservoir. Routine core analysis and wireline log data from a carbonate reservoir are used to demonstrate this technique. The model is successfully applied to predict permeability in control wells (wells with core data but not used in building the model) and uncored wells. This paper will describe the methodlogy and show the permeability prediction result.
AAPG Search and Discovery Article #90141©2012, GEO-2012, 10th Middle East Geosciences Conference and Exhibition, 4-7 March 2012, Manama, Bahrain