A New Facies Classification Scheme Using Gamma Ray and Bulk Density Logs, With Multiple Practical Applications in North Kuwait Heavy Oil Fields

Abstract

This paper presents a method for facies classification derived from cross plots of basic gamma ray and bulk density wireline log data. It has been specifically developed in-house for two North Kuwait heavy-oil fields, and has been calibrated against both field analogues and core sample measurements. This new facies classification scheme has proven to be quick and cost effective, with multiple practical applications for future field development and operation optimization. For two heavy oil fields in North Kuwait basic Gamma Ray and Bulk Density (GR-DENS) curve data from over 1300 wells were cross-plotted. The resulting relationship characteristics were used to delineate eight separate facies, which plot along a continuum from clean porous sands with little cement and clay, to less porous sands with increasing clay and cementation content, to carbonate and shale. The properties for these facies were calibrated against data from core analyses and with outcrop analogues in North Kuwait. These facies were populated into static reservoir models using the Sequential Indicator Simulation (SIS) method, and petrophysical modeling was then conditioned to these facies. These resulting modeled facies, with their associated petrophysical properties, have been used in a wide variety of subsequent analytical studies. The eight facies which have been newly delineated by the GR-DENS classification scheme capture the transitional nature of petrophysical properties for oil saturation, porosity and permeability. This has enabled several improvements for heavy-oil field development including: 1) better delineation of reservoir and baffle zones; 2) better calibration of oil saturation with core data; 3) calibration of facies with 3D seismic amplitude response; 4) better understanding of reservoir geomechanics and seal integrity assessment; 5) greater confidence in the results of static and dynamic reservoir modeling; 6) more effective decision making in the WRFM process; and 7) alignment of the petrophysical and facies characterization approach between two separate heavy oil asset teams, which allows for direct comparisons between their data sets. Although more complex software exists for specialized facies classification, the GR-DENS workflow newly developed for North Kuwait heavy oil has proven to be simple, rapid, accurate and cost effective. In summary a robust facies classification scheme was developed in-house which is appropriately customized for two North Kuwait heavy oil fields. This methodology has enabled the creation of more representative reservoir models, with resulting improvements in understanding for multiple aspects of both fields. These improvements in turn will lead to better production forecasting and optimization as well as enhance future life of field planning.