THE SAND-SILT-CLAY (SSC) PETROPHYSICAL MODEL CHARACTERIZATION: AN ADVANCED PETROPHYSICAL ANALYSIS AND ITS ESSENTIAL APPLICATIONS TO LITHOLOGY COMPUTATION, PERMEABILITY ESTIMATION AND SATURATION MODELLING

Abstract

The Sand-Silt-Clay petrophysical model is developed to solve the problems encountered in analysing most reservoirs in the Malay Basin. This type of reservoir generally consists of fine to very fine grained sediments (silt) with low formation water salinity. In this model, the three main groups of particle sizes of lithological components; Sand, silt and clay sized particles are captured, based on the most of core data acquired in the Malay Basin. The model has been developed from the density-neutron cross-plot to determine the lithology fractions and the porosity of the rocks. The cross plot of density-neutron data which is plotted through water-bearing sandstone, siltstone, and clay stone section in Malay Basin shows a shape like a “boomerang”. In general, the data points that sit on the left limb of this boomerang are characterized as reservoirs and the ones sit on the right limb as non-reservoirs. A line called “silt line” is drawn separating these two groups. The density-neutron cross plot is a fundamental tool which is critical to the interpretation of shaly sands. The density-neutron cross-plot is used to determine the lithology fractions and the porosity of the rocks. In the past, the technique of selecting a clay point from the cross plot has not had a firm scientific foundation. The purpose of this paper is to establish a robust procedure for petrophysical interpretation using Density-Neutron cross plot. Three sets of logs acquired in different reservoirs (gas, oil and heavy oil) and in very different geological environments were used to test the SSC. The reservoirs were selected for geographical diversity and geological variability to demonstrate the robustness of the method. The input logs were density, neutron, resistivity, gamma-ray and caliper. The data from some core’s Sieve Analyses (and XRD), and combined with data from some known good log interpretation results have been used as references to calibrate the model.

AAPG Datapages/Search and Discovery Article #90354 © 2019 AAPG Asia Pacific Technical Symposium, The Art of Hydrocarbon Prediction: Managing Uncertainties, Bogor, (Greater Jakarta), Indonesia, August 7-8, 2019