| Popis: |
Micro and macroscopic-anisotropic reservoir characterization has, recently, been performed using two main approaches:applying the Thomas-Stieber technique on uniaxial resistivity orapplying the tensor model technique using multicomponent induction measurements. The output from either technique is the sand resistivity, RSand, and sand volume, VSand, with accurate water saturation, Sw, as a result of overcoming the effect of laminated shale on the resistivity measurement, which eventually will translate into enhanced hydrocarbon recovery and optimized reservoir development. Some of the key challenges in both techniques are the proper understanding of the total porosity and volume and the distribution of shale in the reservoir in terms of laminated and dispersed forms, which, if in error, will drastically increase the uncertainty of processing results. Additionally, the dependence of fluid density on the saturation and the dependence of the saturation and cementation exponents, m and n, on the total porosity, as well as the silty nature of the sand, all contribute to the complexity. Oil mud resistivity imager and nuclear magnetic resonance (NMR) logs, as well as elemental analysis (if available), were used to confirm the lamination nature of the shale in the reservoir, minimizing uncertainty on total porosity estimation and clay bound water (CBW) calculation. They were also used to help account for the presence of silt-sized sand. This paper provides a complete rock model and a comprehensive workflow that takes into account all of the necessary stages required to estimate water saturation in a thinly bedded sand-shale sequence, where both laminated and dispersed shale types can exist. Also, the paper presents a simple sensitivity analysis performed to understand the uncertainty on the processing parameters and estimated volumetrics on the results when using different techniques compared to the proposed technique in this study. Data used in this paper are from a well drilled in the Malay basin, offshore Malaysia. |
