Characterizing Movable and Non-Movable Zones in a Mature Carbonate Reservoir: A Novel Workflow Using Resistivity Logs

Abstract

Optimizing reservoir recovery depends on an in-depth understanding of natural geological complexity to predict reservoir behavior. Understanding the difference between producible oil and non-movable oil zones is important, which will aid in the refinement of the design of future wells. During the mature life cycle of the Maastrichtian carbonate reservoir, it was observed that some wells would not perform optimally, while others would experience a significant drop in production. By analyzing petrophysical and production data, the reservoir was found to contain hydrocarbons consisting primarily of heavy oil and stringers of light oil. Based on reservoir characterization and after assessing the production profile to understand the hydrocarbon behavior, this study was performed to identify and distinguish movable oil zones from non-movable oil zones. Conventionally, expensive intervention methods, such as running modular dynamics formation tester (MDT), nuclear magnetic resonance (NMR) logs, and production logging tools (PLT), are used to determine the oil viscosity (API) and identify contribution zones from the entire hydrocarbon interval. However, using these methods results in increased operational costs and reduced production. This study proposes an alternative approach using resistivity logs to identify and distinguish between movable and non-movable hydrocarbon zones to improve reservoir management. The concept behind this method depends on the resistivity logs validated using MDT and PLT data. A shallow resistivity reading higher than a deep resistivity reading indicates that hydrocarbons were not flushed (unmoved) by invasion. Thus, the zone contains unproducible hydrocarbon reserves. The resistivity cut-off value was estimated based on the PLT and MDT data to identify movable oil intervals. In all the wells analyzed, there was a good correlation among the calculated zone thickness, core data, sampling data, and mud logs. Dielectric logs were run in a couple of key wells, which enabled the Sxo estimation independent of resistivity. Additionally, the Sxo obtained supports the fluid interpretation. Productive zones were accurately identified for each well, and recompletions were made to produce from these bypassed opportunities. The proposed method is robust with respect to environmental corrections, not contingent on MDT, NMR, and PLT knowledge, and can be carried out without halting production.