Identification of tar mats in carbonate reservoirs using drilling parameters, mud logging analysis, and wireline log data

This study presents a comprehensive, multi-disciplinary approach for identifying tar mat zones in carbonate formations by using an X-oil field as a test case. The aim is to provide insights and enhance tar mat prediction accuracy in order to minimize drilling risks. In this work we propose the integration of four primary methods as a framework for identifying tar mats while drilling. The usually available well data of resistivity logs, real-time mud logging, and drilling parameters from drilling operations were evaluated and compared against visual verifications of core and cuttings analysis. Tar mats were visually confirmed in wells X-H, X-I, and X-G, exhibiting distinct characteristics such as dark staining and viscous textures with the tear-stained zones distinguishing oil- or water-filled zones. Resistivity logs identified reduced permeability zones, evident from the limited separation between Rxo and Rt curves indicating reduced mud filtrate invasion due to reduced permeability pin-pointed near the oil-water contact. This finding was further confirmed by real-time gas monitoring with elevated Hydrocarbon Wetness (Wh) and decreased Balance (Bh) ratios in tar-prone intervals, indicating high concentrations of heavy hydrocarbons and minimal light fractions. Drilling parameters further corroborated these findings, with reduced rate of penetration, increased weight on bit, and oscillating standpipe pressure fluctuations observed while bypassing tar mat sections provide additional diagnostic indicators. After identification of the tar mat zones with the proposed framework, we showcase that they vary in thickness between 3.1 and 9.1 m. By integrating these methods, the study provides a robust framework for identifying tar mat zones, optimizing well placement, and mitigating operational challenges. This novel approach supports cost-effective hydrocarbon extraction and improved reservoir management strategies.