Abstract Maximizing reservoir exposure in a low-resistivity pay carbonate reservoir requires accurately targeting a thin zone characterized by very low resistivity, high porosity, and moderate permeability, while addressing structural uncertainties associated with sub-seismic faults. The low resistivity results from a micritized matrix with micropores, contributing to high porosity and increased salinity. This zone is highly productive, and precise navigation is essential, as it is bounded by high-permeability streaks that can facilitate rapid water movement if exposed. The log characteristics around the target zone for Gamma Ray, Density, and Porosity are subtle, except for the lowest resistivity in the target zone, which is bounded by a gradually increasing resistivity trend. Identifying the lowest resistivity zone was crucial for defining the optimal target zone during geosteering. Due to the low resistivity contrast between the optimal zone and its surroundings, an advanced mapping tool is necessary to provide clear insight into the environment. An ultra-deep azimuthal resistivity (UDAR) tool was placed near the bit to map the resistivity distribution, followed by a density porosity tool to confirm reservoir properties. In the past, using a triple combo assembly that included a deep azimuthal resistivity (DAR) tool was standard for drilling in this challenging environment. However, this setup had limitations in fully mapping the resistivity distribution necessary to accurately identify the optimal target zone of the reservoir, particularly due to the low resistivity contrast. The higher frequency range and shorter spacing of the DAR tool were unable to fully resolve the optimum target zone in this low resistivity contrast, making it very challenging to achieve adequate reservoir exposure in the optimum zone. The recent implementation of the ultra-deep azimuthal resistivity technology with advanced high-definition inversion has made it possible to not only map the full extent of the optimum zone but also the structural changes related to formation dip and sub-seismic faults around the wellbore with a greater depth of detection. This advancement enables informed, proactive reservoir navigation decisions that maximize reservoir exposure and ensure optimal reservoir management. The results demonstrate the effectiveness of this approach in improving hydrocarbon recovery and reducing operational risks in complex carbonate formations. By integrating insights on structural updates and resistivity distribution, the sub-surface model can be improved to enhance the understanding of the area. This proactive approach reduces uncertainties and supports strategic planning for future wells, opening promising opportunities.
Salahuddin et al. (Mon,) studied this question.