Enhanced Geosteering: Unveiling Cutting-Edge Fluid Identification in North Sea Drilling
Authors: Ungar F. , Bravo M. C. , Odeh P. R. , Iversen M. , Yang T. 
Published: February 2025

In real-time geosteering applications, rapid data-informed decision-making is essential. Ultra-deep azimuthal resistivity (UDAR) excels at differentiating between hydrocarbons and water but exhibits reduced efficacy in distinguishing gas from oil. In a recent Norwegian offshore drilling campaign, integration of UDAR with Real-Time Fluid Identification (RFID) yielded data that facilitated trajectory adjustments and well placement optimization. This underscores the prospective advantages of integrated real-time data analysis for navigating intricate reservoirs. Recent studies highlight the efficacy of employing machine learning to forecast the gas-oil ratio (GOR) using Advanced Mud Gas (AMG) data, unveiling hydrocarbon properties in varied reservoirs (Yang et al., 2019, 2020; Arief et al., 2020; Ungar et al., 2023). Originally applied in exploration, this methodology is now extending to production drilling and real-time geosteering.

For real-time GOR predictions, two approaches are adopted: a real-time fluid prediction interface, integrated with Equinor drilling software platform, and RFID support team's analysis using a sophisticated plugin in Geolog for enhanced quality control. Analysis across four wells underlines the RFID team's role in managing complex fluid scenarios, including variable segment compositions, gas-trapped depleted oils, aquifer-influenced reservoir fluid shifts, and gas-oil transitional zones. Two predictions were performed: one utilized the Norwegian Continental Shelf (NCS) database, and another used only the local area database. The latter prediction consistently yields richer reservoir fluids than the prediction based on the entire NCS dataset. However, it should be noted that the local area dataset is biased towards oil-prone samples and requires careful consideration.

Overall, satisfactory results were obtained regarding oil and gas identification using the full NCS database. The field experience gained from the first-time implementation of RFID to geosteering underscores the significance of fluid identification derived from mud gas data as an essential addition to the existing tools. In the context of GOR and fluid density prediction from the RFID software, incorporating gas component ratios is valuable as it can validate the prediction (Haworth et al., 1984; Pixler, 1969). In all four wells, the gas component ratios support the prediction, as the observed change in fluid phase aligns with the depth intervals predicted by the machine learning (ML) model.

This endeavour was unique in its scale and the real-time demands it placed on the team. By collaborating closely with the well planning team, geo-operation center, and service provider, the RFID support team played a pivotal role in guiding well placement and subsequent completion, ultimately optimizing oil production. Following its successful implementation across four consecutive wells, we have established best practices for integrating UDAR and RFID technologies to support steering effectively in real time.