Optimization of ERW Well Drilling In Real-time: Integrating Geosteering Techniques and Drilling Parameters for Reduced Geological Uncertainty

Abstract During the drilling of extended reach wells (EWR), understanding the stratigraphic position and apparent dip of the local geological structure is crucial for decision-making. It poses a significant challenge due to the variability of local dip angles along the wellbore, hindering correlation with neighboring wells and the determination of optimal drilling parameters. The proposed methodology aims to optimize EWR drilling by reducing uncertainty in the knowledge of the stratigraphic position and automating the analysis of drilling parameters. Using geosteering and drilling software technologies supported by machine learning, real-time parameter comparisons from a geological perspective were conducted, allowing the identification of the expected main fault in the geological model and estimation of apparent dips from conventional gamma ray and resistivity logs. This provided a detailed understanding of vertical layer displacement post-fault in the subsided block, along with obtaining true vertical thickness (TVT) logs to define geological boundaries and control stratigraphic position correlatable with neighboring well drilling parameters. This facilitated forward projections, determined casing setting depths, enabled effective entry into the main target, and recommended parameters for optimal performance, thus expediting real-time decision-making. Precise setting adjustments were achieved, continually optimizing the drilling process by varying parameters informed through the integration of both technologies. The combination of model-based geosteering techniques and stratigraphy, along with the integration of drilling parameters, has significantly reduced geological uncertainty in drilling highly deviated wells. This methodology has demonstrated notable improvements in operational decision-making. This unique combination could offer an innovative and practical perspective for practicing engineers, providing them with more precise and effective approaches for real-time decision-making and continuous drilling process optimization.