Addressing Logging Speed Influence on LWD NMR Response: Advanced Correction Methods for Precise Porosity Evaluation

Logging-While-Drilling (LWD) Nuclear Magnetic Resonance (NMR) tools provide critical reservoir porosity evaluations, with their accuracy influenced by the tool’s Rate of Penetration (ROP) under certain conditions. When slowly relaxing pore-fluid components (spins) contribute significantly to the total porosity, the axial motion of the tool can lead to over-polarization, resulting in porosity overcall and shifted relaxation peak values. These effects compromise measurement accuracy, often leading to the exclusion of LWD NMR tools from logging programs, particularly in high-cost drilling operations where slow ROPs are impractical. This paper presents a physics-based modeling approach and a correction method to address ROP-induced inaccuracies in LWD NMR measurements. The methodology involves a detailed understanding of the physics governing the behavior of moving LWD NMR tools. Accurate modeling is employed to determine the impact of ROP on the measurement and develop a correction protocol to minimize its effects. This approach was applied to assess the impact of ROP on a job in Brazil’s pre-salt fields, determining the logging speed at which NMR data quality is affected and enabling effective job planning. Despite the fact that ROP in pre-salt carbonates rarely reaches the limit where it impacts NMR measurements, the operator frequently requires relogging at higher speeds using the LWD NMR tool in wiping mode. Given the high offshore drilling costs in Brazil, a procedure was developed to optimize this relogging process by increasing ROP while ensuring data quality. This optimization leverages the correction methodology discussed in this paper. The results demonstrate a significant improvement in LWD NMR accuracy, even when logging at high ROP conditions. The proposed correction method effectively mitigates ROP-related discrepancies, enhancing the practical application of LWD NMR tools in challenging drilling environments. This advancement supports improved reservoir characterization and more informed decision-making in hydrocarbon field development.