A Pseudo-Extraction Efficiency Correction Method for Standard Mud Gas Data

Standard Mud Gas (SMG) logging has long been a critical tool for ensuring well safety in the oil and gas industry. However, despite the widespread availability of SMG data across all wells, its full potential for reservoir fluid estimation has remained largely untapped due to the lack of accurate extraction efficiency coefficient (EEC) correction for both measured and estimated values. Albeit Advanced Mud Gas (AMG) data provides the necessary corrections with greater accuracy, only a small fraction of wells are drilled with AMG logging. This thus creates a significant need for reliable EEC estimation methods for both legacy and new wells that rely on SMG logging. In this paper, we build on the "pseudo-EEC" method originally proposed by Yang et al. in 2022, further developing it to provide practical corrections to SMG compositions for enhanced reservoir fluid prediction. We aim to scientifically validate this method and offer deeper insights into its application across water-based mud (WBM) and oil-based mud (OBM) systems. By leveraging our in-house reservoir fluid database, we systematically investigated the influence of various variables on pseudo-EEC, including Equation-of-State (EoS) models, C7+ characterization methods, the number of pseudo-components, additives in drilling mud, degasser temperature, topside pressure, and gas phase ratios. These comprehensive investigations allow us to propose a practical approach for estimating pseudo-EEC. Our findings indicate that the pseudo-EEC is generally insensitive to the choice of the EoS models, C7+ characterization methods, and the number of pseudo- components, with notable exceptions for degasser temperature and gas phase ratios. Additionally, we observed that K-values are nearly independent of reservoir fluid composition. Regarding drilling fluid additives, we studied the effects of free water and glycol in WBM, and water in OBM. We also provided brief explanation regarding the analysis of other additives in both mud systems. The results suggest that pseudo-EEC is largely unaffected by variations in these components across both mud systems. Expanding on the initial study focused on C1-C3 components, we extended our analysis to include C4-C5 components. The approach proposed in this work is applicable to a vast amount of SMG data, regardless of the drilling fluid systems used. The pseudo-EEC method developed in this paper significantly expands the utility of SMG data for fluid property estimations. Given the extensive number of legacy wells with only SMG data and numerous new wells being drilled without AMG data (subject to cost or logistical constraints), the pseudo-EEC method holds substantial potential for providing improved reservoir fluid insights and influencing well decision-making processes.