Discrete Inversion Method for Nuclear Magnetic Resonance Data Processing and Its Applications to Fluid Typing and Quantification
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About the Course
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Due to the ill-posed problem of the inversion technique, constraints are applied to obtain stable continuous solutions by the ILT method, which are considered as ground truth but pose challenges for fluid partitioning. The smoothing effects on the continuous solutions are demonstrated by comparison with prior known synthetic datasets, since distributions from actual samples are unknown. We present a comprehensive framework centered around a discrete inversion approach, detailing its successful implementation across various laboratory datasets, its compatibility with ILT, and its enhancement through discrete component optimization. Notably, this methodology exhibits promising results in addressing challenging tasks, including the characterization of shale fluids and the analysis of logging data compromised by low Signal-to-Noise Rations (SNR) conditions
Notable limitations of conventional Inverse Laplace Transform (ILT) methods have been identified, primarily manifesting as: (1) excessive smoothing that leads to inconsistent regularization, resulting in substantial peak overlaps and diminished spectral resolution; and (2) considerable uncertainty in estimating short relaxation times, particularly exacerbated by low Signal-to-Noise Ratio (SNR) conditions. Over-smoothing poses particular challenges to subsequent partitioning methods, as it cannot be distinguished from actual distributions, and any inversion inaccuracies propagate into post-processing. In the discrete inversion approach, the number of components is initially estimated using the Bayesian Information Criterion (BIC), and subsequently, neighboring components can be merged to accurately capture broad distributions, taking into account the sensitivity of the data to noise levels. Notably, the components derived from discrete inversion differ fundamentally from those produced by ILT/partitioning workflows, as they inherently eliminate the need for additional partitioning steps, streamlining the analysis process. In the current study, we demonstrated the fluid classification and quantification become trivial once time ranges of fluids are calibrated for a specific shale reservoir. A further advantage of the proposed method lies in its resilience to noisy well-logging data, characterized by low SNR, where it exhibits significantly reduced uncertainty even when noise levels fluctuate, thereby enhancing the reliability of the results. Currently more applications are being developed based on discrete components and continuous distributions from ILT/Anahess.
Your Instructor
Dr. Hyung Tae Kwak is an accomplished Research Scientist with over two decades of experience in the oil and gas industry. From January 2001 to March 2010, he was a key contributor at Baker Hughes Inc., where he developed critical technologies for wireline NMR logging data interpretation, including Invasion Profile Analysis and permeability prediction integrating diffusion coupling effects. As an NMR laboratory manager, he also invented an NQR lab instrument and a novel NMR probe for capillary condensation in unconventional rocks. Since March 2010, Dr. Kwak has led the Advanced Petrophysics Group at Saudi Aramco EXPEC ARC. He has been instrumental in developing the NAFT platform, a comprehensive technology suite for naturally fractured reservoirs, and the ACME platform, a 4IR-ready tool for sample and data management in upstream operations. His innovative contributions include the EARC HM technology, a cutting-edge history matching tool, and a suite of advanced NMR technologies addressing industry challenges such as temperature correction, reserve estimation, wettability quantification, and improved data processing technology. Dr. Kwak's work has significantly advanced petrophysical research and application, particularly through his development of AI-driven technologies and non-invasive laboratory techniques, making him a leading figure in the field. As of today, he has over 200 publications including peer-reviewed articles and patents.