Evaluating the potential of carbonate sub-facies classification using NMR longitudinal over transverse relaxation time ratio
Abstract view|807|times PDF download|309|times
Abstract
While the well log-based lithology classification has been extensively utilized in reservoir characterization, the classification of carbonate sub-facies remains challenging due to the subtle nuances in conventional well-logs. The nuclear magnetic resonance (NMR) log provides extra information of pore size and pore geometry features, improving differentiating carbonate sub-facies. Here we explore the feasibility of using the ratio between NMR longitudinal relaxation time and transverse relaxation time as a potential lithology indicator to determine carbonate sub-facies. We analyzed a series of logging data and corresponding core samples of Arbuckle Group carbonate containing mudstone, packstone, grainstone, incipient breccia, and breccia in northern Kansas for the characteristics of longitudinal relaxation times, transverse relaxation times, and longitudinal over transverse relaxation time ratios. The results show that mudstone, packstone, and grainstone exhibit high, intermediate, and low longitudinal over transverse relaxation time ratios, respectively, while incipient breccia and breccia have a wide range of longitudinal over transverse relaxation time ratios. Furthermore, we evaluated the potential of using longitudinal over transverse relaxation time ratios to classify carbonate sub-facies using multivariate analysis. By adding longitudinal over transverse relaxation time ratios to neutron porosity, total gamma-ray, and conductivity logs as inputs of automated facies classification, the prediction error decreased, especially for incipient breccia. On the contrary, when photoelectric log and computed gamma-ray are also available, adding longitudinal over transverse relaxation time ratios does not improve the accuracy of sub-facies classification. Our results suggest that longitudinal over transverse relaxation time ratio is an independent lithology indicator. However, it cannot replace other logs like gamma-ray and photoelectric logs in classifying carbonate sub-facies. Our study provided valuable evidence and credible elucidation of the importance and physicochemical mechanism of longitudinal over transverse relaxation time ratios, which is essential for deciphering NMR logging data in carbonate reservoirs.
Cited as: Zhang, F., Zhang, C. Evaluating the potential of carbonate sub-facies classification using NMR longitudinal over transverse relaxation time ratio. Advances in Geo-Energy Research, 2021, 5(1): 87-103, doi: 10.46690/ager.2021.01.09
Keywords
Full Text:
PDFReferences
Ali, L., Bordoloi, S., Wardinsky, S. Modeling permeability in tight gas sands using intelligent and innovative data mining techniques. Paper SPE 116583 Presented at SPE Annual Technical Conference and Exhibition, Denver, Colorado, 21-24 September, 2008.
Asgari, A. A., Sobhi, G. A. A fully integrated approach for the development of rock type characterization, in a middle east giant carbonate reservoir. Journal of Geophysics and Engineering, 2006, 3(3): 260-270.
Babadagli, T., Al-Salmi, S. A review of permeability-prediction methods for carbonate reservoirs using well-log data. SPE Reservoir Evaluation & Engineering, 2004, 7(2): 75-88.
Barwick, J. S. The Salina Basin of North-Central Kansas. AAPG Bulletin, 1928, 12(2): 177-199.
Bezdek, J. C. Pattern Recognition with Fuzzy Objective Function Algorithms. Berlin, Germany, Springer Science & Business Media, 1981.
Bezdek, J. C., Ehrlich, R., Full, W. FCM: The fuzzy c-means clustering algorithm. Computers & Geosciences, 1984, 10(2-3): 191-203.
Brandsegg, K. B., Hammer, E., Sinding-Larsen, R. A comparison of unstructured and structured principal component analyses and their interpretation. Natural Resources Research, 2010, 19(1): 45-62.
Bucheb, J. A., Evans, H. B. Some applications of methods used in electrofacies identification. The Log Analyst, 1994, 35(1). Burke, J. A., Campbell, R. L., Schmidt, A. W. The litho-porosity cross plot a method of determining rock characteristics for computation of log data. Paper SPE 2771 Presented at SPE lllinois Basin Regional Meeting, Evansville, Indiana, 30-31 October, 1969.
Busch, J. M., Fortney, W. G., Berry, L. N. Determination of lithology from well logs by statistical analysis. SPE Formation Evaluation, 1987, 2(4): 412-418.
Callaghan, P. T. Principles of Nuclear Magnetic Resonance Microscopy. New York, USA, Oxford University Press, 1991.
Carr, H. Y., Purcell, E. M. Effects of diffusion on free precession in nuclear magnetic resonance experiments. Physical Review, 1954, 94(3): 630-638.
Chang, H. C., Chen, H. C., Fang, J. H. Lithology determination from well logs with fuzzy associative memory neural network. IEEE Transactions on Geoscience and Remote Sensing, 1997, 35(3): 773-780.
Cheng, Y., Chen, S., Eid, M., et al. Determination of permeability from NMR T1 /T2 ratio in carbonates. Paper SPWLA 2017 Presented at SPWLA 58th Annual Logging Symposium, Oklahoma, USA, 17-21 June, 2017.
Clavier, C., Rust, D. H. Mid plot: A new lithology technique. The Log Analyst, 1976, 17(6): 1-9.
Coates, G. R., Xiao, L., Prammer, M. G. NMR Logging: Principles and Applications. Houston, USA, Haliburton Energy Services Publication H02308, 1999.
Cole, V. B. Subsurface Ordovician-Cambrian Rocks in Kansas. Kansas Geological Survey, Kansas, USA, 1975.
Coman, R., Tietjen, H., Thern, H., et al. Improved NMR logging approach to simultaneously determine porosity, T2 and T1 . Paper SPE 175050 Presented at SPE Annual Technical Conference and Exhibition, Houston, Texas, 28-30 September, 2015.
Criollo, D., Marin, Z., Vasquez, D. Advanced electrofacies modelling and permeability prediction: A case study incorporating multi-resolution core, NMR and image log textural information into a carbonate facies study. Paper SPE 175050 Presented at 22nd Formation Evaluation Symposium of Japan, Chiba, Japan, 29-30 September, 2016.
Delfiner, P. C., Peyret, O., Serra, O. Automatic determination of lithology from well logs. SPE Formation Evaluation, 1987, 2(3): 303-310.
d’Eurydice, M. N., Montrazi, E. T., Fortulan, C. A., et al. T2 -filtered T2 -T2 exchange NMR. The Journal of Chemical Physics, 2016, 144(20): 204201.
Dewan, J. T. Essentials of Modern Open-Hole Log Interpre-tation. Tulsa, USA, PennWell, 1983.
Dorfman, M. H., Newey, J. J., Coates, G. New techniques in lithofacies determination and permeability prediction in carbonates using well logs. Geological Society, London, Special Publications, 1990, 48(1): 113-120.
Doveton, J. H. Principles of Mathematical Petrophysics. Oxford, UK, Oxford University Press (UK), 2014.
Doveton, J. H., Cable, H. W. Fast matrix methods for the lithological interpretation of geophysical logs. Geomathematical and Petrophysical Studies in Sedimentology, 1979, 1979: 101-116.
Doveton, J. H., Watney, L. Textural and pore size analysis of carbonates from integrated core and nuclear magnetic resonance logging: An arbuckle study. Interpretation, 2015, 3(1): SA77-SA89. Dunn, J. C. A fuzzy relative of the isodata process and its use in detecting compact well-separated clusters. Journal of Cybernetics, 1973, 3(3): 32-57.
Dunn, K. J., Bergman, D. J., LaTorraca, G. A. Nuclear Magnetic Resonance: Petrophysical and Logging Applications. Amsterdam, the Netherlands, Elsevier, 2002.
Ehrenberg, S. N., Nadeau, P. H. Sandstone vs. Carbonate petroleum reservoirs: A global perspective on porosity-depth and porosity-permeability relationships. AAPG Bulletin, 2005, 89(4): 435-445.
El Sharawy, M. S., Nabawy, B. S. Determination of electrofacies using wireline logs based on multivariate statistical analysis for the kareem formation, gulf of suez, egypt. Environmental Earth Sciences, 2016, 75(21): 1394.
Flaum, C., Pirie, G. Determination of lithology from induced gamma-ray spectroscopy. Paper SPWLA 1981 Presented at the SPWLA 22nd Annual Logging Symposium, Mexico City, Mexico, 23-26 June, 1981.
Fleury, M., Romero-Sarmiento, M. Characterization of shales using T1–T2 NMR maps. Journal of Petroleum Science and Engineering, 2016, 137: 55-62.
Franseen, E. K. A review of arbuckle group strata in kansas from a sedimentologic perspective: Insights for future research from past and recent studies. The Compass: Earth Science Journal of Sigma Gamma Epsilon, 2000, 75(2-3): 68-89.
Franseen, E. K., Byrnes, A. P., Cansler, J. R., et al. Geologic controls on variable character of Arbuckle reservoirs in Kansas: An emerging picture. Kansas Geological Survey, Open-file Report, 2003, 59: 1-30.
Freedman, R., Lo, S., Flaum, M., et al. A new NMR method of fluid characterization in reservoir rocks: Experimental confirmation and simulation results. SPE Journal, 2001, 6(4): 452-464.
Freedman, R., Morriss, C. E. Apparatus including multi-wait time pulsed nmr logging method for determining accurate T2 -distributions and accurate T1 /T2 ratios and generating a more accurate output record using the updated T2 -distributions and T1/T2 ratios. USA, US5486762A, 1996.
Godefroy, S., Korb, J. P., Fleury, M., et al. Surface nuclear magnetic relaxation and dynamics of water and oil in macroporous media. Physical Review E, 2001, 64(2): 021605.
Grimm, E. C. Coniss: A fortran 77 program for stratigraphically constrained cluster analysis by the method of incremental sum of squares. Computers & Geosciences 1987, 13(1): 13-35.
Hurlimann, M. D., Flaum, C., Flaum, M., et al. Nuclear magnetic resonance method and logging apparatus for fluid analysis. USA, US6891369B2, 2005.
Jain, A. K. Data clustering: 50 years beyond k-means. Pattern Recognition Letters, 2010, 31(8): 651-666.
Jolliffe, I. Principal Component Analysis. Berlin, Germany, Springer, 2011.
Kenyon, W. E., Day, P. I., Straley, C., et al. A three-part study of NMR longitudinal relaxation properties of water-saturated sandstones. SPE Formation Evaluation, 1988, 3(3): 622-636.
Khetrapal, C. L., Kunwar, A., Tracey, A., et al. NMR Basic Principles and Progress. Berlin, Germany, Springer, 1975.
Kim, H. M., Mallick, B. K., Holmes, C. C. Analyzing nonstationary spatial data using piecewise gaussian processes. Journal of the American Statistical Association, 2005, 100(470): 653-668.
Kleinberg, R. L., Farooqui, S. A., Horsfield, M. A. T1/T2 ratio and frequency dependence of NMR relaxation in porous sedimentary rocks. Journal of Colloid and Interface Science, 1993a, 158(1): 195-198.
Kleinberg, R. L., Kenyon, W. E., Mitra, P. P. Mechanism of NMR relaxation of fluids in rock. Journal of Magnetic Resonance, Series A, 1994, 108(2): 206-214.
Kleinberg, R. L., Straley, C., Kenyon, W. E., et al. Nuclear magnetic resonance of rocks: T1 vs. T2 . Paper SPE 26470 Presented at SPE Annual Technical Conference and Exhibition, Houston, Texas, 3-6 October, 1993b.
Korb, J. P., Whaley-Hodges, M., Bryant, R. G. Translational diffusion of liquids at surfaces of microporous materials: Theoretical analysis of field-cycling magnetic relaxation measurements. Physical Review E, 1997, 56(2): 1934-1945.
Lee, S. H., Kharghoria, A., Datta-Gupta, A. Electrofacies characterization and permeability predictions in complex reservoirs. SPE Reservoir Evaluation & Engineering, 2002, 5(3): 237-248.
Lim, J. S., Park, H. J., Kim, J. A new neural network approach to reservoir permeability estimation from well logs. Paper SPE 100989 Presented at SPE Asia Pacific Oil & Gas Conference and Exhibition, Adelaide, Australia, 11-13 September, 2006.
Lucia, F. J. Carbonate reservoir characterization: An integrated approach. Berlin, Germany, Springer, 2007.
Mailhiot, S. E., Williamson, N. H., Brown, J. R., et al. T1–T2 correlation and biopolymer diffusion within human osteoarthritic cartilage measured with nuclear magnetic resonance. Applied Magnetic Resonance, 2017, 48(4): 407-422.
Martinez, A. M., Kak, A. C. Pca versus lda. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001, 23(2): 228-233.
Mathisen, T., Lee, S. H., Datta-Gupta, A. Improved permeability estimates in carbonate reservoirs using electrofacies characterization: A case study of the north robertson unit, west texas. Paper SPE 70034 Presented at SPE Permian Basin Oil and Gas Recovery Conference, Midland, Texas, 15-17 May, 2001.
McDonald, P. J., Korb, J. P., Mitchell, J., et al. Surface relaxation and chemical exchange in hydrating cement pastes: A two-dimensional NMR relaxation study. Physical Review E, 2005, 72(1): 011409.
Meiboom, S., Gill, D. Modified spin-echo method for measuring nuclear relaxation times. Review of Scientific Instruments, 1958, 29(8): 688-691.
Perez, H. H., Datta-Gupta, A., Mishra, S. The role of electrofacies, lithofacies, and hydraulic flow units in permeability predictions from well logs: A comparative analysis using classification trees. SPE Reservoir Evaluation & Engineering, 2005, 8(2): 143-155.
Poupon, A., Hoyle, W. R., Schmidt, A. W. Log analysis in formations with complex lithologies. Journal of Petroleum Technology, 1971, 23(8): 995-1005.
Qi, L., Carr, T. R. Neural network prediction of carbonate lithofacies from well logs, big bow and sand arroyo creek fields, southwest kansas. Computers & Geosciences, 2006, 32(7): 947-964.
Rastegarnia, M., Talebpour, M., Sanati, A. Prediction of electrofacies based on flow units using NMR data and svm method: A case study in cheshmeh khush field, southern iran. Journal of Petroleum Science and Technology, 2017, 7(3): 84-99.
Rokach, L., Maimon, O. Clustering Methods, in Data Mining and Knowledge Discovery Handbook. Boston, USA, Springer, 2005.
Roslin, A., Esterle, J. S. Electrofacies analysis for coal lithotype profiling based on high-resolution wireline log data. Computers & Geosciences, 2016, 91: 1-10.
Serra, O. T., Abbott, H. The contribution of logging data to sedimentology and stratigraphy. Society of Petroleum Engineers Journal, 1982, 22(1): 117-131.
Sharma, P., Mamgain, G., Bahuguna, V., et al. Improved permeability estimates in carbonate reservoirs using electrofacies characterization: A case study of mumbai high south. Paper Presented at The 2nd South Asain Geoscience Conference and Exhibition, New Delhi, India, 12-14 January, 2011.
Simpson, J. H., Carr, H. Y. Diffusion and nuclear spin relaxation in water. Physical Review, 1958, 111(5): 1201.
Skalinski, M., Gottlib-Zeh, S., Moss, B. Defining and predicting rock types in carbonates-preliminary results from an integrated approach using core and log data from the tengiz field. Petrophysics, 2006, 47(1): 328-340.
Song, Y. Q., Venkataramanan, L., Hürlimann, M. D., et al. T1–T2 correlation spectra obtained using a fast two-dimensional laplace inversion. Journal of Magnetic Resonance, 2002, 154(2): 261-268.
Straley, C. An experimental investigation of methane in rock materials. Paper SPWLA 1997 Presented at SPWLA 38th Annual Logging Symposium, Houston, Texas, 15-18 June, 1997.
Van Der Maaten, L. Learning a parametric embedding by preserving local structure. Artificial Intelligence and Statistics, 2009, 5: 384-391.
Washburn, K. E., Birdwell, J. E. Updated methodology for nuclear magnetic resonance characterization of shales. Journal of Magnetic Resonance, 2013, 233: 17-28.
Westphal, H., Surholt, I., Kiesl, C., et al. Nmr measurements in carbonate rocks: Problems and an approach to a solution. Pure and Applied Geophysics, 2005, 162(3): 549-570.
Zadeh, L. A., Klir, G. J., Yuan, B. Fuzzy sets, fuzzy logic, and fuzzy systems: Selected papers. Singapore, World Scientific, 1996.
Zeller, D. E. Stratigraphic Succession in Kansas. Kansas Geological Survey Bulletin 189, 1968.
DOI: https://doi.org/10.46690/ager.2021.01.09
Refbacks
- There are currently no refbacks.
Copyright (c) 2021 The Author(s)
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.