Alqahtani, A. A., Tutuncu, A. N., 2014. Quantification of Total Organic Carbon Content in Shale Source Rocks: An Eagle Ford Case Study Proceedings of the 2nd Unconventional Resources Technology Conference. August 25-27, 2014. Denver, Colorado, USA. https://doi.org/10.15530/urtec-2014-1921783 |
Cao, R. Y., Ma, Y. Z., Gomez, E., 2014. Geostatistical Applications in Petroleum Reservoir Modeling. South African Institute of Mining and Metallurgy, 114: 625-629 http://www.scielo.org.za/scielo.php?script=sci_abstract&pid=S2225-62532014000800013&lng=pt&nrm=iso&tlng=en |
Chiles, J. P., Delfiner, P., 2012. Geostatistics: Modeling Spatial Uncertainty. John Wiley & Sons, New York. 699 |
Cluff, S. G., Cluff, R. M., Hallau, D. G., et al., 2004. Petrophysics of the Lance and Upper Mesaverde Reservoirs at Pinedale Field, Sublette County, Wyoming, USA. AAPG Memoir, 107: 351-416. https://doi.org/10.1306/13511895m1073635 |
Cressie, N., 1993. Statistics for Spatial Data. John Wiley & Sons, New York. 900 |
Delfiner, P., 2007. Three Statistical Pitfalls of Phi-K Transforms. SPE Reservoir Evaluation & Engineering, 10(6): 609-617. https://doi.org/10.2118/102093-pa |
Ehsan, M., Gu, H. M., Akhtar, M. M., et al., 2018. Identification of Hydrocarbon Potential of Talhar Shale: Member of Lower Goru Formation Using Well Logs Derived Parameters, Southern Lower Indus Basin, Pakistan. Journal of Earth Science, 29(3): 587-593. https://doi.org/10.1007/s12583-016-0910-2 |
Fitch, P. J. R., Lovell, M. A., Davies, S. J., et al., 2015. An Integrated and Quantitative Approach to Petrophysical Heterogeneity. Marine and Petroleum Geology, 63: 82-96 doi: 10.1016/j.marpetgeo.2015.02.014 |
Fylling, A., 2002. Quantification of Petrophysical Uncertainty and Its Effect on In-Place Volume Estimates: Numerous Challenges and Some SolutionsAll Days. September 29-October 2, 2002. San Antonio, Texas. https://doi.org/10.2118/77637-ms |
Gotway, C. A., Young, L. J., 2002. Combining Incompatible Spatial Data. Journal of the American Statistical Association, 97(458): 632-648. https://doi.org/10.1198/016214502760047140 |
Holditch, S. A., 2006. Tight Gas Sands. Journal of Petroleum Technology, 58(6): 86-93. https://doi.org/10.2118/103356-jpt |
Isaaks, E. H., Srivastava, R. M., 1989. An Introduction to Applied Geostatistics. Oxford University Press, Oxford |
Jennings, J. W. Jr., 1999. How Much Core-Sample Variance should a Well-Log Model Reproduce?. SPE Reservoir Evaluation & Engineering, 2(5): 442-450. https://doi.org/10.2118/57477-pa |
Kennedy, M., 2015. Practical Petrophysics. Elsevier, Amsterdam |
Lake, L. W., Jensen, J. L., 1991. A Review of Heterogeneity Measures Used in Reservoir Characterization. In Situ, 15(4): 409-439 http://www.researchgate.net/publication/279604031_Review_of_heterogeneity_measures_used_in_reservoir_characterization |
Li, J. Q., Zhang, P. F., Lu, S. F., et al., 2019. Scale-Dependent Nature of Porosity and Pore Size Distribution in Lacustrine Shales: An Investigation by BIB-SEM and X-Ray CT Methods. Journal of Earth Science, 30(4): 823-833. https://doi.org/10.1007/s12583-018-0835-z |
Li, S. L., Gao, X. J., 2019. A New Strategy of Crosswell Correlation for Channel Sandstone Reservoirs—An Example from Daqing Oilfield, China. Interpretation, 7(2): T409-T421. https://doi.org/10.1190/int-2018-0074.1 |
Li, S. L., Zhang, Y., Ma, Y. Z., et al., 2018. A Comparative Study of Reservoir Modeling Techniques and Their Impact on Predicted Performance of Fluvial-Dominated Deltaic Reservoirs: Discussion. AAPG Bulletin, 102(8): 1659-1663. https://doi.org/10.1306/0108181613516519 |
Loucks, R. G., Reed, R. M., Ruppel, S. C., et al., 2012. Spectrum of Pore Types and Networks in Mudrocks and a Descriptive Classification for Matrix-Related Mudrock Pores. AAPG Bulletin, 96(6): 1071-1098. https://doi.org/10.1306/0817111106 |
Lucia, J. F., 2007. Carbonate Reservoir Characterization: 2nd Edition. Springer, Berlin |
Ma, Y. Z., 2010. Error Types in Reservoir Characterization and Management. Journal of Petroleum Science and Engineering, 72(3/4): 290-301. https://doi.org/10.1016/j.petrol.2010.03.030 |
Ma, Y. Z., 2011. Lithofacies Clustering Using Principal Component Analysis and Neural Network: Applications to Wireline Logs. Mathematical Geosciences, 43(4): 401-419. https://doi.org/10.1007/s11004-011-9335-8 |
Ma, Y. Z., 2018. An Accurate Parametric Method for Assessing Hydrocarbon Volumetrics: Revisiting the Volumetric Equation. SPE Journal, 23(5): 1566-1579. https://doi.org/10.2118/189986-pa |
Ma, Y. Z., 2019. Quantitative Geosciences: Data Analytics, Geostatistics, Reservoir Characterization and Modeling. Springer International Publishing, Cham. 640. https://doi.org/10.1007/978-3-030-17860-4 |
Ma, Y. Z., 2020. Three-Dimensional Modeling of Mineral/Elemental Compositions for Shale Reservoirs. SPE Journal, 25(4): 2067-2078. https://doi.org/10.2118/201118-pa |
Ma, Y. Z., Gomez, E., 2019. Sampling Biases and Mitigations in Modeling Shale Reservoirs. Journal of Natural Gas Science and Engineering, 71: 102968. https://doi.org/10.1016/j.jngse.2019.102968 |
Ma, Y. Z., Gomez, E., 2015. Uses and Abuses in Applying Neural Networks for Predictions in Hydrocarbon Resource Evaluation. Journal of Petroleum Science and Engineering, 133: 66-75. https://doi.org/10.1016/j.petrol.2015.05.006 |
Ma, Y. Z., Holditch, S. A., 2016. Preface: Unconventional Oil and Gas Resources Handbook. Elsevier, Amsterdam. xi-xiv. https://doi.org/10.1016/b978-0-12-802238-2.05001-x |
Ma, Y. Z., Moore W. R., Gomez, E., et al., 2016. Tight Gas Sandstone Reservoirs, Part 1: Overview and Lithofacies. Unconventional Oil and Gas Resources Handbook, Science Direct. 405-427 http://www.sciencedirect.com/science/article/pii/B9780128022382000146 |
Matheron, G., 1989. Estimating and Choosing—An Essay on Probability in Practice. Springer-Verlag, Berlin |
Moore, W. R., Ma, Y. Z., Urdea, J., et al., 2011. Uncertainty Analysis in Well Log and Petrophysical Interpretations. In: Ma, Y. Z., LaPointe, P., eds., Uncertainty Analysis and Reservoir Modeling. AAPG Memoir, 96: 17-28 |
Moore, W. R., Ma, Y. Z., Pirie, I., et al., 2016. Tight Gas Sandstone Reservoirs, Part 2: Petrophysical Analysis and Reservoir Modeling. In: Ma, Y. Z., Holditch, S., eds., Unconventional Resource Handbook: Evaluation and Development. Elsevier, Amsterdam. 429-449 |
Murtha, J., Ross, J., 2009. Uncertainty and the Volumetric Equation. Journal of Petroleum Technology, 61(9): 20-22. https://doi.org/10.2118/0909-0020-jpt |
Pearl, J., 2000. Causality: Models, Reasoning and Inference. Cambridge University Press, Cambridge. 384 |
Prensky, S. E., 1984. Use of Gamma-Ray Log for Locating Cretaceous-Tertiary Unconformity, Pinedale Area, Northern Green River Basin, Wyoming: Abstract. AAPG Bulletin, 68(7): 946-946. https://doi.org/10.1306/ad4615c0-16f7-11d7-8645000102c1865d |
Robinson, W. S., 1950. Ecological Correlations and the Behavior of Individuals. American Sociological Review, 15(3): 351. https://doi.org/10.2307/2087176 |
Saraji, S., Goual, L., Piri, M., et al., 2013. Wettability of Supercritical Carbon Dioxide/Water/Quartz Systems: Simultaneous Measurement of Contact Angle and Interfacial Tension at Reservoir Conditions. Langmuir, 29(23): 6856-6866 doi: 10.1021/la3050863 |
Slatt, R. M., 2006. Stratigraphic Reservoir Characterization for Petroleum Geologists, Geophysicists, and Engineers. In: Cubitt, J., ed., Handbook of Petroleum Exploration and Production. Elsevier, Amsterdam. https://doi.org/10.1016/s1567-8032(06)x8035-7 |
Tiab, D., Donaldson, E. C., 2003. Petrophysics: Theory and Practice of Measuring Reservoir Rock and Fluid Transport Properties: 2nd Edition. Gulf Professional Pub., Oxford |
Wang, G. C., Carr, T. R., 2012. Marcellus Shale Lithofacies Prediction by Multiclass Neural Network Classification in the Appalachian Basin. Mathematical Geosciences, 44(8): 975-1004. https://doi.org/10.1007/s11004-012-9421-6 |
Wu, Z. R., He, S., Han, Y. J., et al., 2020. Effect of Organic Matter Type and Maturity on Organic Matter Pore Formation of Transitional Facies Shales: A Case Study on Upper Permian Longtan and Dalong Shales in Middle Yangtze Region, China. Journal of Earth Science, 31(2): 368-384. https://doi.org/10.1007/s12583-019-1237-6 |