Predicting brittleness indices of prospective shale formations from sparse well-log suites assisted by derivative and volatility attributes

David A. Wood

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A technique is proposed that calculates derivative and volatility attributes from just a few well log curves to assist in brittleness index predictions from sparse well-log datasets with machine learning methods. Six well-log attributes are calculated for selected recorded well logs: the first derivative, the moving average of the first derivative, the second derivative, the logarithm of the instantaneous volatility, the standard deviation of volatility, and the moving average of volatility. These attributes make it possible to extrapolate brittleness index calibrations from the few cored and comprehensively logged wells to surrounding wells in which only minimal well-log suites are recorded. Data from two cored wells penetrating the lower Barnett Shale with distinct lithology and five well logs recorded are used to demonstrate the technique. Based on multi-K-fold cross validation analysis, the data matching K-nearest neighbour machine learning model provides the most accurate brittleness index predictions, closely followed by tree-ensemble models. For this dataset, recorded data from three well logs plus calculated attributes matches the brittleness index prediction accuracy that is achieved by the five recorded logs. Moreover, any one of the logs plus their calculated attributes yields better brittleness index prediction performance than that achieved by a combination of just those three recorded well logs. Analysis of the Gini indices of the tree-ensemble models reveals the relative influences of the recorded logs and their attributes on the brittleness index prediction solutions. Such information is used to perform feature selection to optimize the well-log attributes involved to generate reliable brittleness index predictions.

Cited as: Wood, D. A. Predicting brittleness indices of prospective shale formations from sparse well-log suites assisted by derivative and volatility attributes. Advances in Geo-Energy Research, 2022, 6(4): 334-346.


Well-log attributes, brittleness index predictions, feature selection, multi-K-fold cross validation, comparative influence analysis, sparsely logged wells

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Abouelresh, M., Slatt, R. Lithofacies and sequence stratigraphy of the Barnett Shale in east-central Fort Worth Basin, Texas. AAPG Bulletin, 2012, 96 (1): 1-22.

Altindag, R. Correlation of specific energy with rock brittleness concepts on rock cutting. Journal of The South African Institute of Mining and Metallurgy, 2003, 103: 163-172.

Alzahabi, A., AlQahtani, G., Soliman, M. Y., et al. Fracturability index is a mineralogical index: A new approach for fracturing decision. Paper SPE 178033 Presented at SPE Saudi Arabia Section Annual Technical Symposium and Exhibition, Al-Khobar, Saudi Arabia, 21-23 April, 2015.

Boyer, C., Kieschnick, J., Rivera, R. S., et al. Producing Gas from its Source. New York, USA, Schlumberger, 2006.

Chen, T. Guestrin, C. XGBoost: A scalable tree boosting system. Paper Presented at Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, August 13-17, 2016.

Freund, Y., Schapire, R. E. A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 1997, 55: 119-139.

Gholami, R., Rasouli, V., Sarmadivaleh, M., et al. Brittleness of gas shale reservoirs: A case study from the north Perth basin, Australia. Journal of Natural Gas Science and Engineering, 2016, 33: 1244-1259.

Glorioso, J. C., Rattia, A. Unconventional Reservoirs: Basic Petrophysical Concepts for Shale Gas. Paper SPE 153004 Presented at SPE/EAGE European Unconventional Resources Conference and Exhibition, Vienna, Austria, 20- 22 March, 2012.

Grieser, B., Bray, J. M. Identification of production potential in unconventional reservoirs. Paper SPE 106623 Presented at Production and Operations Symposium, Oklahoma City, Oklahoma, 31 March-3 April, 2007.

Guo. Z. Q., Li, X. Y., Chapman, M. Correlation of brittleness index with fractures and microstructure in the Barnett Shale. Paper Presented at 74th EAGE Conference & Exhibition, Copenhagen, Denmark, 4-7 June, 2012.

Herwanger, J. V., Bottrill, A. D., Mildren, S. D. Uses and abuses of the brittleness index with applications to hydraulic stimulation. Paper URTeC 2172545 Presented at Unconventional Resources Technology Conference, San Antonio, Texas, 20-22 July, 2015.

Ho, T. K. The random subspace method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998, 20(8): 832-844.

Jarvie, D. M., Hill, R. J., Ruble, T. E., et al. Unconventional shale-gas systems: The Mississippian Barnett Shale of North-Central Texas as one model for thermogenic shale-gas assessment. AAPG Bulletin, 2007, 91(4): 475-499.

Jin, X., Shah, S. N., Roegiers, J. C., et al. Fracability evaluation in shale reservoirs-An integrated petrophysics and geomechanics approach. Paper SPE 168589 Presented at Proceeding of SPE Hydraulic Fracturing Technology Conference, The Woodlands, Texas, 4-6 February, 2014.

Kuanda, R. B., Asbury, B. Prediction of rock brittleness using nondestructive methods for hard rock tunnelling. Journal of Rock Mechanics and Geotechnical Engineering, 2016, 8: 533-540.

Lai, J., Wang, G., Huang, L., et al. Brittleness index estimation in a tight shaly sandstone reservoir using well logs. Journal of Natural Gas Science and Engineering, 2015, 27: 1536-1545.

Mews, K. S., Alhubail, M. M., Barati, R. G. A review of brittleness index correlations for unconventional tight and ultra-tight reservoirs. Geosciences, 2019, 9 (7): 319.

Mlella, M., Ma, M., Zhang, R., et al. Machine learning for geophysical characterization of brittleness: Tuscaloosa Marine Shale case study. Interpretation, 2020, 8(3): T589.

Myers, L., Sirois M. J. Spearman correlation coefficients, differences between. Oxford, US, Oxford Wiley-Blackwell, 2014.

Ore, T., Gao, D. Supervised machine learning to predict brittleness using well logs and seismic signal attributes: Methods and application in an unconventional reservoir. Paper SEG 3594773 Presented at SEG/AAPG/SEPM First International Meeting for Applied Geoscience & Energy, Denver, Colorado, 26 September-1 October, 2021.

Pollastro, R. M., Jarvie D. M., Hill, R. J., et al. Geologic framework of the Mississippian Barnett Shale, Barnett Paleozoic total petroleum system, Bend Arch-Fort Worth Basin, Texas. AAPG Bulletin, 2007, 91(4): 405-436.

Rickman, R., Mullen, M. J., Petre, J. E., et al. A practical use of shale petrophysics for stimulation design optimization: All shale plays are not clones of the Barnett Shale. Paper SPE 115258 Presented at SPE Annual Technical Conference and Exhibition, Denver, Colorado, 21-24 September, 2008.

Shi, X., Liu, G., Cheng, Y., et al. Brittleness index prediction in shale gas reservoirs based on efficient network models. Journal of Natural Gas Science and Engineering, 2016, 35: 673-685.

Singh, P., Slatt, R. M., Coffey, W. Barnett Shale-Unfolded: Sedimentology, sequence stratigraphy, and regional mapping. Gulf Coast Association of Geological Societies Transactions, 2008, 58: 777-795.

Verma, S., Zhao, T. Marfurt, K. J., et al. Estimation of total organic carbon and brittleness volume. Interpretation, 2016, 4 (3): 373-385.

Walper, J. L. Plate tectonic evolution of the Fort Worth Basin, in C. A. Martin, ed., Petroleum Geology of the Fort Worth Basin and Bend Arch Area, 1982: 237-251.

Wang, F., Gale, J. F. W. Screening criteria for shale-gas systems. Gulf Coast Association of Geological Societies Transactions, 2009, 59: 779-793.

Wang, Y. The method of application of gamma-ray spectral logging data for determining clay mineral content. Journal of Oil and Gas Technology, 2013, 35(2): 100-104.

Wood, D. A., Hazra, B. Characterization of organic-rich shales for petroleum exploration & exploitation: A review-part 3 applied geomechanics, petrophysics and reservoir modeling. Journal of Earth Science, 2017, 28(5): 779-803.

Wood, D. A. Brittleness index predictions from lower Barnett Shale well-log data applying an optimized data matching algorithm at various sampling densities. Geoscience Frontiers, 2021, 12(6): 101087.

Wood, D. A. Assessing the brittleness and total organic carbon of shale formations and their role in identifying optimum zones to fracture stimulate. Sustainable Geoscience for Natural Gas Subsurface Systems. Gulf Professional Publishing, 2022a: 129-157.

Wood, D. A. Gamma-ray log derivative and volatility attributes assist facies characterization in clastic sedimentary sequences for formulaic and machine learning analysis. Advances in Geo-Energy Research, 2022b, 6(1): 69-85.

Wood, D. A. Enhancing lithofacies machine learning predictions with gamma-ray attributes for boreholes with limited diversity of recorded well logs. Artificial Intelligence in Geosciences, 2022c, 2: 148-164.

Wood, D. A. Carbonate/siliciclastic lithofacies classification aided by well-log derivative, volatility and sequence boundary attributes combined with machine learning. Earth Science Informatics, 2022d.

Ye, Y., Tang, S., Xi, Z., et al. A new method to predict brittleness index for shale gas reservoirs: Insights from well logging data. Journal of Petroleum Science and Engineering, 2022, 208: 109431.

Yang, Y., Sone, H., Hows, A., et al. Comparison of brittleness indices in organic-rich shale formations. Paper ARMA 403 Presented at 47th U.S. Rock Mechanics/Geomechanics Symposium, San Francisco, California, 23-26 June, 2013.

Zhang, D., Ranjith, P. G., Perera, M. S. A. The brittleness indices used in rock mechanics and their application in shale hydraulic fracturing: A review. Journal of Petroleum Science and Engineering, 2016, 143: 158-170.



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