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Bakken Stratigraphic and Type Well-Log Learning Network for Transparent Prediction and Rigorous Data Mining

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Abstract

A Bakken formation learning network is established based upon type well-log data (seven petrophysical variables) and a discrete stratigraphic index (Str) comprising 1000 records extending into the underlying Three Forks formation. The transparent open box (TOB) learning network is applied to this dataset to predict Str, which it achieves with only two erroneous predictions for the 1000 records and high statistical accuracy (root mean squared error (RMSE) = 0.1057, for Str scale of 1–4; coefficient of determination (R2) = 0.9870). Data mining reveals that the few prediction errors are located in the transition zones between the stratigraphic members. Feature selection focused on those transition zones has the potential to further reduce errors. The TOB algorithm demonstrates its potential to be applied for more extensive and complex lithofacies and stratigraphic sequence modeling. The Bakken TOB network is also configured to predict shear wave velocity, which it does with high accuracy (RMSE = 11 m/s and R2 = 0.9994), highlighting the flexibility of the TOB algorithm to assess both continuous and discrete dependent variables.

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References

  • Altman, N. S. (1992). An introduction to kernel and nearest-neighbor nonparametric regression. The American Statistician,46(3), 175–185.

    Google Scholar 

  • Arora, S., & Singh, S. (2013). The firefly optimization algorithm: Convergence analysis and parameter selection. International Journal of Computer Applications,69(3), 48–52.

    Article  Google Scholar 

  • Arora, S., & Singh, S. (2014). Performance research on firefly optimization algorithm with mutation. In International conference on communication, computing & systems (pp. 168–172).

  • Atkeson, C. G., Moore, A. W., & Schaal, S. (1997). Locally weighted learning. Artificial Intelligence Review,11(1–5), 11–73.

    Article  Google Scholar 

  • Bhattacharya, S., & Carr, T. (2016). Integrated petrofacies characterization and interpretation of depositional environment of the Bakken shale in the Williston basin, north America. Petrophysics,57(2), 95–110.

    Google Scholar 

  • Bhattacharya, S., & Carr, T. (2019). Integrated data-driven 3D shale lithofacies modeling of the Bakken Formation in the Williston basin, North Dakota, United States. Journal of Petroleum Science and Engineering,177, 1072–1086.

    Article  Google Scholar 

  • Bhattacharya, S., & Mishra, S. (2018). Applications of machine learning for facies and fracture prediction using Bayesian Network Theory and Random Forest: Case studies from the Appalachian basin, USA. Journal of Petroleum Science and Engineering,170, 1005–1017.

    Article  Google Scholar 

  • Birattari, M., Bontempi, G., & Bersini, H. (1999). Lazy learning meets the recursive least squares algorithm. In Proceedings of the 1998 conference on advances in neural information processing systems II (pp. 375–381). Cambridge, MA: MIT Press.

  • Bontempi, G., Birattari, M., & Bersini, H. (1999). Lazy learning for local modeling and control design. International Journal of Control,72(7/8), 643–658.

    Article  Google Scholar 

  • Chemali, R., Gianzero, S., Strickland, R. W., & Tijani, S. M. (1983). The shoulder bed effect on the dual laterolog and its variation with the resistivity of the borehole fluid. In Society of petrophysicists and well-log analysts annual logging symposium, 27–30 June, Calgary, Alberta, 25 pp. https://www.onepetro.org/conference-paper/SPWLA-1983-UU.

  • Chen, G. H., & Shah, D. (2018). Explaining the success of nearest neighbor methods in prediction. Foundations and Trends in Machine Learning,10(5–6), 337–588.

    Article  Google Scholar 

  • Christopher, J. E. (1961). Transitional Devonian–Mississippian formations of southern Saskatchewan. Saskatchewan Department of Mineral Resources Report, 66, 103 pp.

  • Cover, T. M., & Hart, P. E. (1967). Nearest neighbor pattern classification. IEEE Transactions on Information Theory,13(1), 21–27.

    Article  Google Scholar 

  • Egenhoff, S. O., & Fishman, N. S. (2013). Traces in the dark—Sedimentary processes and facies gradients in the upper shale member of the Upper Devonian-Lower Mississippian Bakken Formation, Williston basin, North Dakota, USA. Journal of Sedimentary Research,83, 803–824.

    Article  Google Scholar 

  • EIA. (2019). Bakken Region Drilling productivity report, U. S. Energy Information Administration (EIA), March 2019. https://www.eia.gov/petroleum/drilling/pdf/bakken.pdf. Accessed April 12, 2019.

  • Fix, E., & Hodges, Jr. J. L. (1951). Discriminatory analysis, nonparametric discrimination: consistency properties. Technical report, USAF School of Aviation Medicine.

  • Flannery, J. (2006). Integrated analysis of the Bakken Petroleum System, U.S. Williston Basin. In Saskatchewan and Northern Plains Oil & Gas Symposium (pp. 138–145). Saskatchewan Geological Society.

  • Frontline Solvers. (2019). Standard excel solver—Limitations of nonlinear optimization. https://www.solver.com/standard-excel-solver-limitations-nonlinear-optimization Accessed April 12, 2019.

  • Garcia, S., Derrac, J., Cano, J., & Herrera, F. (2012). Prototype selection for nearest neighbor classification: Taxonomy and empirical study. IEEE Transactions on Pattern Analysis and Machine Intelligence,34(3), 417–435.

    Article  Google Scholar 

  • Gaswirth, S. B., Marra, K. R., Cook, T. A, Charpentier, R. R., Gautier, D. L., Higley, D. K., et al. (2013). Assessment of undiscovered oil resources in the Bakken and Three Forks Formations, Williston Basin Province, Montana, North Dakota, and South Dakota, 2013. U.S. Geological Survey Fact Sheet 2013–3013 (p. 4). https://pubs.usgs.gov/fs/2013/3013/.

  • Guedez, A., Mokhtari, M., Seibi, A., & Mitra, A. (2018). Developing correlations for velocity models in vertical transverse isotropic media: Bakken case study. Journal of Natural Gas Science and Engineering,54, 175–188.

    Article  Google Scholar 

  • Gul, A., Perperoglou, A., Khan, Z., Mahmoud, O., Miftahuddin, M., Adler, W., & Lausen, B. (2018). Ensemble of a subset of kNN classifiers. Advances in Data Analysis and Classification, 12(4), 827–840. https://doi.org/10.1007/s11634-015-0227-5.

    Article  Google Scholar 

  • Gutschick, R. C., Suttner, L. J., & Switek, M. J. (1962). Biostratigraphy of transitional Devonian–Mississippian Sappington formation of southwest Montana: Billings Geological Society. In 13th annual field conference and symposium (pp. 79–87).

  • Heinert, M. (2008). Artificial neural networks—How to open the black boxes. Application of Artificial Intelligence in Engineering Geodesy: AIEG,5, 42–62.

    Google Scholar 

  • Hester, T., & Schmoker, J. (1985). Selected physical properties of the Bakken Formation, North Dakota and Montana Part of the Williston Basin. Department of the Interior, U.S. Geological Survey, Oil and Gas Investigations.

  • Hyndman, R. J., & Koehler, A. B. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting,22(4), 679–688. https://doi.org/10.1016/j.ijforecast.2006.03.001.

    Article  Google Scholar 

  • Kasper, D. (1992). Stratigraphy and sedimentology of the Bakken Formation of west-central Saskatchewan: A preliminary report. In Summary of investigations 1992, Saskatchewan Geological Survey. Saskatchewan Energy and Mines, Miscellaneous Report 92-4.

  • Kokesh, F. P., & Blizzard, R. B. (1959). Geometrical factors in sonic logging. Geophysics,24(1), 64–76.

    Article  Google Scholar 

  • Lever, J., Krywinski, M., & Altman, N. (2016). Model selection and overfitting. Nature Methods,13, 703–704. https://doi.org/10.1038/nmeth.3968.

    Article  Google Scholar 

  • Luo, G., Tian, Y., Bychina, M., & Ehlig-Economides, C. (2018). Production optimization using machine learning in Bakken Shale. In URTEC-2902505-MS. SPE/AAPG/SEG unconventional resources technology conference, 23–25 July, Houston, Texas, USA, 24 pp. https://doi.org/10.15530/urtec-2018-2902505.

  • MacQueen, J. B. (1967). Some methods for classification and analysis of multivariate observations. In Proceedings of 5th Berkeley symposium on mathematical statistics and probability. 1 (pp. 281–297). University of California Press.

  • Makridakis, S. (1993). Accuracy measures: Theoretical and practical concerns. International Journal of Forecasting,9(4), 527–529.

    Article  Google Scholar 

  • Mood, A., Graybill, F., & Boes, D. (1974). Introduction to the theory of statistics (3rd ed.). New York: McGraw-Hill.

    Google Scholar 

  • Nordquist, J. W. (1953). Mississippian stratigraphy of northern Montana. In Fourth annual field conference guidebook (pp. 68–82). Billings Geological Society.

  • North Dakota Industrial Commission. (2017). Department of Mineral Resources. Oil gas division. https://www.dmr.nd.gov/oilgas. Accessed April 12, 2019.

  • Pal, S. K., Raj, C. S., & Singh, A. P. (2012). Comparative study of firefly algorithm and particle swarm optimization for noisy non-linear optimization problems. International Journal of Intelligent Systems and Applications,10, 50–57.

    Article  Google Scholar 

  • Panja, P., Velasco, R., Pathak, M., & Deo, M. (2018). Application of artificial intelligence to forecast hydrocarbon production from shale. Petroleum,4, 75–89.

    Article  Google Scholar 

  • Parapuram, G. K., Mokhtari, M., & Hmida, J. B. (2017). Prediction and analysis of geomechanical properties of the upper Bakken shale utilizing artificial intelligence and data mining. In Unconventional resources technology conference, 24–26 July, Austin, Texas, USA.

  • Parapuram, G. K., Mokhtari, M., & Hmida, J. B. (2018). An artificially intelligent technique to generate synthetic geomechanical well logs for the Bakken Formation. Energies,11, 680. https://doi.org/10.3390/en11030680.

    Article  Google Scholar 

  • Pearson, K. (1894). On the dissection of asymmetrical frequency curves. Philosophical Transactions of the Royal Society A,185, 71–110.

    Google Scholar 

  • Pitman, J. K., Price, L. C., & LeFever, J. A. (2001). Diagenesis and fracture development in the Bakken Formation, Williston Basin: implications for reservoir quality in the middle member (pp. 1–19). U.S. Geological Survey Professional Paper, 1653.

  • Pollastro, R., Roberts, L., Cook, T., & Lewan, M. (2008). Assessment of undiscovered technically recoverable oil and gas resources of the Bakken Formation, Williston Basin, Montana and North Dakota. U.S. Geological Survey Open-File Report 2008–1353 (3 pp).

  • Samworth, R. (2012). Optimal weighted nearest neighbour classifiers. The Annals of Statistics,40(5), 2733–2763.

    Article  Google Scholar 

  • Shakhnarovich, G., Darrell, T., & Indyk P. (2006). Nearest-neighbor methods in learning and vision: theory and practice (Neural information processing). The MIT Press. ISBN: 026219547X.

  • Simpson, G., Hohman, J., Pitie, I., & Horkowitz, J. (2015). Using advanced logging measurements to develop a robust petrophysical model for the Bakken petroleum system. In SPWLA 56th annual logging symposium, July 18–22 (pp. 1–24).

  • Smith, M. G., & Bustin, R. M. (1996). Lithofacies and paleoenvironments of the Late Devonian and Early Mississippian Bakken Formation. Williston Basin: Canadian Society of Petroleum Geologists Bulletin,44, 495–507.

    Google Scholar 

  • Sonnenberg, S. A. (2017). Sequence stratigraphy of the Bakken and Three Forks Formations, Williston Basin, USA. Presented at: AAPG Rocky Mountain section annual meeting, Billings, Montana, June 25–28.

  • Wood, D. A. (2016a). Metaheuristic profiling to assess performance of hybrid evolutionary optimization algorithms applied to complex wellbore trajectories. Journal of Natural Gas Science and Engineering,33, 751–768. https://doi.org/10.1016/j.jngse.2016.05.041.

    Article  Google Scholar 

  • Wood, D. A. (2016b). Evolutionary memetic algorithms supported by metaheuristic profiling effectively applied to the optimization of discrete routing problems. Journal of Natural Gas Science and Engineering,35, 997–1014. https://doi.org/10.1016/j.jngse.2016.09.031.

    Article  Google Scholar 

  • Wood, D. A. (2018a). A transparent open-box learning network provides insight to complex systems and a performance benchmark for more-opaque machine learning algorithms. Advances in Geo-Energy Research,2(2), 148–162.

    Article  Google Scholar 

  • Wood, D. A. (2018b). Transparent open-box learning network provides auditable predictions for coal gross calorific value. Modeling Earth Systems and Environment. https://doi.org/10.1007/s40808-018-0543-9.

    Article  Google Scholar 

  • Wood, D. A. (2019). Sensitivity analysis and optimization capabilities of the transparent open-box learning network in predicting coal gross calorific value from underlying compositional variables. Modeling Earth Systems and Environment. https://doi.org/10.1007/s40808-019-00583-1.

    Article  Google Scholar 

  • Wood, D. A., Choubineh, A., & Vaferi, B. (2018). Transparent open-box learning network provides auditable predictions: Pool boiling heat transfer coefficient for alumina-water-based nanofluids. Journal of Thermal Analysis and Calorimetry. https://doi.org/10.1007/s10973-018-7722-9.

    Article  Google Scholar 

  • Wright, S. (1921). Correlation and causation. Journal of Agricultural Research,20, 557–585.

    Google Scholar 

  • Yang, X.-S. (2009). Firefly algorithms for multimodal optimization. In Stochastic Algorithms: Foundations and Applications, SAGA, Lecture Notes in Computer Sciences (Vol. 5792, pp. 169–178).

  • Yang, X.-S., & He, X. (2013). Firefly algorithm: Recent advances and applications. International Journal of Swarm Intelligence,1(1), 36–50.

    Article  Google Scholar 

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This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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  1. DWA Energy Limited, Lincoln, UK

    David A. Wood

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  1. David A. Wood
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Correspondence to David A. Wood.

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Appendices

Appendix 1

Schematic workflow diagram for the TOB well-log learning methodology.

figure a

Appendix 2

The reconstructed stratigraphic member and Bakken type well-log dataset analyzed in this study (including selected ratios between the variables) are included as a supplementary Excel file.

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Wood, D.A. Bakken Stratigraphic and Type Well-Log Learning Network for Transparent Prediction and Rigorous Data Mining. Nat Resour Res 29, 1329–1349 (2020). https://doi.org/10.1007/s11053-019-09525-1

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