Well-log attributes assist in the determination of reservoir formation tops in wells with sparse well-log data
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Abstract
The manual picking of reservoir formation boundaries using limited available well-log data in multiple wells across gas and oil reservoirs tends to be subjective and unreliable. The reasons for this are typically caused by the combined effects of spatial boundary complexity and limited well-log data availability. Formation boundary characterization and classification can be improved when treated as a binary classification task based on two or three recorded well logs assisted by their calculated derivative and volatility attributes assessed by machine learning. Two example wellbores penetrating a complex reservoir boundary, one with gamma-ray, compressional-sonic, and bulk-density logs recorded, the other with just gamma-ray and bulk-density logs recorded, are used to illustrate a more rigorous proposed methodology. By combining attribute calculation, optimized feature selection, multi-k-fold cross validation, confusion matrices, feature-influence analysis, and machine learning models it is possible to improve the classification of the formation boundary. With just gamma-ray and bulk-density recorded well logs plus selected attributes. K-nearest neighbour, support vector classification, and extreme gradient boosting machine learning models are able to achieve high binary classification accuracy: greater than 0.97 for training/validation in one well; and greater than 0.94 for testing in another well. extreme gradient boosting feature-influence analysis reveals the attributes that are the most important in the formation boundary predictions but these are likely to vary from reservoir to reservoir. The results of the study suggest that well-log attribute analysis, combined with machine learning has the potential to provide a more systematic formation boundary definition than relying only on a few recorded well-log curves.
Document Type: Original article
Cited as: Wood., D. A. Well-log attributes assist in the determination of reservoir formation tops in wells with sparse well-log data. Advances in Geo-Energy Research, 2023, 8(1): 45-60. https://doi.org/10.46690/ager.2023.04.05
Keywords
References
Al-AbdulJabbar, A., Elkatatny, S., Mahmoud, M., et al. Predicting formation tops while drilling using artificial intelligence. Paper SPE 192345 Presented at SPE Kingdom of Saudi Arabia Annual Technical Symposium and Exhibition, Dammam, Saudi Arabia, 23-26 April, 2018.
Atashnezhad, A., Wood, D. A., Fereidounpour, A., et al. Designing and optimizing deviated wellbore trajectories using novel particle swarm algorithms. Journal of Natural Gas Science and Engineering, 2014, 21: 1184-1204.
Banas, R., McDonald, A., Perkins, T. J. Novel methodology for automation of bad well log data identification and repair. Paper SPWLA 2021-0070 Presented at SPWLA 62nd Annual Logging Symposium, Virtual Event, 17-21 May, 2021.
Chang, Y., Hsieh, C., Chang, K., et al. Training and testing low-degree polynomial data mappings via linear SVM. Journal of Machine Learning Research, 2010, 11(4): 1471-1490.
Chen, T. Guestrin, C. XGBoost: A scalable tree boosting system. Paper Presented at the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, California, 13-17 Augest, 2016.
Cortes, C., Vapnik, V. Support-Vector Networks. Machine Learning, 1995, 20(3): 273-297.
Cox, D. R. The regression analysis of binary sequences. Journal of the Royal Statistical Society. Series B (Methodological). 1958, 20(2): 215-242.
Darling, T. Well Logging and Formation Evaluation. Huston, USA, Gulf Professional Publishing, 2005.
Faris, H., Mirjalili, S., Aljarah, I., et al. Salp swarm algorithm: Theory, literature review, and application in extreme learning machines, in Nature-inspired Optimizers, edited by S., Song Dong, J., Lewis, A., Cham, pp. 185-199, 2020.
Fix, E., Hodges Jr., J. L. Discriminatory analysis, nonparametric discrimination: Consistency properties. San Antonio, USAF School of Aviation Medicine, 1951.
Grant, C. W., Bashore, W. M., Compton, S. Rapid reservoir modeling with automated tops correlation. Paper URTEC 2904037 Presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, Houston, Texas, 23-25 July, 2018.
Hong, Y., Kang, C. Automatic well correlation by aligning multiple well using deep neural networks. Paper SPE 202908 Presented at Abu Dhabi International Petroleum Exhibition & Conference, Abu Dhabi, UAE, 9-12 November, 2020.
Lineman, D. J., Mendelson, J. D., Toksoz, M. N. Well-towell log correlation using knowledge-based systems and dynamic depth warping. Paper SPWLA 1987-UU Presented at the SPWLA 28th Annual Logging Symposium, London, England, 29 June-2 July, 1987.
Liu, K., Ostadhassan, M. The impact of pore size distribution data presentation format on pore structure interpretation of shales. Advances in Geo-Energy Research, 2019, 3(2): 187-197.
Luo, H., Liu, S., Wang, C., et al. Well-to-seismic calibration in depth domain using dynamic depth warping techniques. Paper Presented at International Geophysical Conference, Beijing, China, 24-27 April, 2018.
Luthi, S. M. Geological Well Logs: Their Use in Reservoir Modeling. Berlin, Germany, Springer Science & Business Media, 2011.
Rafiei, Y., Motie, M. Improved reservoir characterization by employing hydraulic flow unit classification in one of Iranian carbonate reservoirs. Advances in Geo-Energy Research, 2019, 3(3): 277-286.
Rao, R. V. Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems. International Journal of Industrial Engineering Computations, 2016, 7: 19-34.
SciKit Learn. Supervised and unsupervised machine learning models in Python. 2023a.
SciKit Learn. GridSearchCV: Exhaustive search over specified parameter values for an estimator in Python. 2023b.
SciKit Learn. Bayesian optimization of hyperparameters in Python. 2023c.
SciKit Learn. Cross-validation: evaluating estimator performance. 2023d.
Serra, O. Fundamentals of Well-Log Interpretation-2. The Interpretation of Logging Data, Developments in Petroleum Science. Amsterdam, Netherlands, Elsevier, 1986.
Shahid, R., Bertazzon, S., Knudtson. M. L., et al. Comparison of distance measures in spatial analytical modeling for health service planning. BMC Health Services Research, 2009, 9: 200.
Wood, D. A. Hybrid cuckoo search optimization algorithms applied to complex wellbore trajectories aided by dynamic, chaos-enhanced, fat-tailed distribution sampling and metaheuristic profiling. Journal of Natural Gas Science and Engineering, 2016, 34: 236-252.
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, 2022a, 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, 2022b, 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, 2022c, 15(3): 1699-1721.
Wood, D. A. Optimized feature selection assists lithofacies machine learning with sparse well log data combined with calculated attributes in a gradational fluvial sequence. Artificial Intelligence in Geosciences, 2022d, 3: 132-147.
Zhang, Y., Li, Y., Guo, W., et al. Differential evolution and the influencing factors of low-maturity terrestrial shale with different types of kerogen: A case study of a Jurassic shale from the northern margin of Qaidam Basin, China. International Journal of Coal Geology, 2020, 230: 103591.
Zhang, B., Ye, T., Xiao, Y., et al. Automated well top picking and reservoir property analysis of the Belly River Formation of the Western Canada Sedimentary Basin. Paper URTEC 3719133 Presented at SPE/AAPG/SEG Unconventional Resources Technology Conference, Houston, Texas, 20-22 June, 2022.
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.
Zoraster, S., Paruchuri, Ramoj, P., Darby, S. Curve alignment for well-to-well log correlation. Paper SPE 90471 Presented at SPE Annual Technical Conference and Exhibition, Houston, Texas, 26-29 September, 2004.
DOI: https://doi.org/10.46690/ager.2023.04.05
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