International Scientific Journal


In the realm of unconventional reservoir hydraulic fracturing design, the conventional optimization of mechanistic model parameters is a time-consuming process that impedes its responsiveness to the swift demands of on-site development. This study, rooted in Xinjiang oilfield data, delves into the utilization of machine learning methods for extensive field data. The research systematically elucidates the training and optimization procedures of a production forecasting model, achieving effective optimization of hydraulic fracturing design parameters. By employing polynomial feature cross-construction generate composite features, feature filtering is performed using the maximal information coefficient. Subsequently, wrapper-style feature selection techniques, including ridge regression and decision trees, are applied to ascertain the optimal combinations of model input parameters. The integration of stacking during model training enhances performance, while stratified K-fold cross-validation is implemented to mitigate the risk of overfitting. The ultimate optimization of hydraulic fracturing design parameters is realized through a competitive learning particle swarm algorithm. Results indicate that the accuracy of the data-driven production forecasting model can reach 85%. This model proficiently learns patterns from mature blocks and effectively applies them to optimize new blocks. Furthermore, expert validation confirms that the optimization results align closely with actual field conditions.
PAPER REVISED: 2023-08-28
PAPER ACCEPTED: 2023-11-21
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THERMAL SCIENCE YEAR 2024, VOLUME 28, ISSUE Issue 2, PAGES [1085 - 1090]
  1. Duplyakov, V., et al., Practical Aspects of Hydraulic Fracturing Design Optimization Using Machine Learning on Field Data: Digital Database, Algorithms and Planning the Field Tests, Proceedings, SPE Symposium: Hydraulic Fracturing, SPE, Virtual, Russia, 2020
  2. Cao, Q., et al., Data-Driven Production Forecasting Using Machine Learning, Proceedings, SPE Argenti­na Exploration and Production of Unconventional Resources Symposium, Buenos Aires, Argentina, 2016
  3. Zhou, Q., et al., Evaluating Gas Production Performances in Marcellus Using Data Mining Technologies, Journal of Natural Gas Science and Engineering, 20 (2014), 2, pp. 109-120
  4. Lolon, E., et al., Evaluating the Relationship Between Well Parameters and Production Using Multivar­iate Statistical Models: A Middle Bakken and Three Forks Case History, Proceedings, SPE Hydraulic Fracturing Technology Conference and Exhibition, SPE, Woodlands, Tex., USA, 2016
  5. Pankaj, P., et al., Application of Data Science and Machine Learning for Well Completion Optimization, Proceedings, Offshore Technology Conference, Houston, Tex., USA, 2018
  6. Clar, F. H., et al.,Data-Driven Approach to Optimize Stimulation Design in Eagle Ford Formation, Pro­ceedings, Unconventional Resources Tech. Conf., Denver, Cal., USA, 2019, pp. 4317-4336
  7. Berneti, S. M., et al., An Imperialist Competitive Algorithm Artificial Neural Network Method to Predict Oil Flow Rate of The Wells, International Journal of Computer Applications, 26 (2011), 10, pp. 47-50
  8. Chakra, N. C., et al., An Innovative Neural Forecast of Cumulative Oil Production from a Petroleum Reservoir Employing Higher-Order Neural Networks (HONN), Journal of Petroleum Science and Engi­neering, 106 (2013), 2, pp. 18-33
  9. Sheremetov, L., et al., Data-Driven Forecasting of Naturally Fractured Reservoirs Based on Non-Linear Autoregressive Neural Networks with Exogenous Input, Journal of Petroleum Science and Engineering, 123 (2014), 3, pp. 106-119
  10. Aizenberg, I., et al., System Identification Using FRA and a Modified MLMVN with Arbitrary com­plex-Valued Inputs, Proceedings, Int. Joint Conf. on Neural Networks, Vancouver, Canada, 2016, pp. 4404-4411
  11. Wang, T., et al., Productivity Prediction of Fractured Horizontal Well in Shale Gas Reservoirs with Ma­chine Learning Algorithms, Applied Sciences, 11 (2021), 2, ID12064
  12. Shi, Y., et al., Productivity Prediction of a Multilateral-Well Geothermal System Based On a Long Short- Term Memory and Multi-Layer Perceptron Combinational Neural Network, Applied Energy, 282 (2021), 2, ID116046

© 2024 Society of Thermal Engineers of Serbia. Published by the Vinča Institute of Nuclear Sciences, National Institute of the Republic of Serbia, Belgrade, Serbia. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International licence