Version 1
: Received: 22 October 2023 / Approved: 23 October 2023 / Online: 23 October 2023 (08:08:27 CEST)
How to cite:
Sun, Y.; Wang, J.; Wang, T.; Li, J.; Wei, Z.; Fan, A.; Liu, H.; Chen, S.; Zhang, Z.; Chen, Y.; Huang, L. Post-Fracture Production Prediction with Production Segmentation and well logging: Harnessing Pipelines and Hyperparameter Tuning with GridSearchCV. Preprints2023, 2023101410. https://doi.org/10.20944/preprints202310.1410.v1
Sun, Y.; Wang, J.; Wang, T.; Li, J.; Wei, Z.; Fan, A.; Liu, H.; Chen, S.; Zhang, Z.; Chen, Y.; Huang, L. Post-Fracture Production Prediction with Production Segmentation and well logging: Harnessing Pipelines and Hyperparameter Tuning with GridSearchCV. Preprints 2023, 2023101410. https://doi.org/10.20944/preprints202310.1410.v1
Sun, Y.; Wang, J.; Wang, T.; Li, J.; Wei, Z.; Fan, A.; Liu, H.; Chen, S.; Zhang, Z.; Chen, Y.; Huang, L. Post-Fracture Production Prediction with Production Segmentation and well logging: Harnessing Pipelines and Hyperparameter Tuning with GridSearchCV. Preprints2023, 2023101410. https://doi.org/10.20944/preprints202310.1410.v1
APA Style
Sun, Y., Wang, J., Wang, T., Li, J., Wei, Z., Fan, A., Liu, H., Chen, S., Zhang, Z., Chen, Y., & Huang, L. (2023). Post-Fracture Production Prediction with Production Segmentation and well logging: Harnessing Pipelines and Hyperparameter Tuning with GridSearchCV. Preprints. https://doi.org/10.20944/preprints202310.1410.v1
Chicago/Turabian Style
Sun, Y., Yuanyuan Chen and Lei Huang. 2023 "Post-Fracture Production Prediction with Production Segmentation and well logging: Harnessing Pipelines and Hyperparameter Tuning with GridSearchCV" Preprints. https://doi.org/10.20944/preprints202310.1410.v1
Abstract
As the oil industry increasingly turns to unconventional low-permeability and low-porosity reservoirs, precise post-fracture production prediction is a pivotal instrument in investment de-cision-making, formulation of energy policies, and promotion of environmental assessments. Nevertheless, despite extensive research spanning decades, the precise post-fracture production prediction based on logging parameters remains an intricate task. In this study, we gathered ex-tensive logging data and segmented the post-frac gas production during well testing on the first day to enrich the dataset. Nine pipelines were then architected using various techniques of data preprocessing, feature extraction, and advanced machine learning models. Hyperparameter op-timization was executed via the GridsearchCV. To assess the efficacy of diverse models, metrics including the coefficient of determination (R2), standard deviation (SD), and root mean square error (RMSE) were invoked. Among the several pipelines explored, the PS-NN exhibited excellent predictive capability in specific reservoir contexts (an R2 value of 0.94 and an RMSE of 48.15). In essence, integrating machine learning with logging parameters effectively assesses reservoir productivity at multi-meter formation scales. This strategy not only mitigates uncertainties en-demic to reservoir exploration but also equips petroleum engineers with monitoring on reservoir dynamics, thereby facilitating reservoir development. Additionally, this approach provides res-ervoir engineers with an efficient way of reservoir performance oversight.
Keywords
Post-fracture production prediction; Production segmentation; well logging; Pipelines; GridSearchCV; Machine learning
Subject
Environmental and Earth Sciences, Geochemistry and Petrology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
