Classification of Shale Oil Horizontal Well Production: A Case Study of the Chang-7 Interval in the Ordos Basin

Authors

  • Jiaming Liu
  • Yuechen Li
  • Yutong Li
  • Ruifei Wang

DOI:

https://doi.org/10.62051/ijnres.v7n1.02

Keywords:

Shale Oil; Horizontal Well; Classification and Evaluation; K-means Algorithm.

Abstract

This study analyzes 70 shale-oil horizontal wells in the Chang-7 interval of the Qingcheng Oilfield, Ordos Basin, using the K-means clustering algorithm to evaluate production capacity. The objective is to provide a foundation for differentiated development and refined management. First-year cumulative oil production was selected as the primary classification metric. The silhouette coefficient and elbow methods were used to determine the optimal cluster number. Results indicate that a three-class division is most appropriate: Class I wells (> 4,300 t, 21.7%), Class II wells (2,900-4,300 t, 40.8%), and Class III wells (< 2,900 t, 37.5%). Correlation analysis revealed distinct controlling factors for each well type. Class I wells are jointly influenced by engineering and geological conditions, with key parameters including drilled lateral length, proppant volume, and log-interpreted Class I intervals. Class II wells are primarily affected by engineering parameters and production practices, notably injected fluid volume, cluster number, pump rate, and shut-in duration. Class III wells are mainly governed by geological factors, with permeability, porosity, and average total organic carbon content being the most critical. The findings suggest that fracture-design and production strategies should be tailored to the specific controlling factors of each well type to enhance shale-oil development efficiency and achieve targeted exploitation. This approach offers a practical technical pathway and theoretical reference for classification management and optimization of shale-oil horizontal wells in the region.

References

[1] Li, E., Wang, Z., Guo, Y., Zhang, J., Bi, S., & Sun, B. (2025). Research on the synergistic inhibition of wax formation in shale oil system using efficient wax inhibitors: Experiments and mechanisms. Chemical Engineering Science, 121902.

[2] Chen, Y. J., Wang, H. Y., & Sharma, M. (2025). The benefits and challenges of well monitoring of Gulong shale oil. Earth Energy Science.

[3] Cao, J., Cai, M., Li, J., Guo, Z., Jiang, X., & Dong, G. (2025). Optimization of Volumetric Fracturing Stages and Clusters in Continental Shale Oil Reservoirs Based on Geology-Engineering Integration. Energies, 18(12), 3066.

[4] Liu, J., Wang, R., Song, P., Li, Y., & Zheng, S. (2025). Quantitative Characterization and Flow Simulation of Micropore Structure in Clastic Gas Reservoirs Based on Micron CT Scanning. ACS omega, 10(20), 20686-20700.

[5] Zhang, Z., Hu, J., & Zhang, Y. (2025). A semi-analytical model for fractured horizontal wells production considering imbibition during shut-in periods. Petroleum Science and Technology, 43(15), 1891-1909.

[6] Chaikine, I. A., & Gates, I. D. (2021). A machine learning model for predicting multi-stage horizontal well production. Journal of Petroleum Science and Engineering, 198, 108133.

[7] Liang, Z., Li, X., Zhou, H., Meng, L., Sun, A., Wu, Q., & Wen, H. (2025). Continental Shale Oil Reservoir Lithofacies Identification and Classification with Logging Data—A Case Study from the Bohai Bay Basin, China. Minerals, 15(5), 484.

[8] Chen, S., Wang, X., Li, X., Sui, J., Yang, Y., Yang, Q., ... & Dai, C. (2024). Geophysical prediction technology for sweet spots of continental shale oil: A case study of the Lianggaoshan Formation, Sichuan Basin, China. Fuel, 365, 131146.

Downloads

Published

03-09-2025

Issue

Section

Articles

How to Cite

Liu, J., Li, Y., Li, Y., & Wang, R. (2025). Classification of Shale Oil Horizontal Well Production: A Case Study of the Chang-7 Interval in the Ordos Basin. International Journal of Natural Resources and Environmental Studies, 7(1), 5-10. https://doi.org/10.62051/ijnres.v7n1.02