Post-pressure monitoring and evaluation of horizontal wells using pressure-based analysis

i

This article has been updated. A Correction to this article was published on .

i

The Original Article was published on

i

The Original Article was published on

i

This article has been updated

i

This article was retracted on

i

This article has been updated

Abstract

Horizontal well post-pressure monitoring is important in testing wellbore integrity, reservoir response, and stability after interventions involving pressure. However, existing pressure-based evaluation methods lack systematic integration to capture transient behavior and translate pressure dynamics into actionable well condition assessments. The quantitative description of post-pressure behavior is aided by smoothed pressure trends, rolling variance, and derived stability indices, whereas the statistical approach to detect abnormal pressure deviations is based on the Z-score. This study optimized horizontal well monitoring, supporting sustainable energy by enhancing production efficiency and minimizing risks. A key finding of this study is the development of a clear rule-based decision framework that transforms pressure-derived metrics into operationally meaningful well condition states (stable, warning, and risk), shifting into a quantitative analysis and field-level decision-making. The proposed framework was confirmed with real post-pressure information from a horizontal oil well (Well No. 807). The proposed framework contributes to sustainable energy and environmental solutions by enabling reliable post-pressure well monitoring, improving operational efficiency, reducing intervention risks, and supporting optimized resource utilization in horizontal well systems. The results indicate a clear transition from initial transient pressure behavior to a stable equilibrium state, with approximately 65% in the stable category, 25% in the warning category, and 10% in the risk category. In addition, the role of visual imaging logging is considered at a conceptual level due to the absence of real imaging data, intended to support future interpretation of pressure anomalies.

Keywords

horizontal wells / post-pressure monitoring / pressure analysis / anomaly detection / stability index / rule-based classification

CHANGE HISTORY

A Correction to this paper has been published:
https://doi.org

The Retraction Note was updated to reflect reception of statements of disagreement from all authors.

This article has been retracted.Please see the Retraction Notice for more detail:
https://doi.org

References

[1] Luo L, Abdulhameed D, Tao G, et al. (2025) Large-scale experimental validation of real-time monitoring in underground gas storage wells using distributed fiber optic sensing. IEEE Access 13: 19523-19532. https://doi.org/10.1109/ACCESS.2025.3533389

[2] Ce W, Shanbin H, Mingyu W, et al. (2022) Density cutting volume fracturing technology for horizontal wells in the ultra-low permeability reservoirs: A case study of the Yuan284 Area in the Huaqing Oilfield Ordos Basin, China. Arab J Geosci 15: 1409. https://doi.org/10.1007/s12517-022-10657-7

[3] You L, Xie Y, Chen Y, et al. (2025) Development directions of formation damage evaluation and fracturing fluids in tight and shale oil reservoirs. Petroleum 11: 125-142. https://doi.org/10.1016/j.petlm.2025.02.004

[4] Li YL, Li XH, Chen ZY, et al. (2025) Experimental study on pressure response characteristics of parent wells during frac hits. Geosyst Eng 1-15. https://doi.org/10.1080/12269328.2025.2561024

[5] Miao W, Wang X, Zhang W, et al. (2025) Gas sources and productivity-influencing factors of matrix reservoirs in Xujiahe Formation-A case study of Xin 8-5H Well and Xinsheng 204-1H Well. Processes 13: 2644. https://doi.org/10.3390/pr13082644

[6] Liu X, Li D, Jia Y, et al. (2023) Optimizing construction parameters for fractured horizontal wells in shale oil. Front Earth Sci 10: 1015107. https://doi.org/10.3389/feart.2022.1015107

[7] Wen H, Wu J, Zhu Y, et al. (2025) Production prediction method for deep coalbed fractured wells based on multi-task machine learning model with attention mechanism. Processes 13: 1787. https://doi.org/10.3390/pr13061787

[8] Lei Q, Xu Y, Cai B, et al. (2022) Progress and prospects of horizontal well fracturing technology for shale oil and gas reservoirs. Pet Explor Dev 49: 191-199. https://doi.org/10.1016/S1876-3804(22)60015-6

[9] Li J, Zhou X, Gayubov A, et al. (2023) Study on production performance characteristics of horizontal wells in low permeability and tight oil reservoirs. Energy 284: 129286. https://doi.org/10.1016/j.energy.2023.129286

[10] Feng R, Li C, Sun L, et al. (2024) Study on the influence of deep coalbed methane horizontal well deployment orientation on production. Energies 17: 5784. https://doi.org/10.3390/en17225784

[11] Meng L, Zhang X, Jin Y, et al. (2025) Prediction model and real-time diagnostics of hydraulic fracturing pressure for highly deviated wells in deep oil and gas reservoirs. Sci Rep 15: 3559. https://doi.org/10.1038/s41598-025-88027-y

[12] Liang J, Luo M, Li W, et al. (2025) Machine learning enhanced formation pressure prediction using integrated well logging and mud logging. Sci Rep 15: 23357. https://doi.org/10.1038/s41598-025-07048-9

[13] Li L, Xie M, Liu W, et al. (2024) A new productivity evaluation method for horizontal wells in offshore low-permeability reservoir based on modified theoretical model. Processes 12: 2830. https://doi.org/10.3390/pr12122830

[14] Luo R, Liu J, Yang D, et al. (2023) Application and analysis of array production logging technology for multiphase flow in horizontal wells. Processes 11: 3421. https://doi.org/10.3390/pr11123421

[15] Kang Z, Li X, Qin H, et al. (2025) Novel logging while drilling azimuthal laterolog resistivity instrument design for oil-based mud. J Geophys Eng 22: 179-192. https://doi.org/10.1093/jge/gxae118

[16] Li N, Wang K, Lu J, et al. (2024a) First successful downhole testing of the permeability logging prototype. J Geophys Eng 21: 1179-1182. https://doi.org/10.1093/jge/gxae067

[17] Li N, Liu P, Wu H, et al. (2024b) Development and prospect of acoustic reflection imaging logging processing and interpretation method. Pet Explor Dev 51: 839-851. https://doi.org/10.1016/S1876-3804(24)60510-0

[18] Song H, Xia T, Li M, et al. (2025) Interpretation method of multiple array production logging based on machine learning in a horizontal well. J Geophys Eng 22: 1300-1314. https://doi.org/10.1093/jge/gxaf065

[19] Fernandes W, Komati KS, De Souza Gazolli KA (2024) Anomaly detection in oil-producing wells: A comparative study of one-class classifiers in a multivariate time series dataset. J Pet Explor Prod Technol 14: 343-363. https://doi.org/10.1007/s13202-023-01710-6

[20] He Y, Zhang H, Wu Z, et al. (2025) Porosity prediction of tight reservoir rock using well logging data and machine learning. Sci Rep 15: 13124. https://doi.org/10.1038/s41598-025-95578-7

[21] Al-alimi A, Rafiei Y, Ameri MJ, et al. (2025) Surface based real time monitoring in horizontal smart wells eliminates downhole sensors. Sci Rep 15: 41309. https://doi.org/10.1038/s41598-025-25193-z