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Abstract

Summary

Pressure transient analysis (PTA) is a standard tool for interpreting reservoir response in well pressure to production and injection operations and characterizing reservoirs in subsurface engineering. It involves studying flow regimes and the associated pressure changes to extract information about well and reservoir parameters. New opportunities to support reservoir decision-making have opened with more abundant availability of PTA data. A wide deployment of permanent downhole gauges (PDG) in wells provided the wealth of high-frequency data for PTA and has encouraged researchers to explore the integration of PTA into current well monitoring and reservoir evaluation and modelling techniques.

This paper describes an automated method designed and developed to detect and classify reservoir flow regime features using pressure transient data obtained from production and injection wells. The proposed is focused on feature recognition model and solving the optimization problem for pattern classification. The method is applied and validated on real field data. The method presented in this study demonstrated a high level of accuracy, successfully identifying flow regime features with an 89% success rate based on a single pressure transient within the chosen real well dataset.

© EAGE Publications BV
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/content/papers/10.3997/2214-4609.202335019
2023-11-27
2026-02-14

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References

  1. Allain, O., Houze, O. P. (1992). A Practical Artificial Intelligence Application in Well Test Interpretation.European Petroleum Computer Conference. SPE-24287-MS. doi:10.2118/24287‑MS
     https://doi.org/10.2118/24287-MS [Google Scholar]
  2. Al-Maraghi, A. M., El-Banbi, A. H. (2015). Automatic Reservoir Model Identification using Artificial Neural Networks in Pressure Transient Analysis.SPE North Africa Technical Conference and Exhibition. SPE-175850-MS. doi:10.2118/175850‑MS
     https://doi.org/10.2118/175850-MS [Google Scholar]
  3. Bourdet, D. (2002). Well Test Analysis: The Use of Advanced Interpretation Models. Elsevier.
     [Google Scholar]
  4. Freites, A., Corbett, P., Rongier, G. (2023). Automated Classification of Well Test Responses in Naturally Fractured Reservoirs Using Unsupervised Machine Learning.Transp Porous Med147. doi:10.1007/s11242‑023‑01929‑1
     https://doi.org/10.1007/s11242-023-01929-1 [Google Scholar]
  5. Guyaguler, B., Horne, R. N. and Tauzin, E. (2001). Automated Reservoir Model Selection in Well Test Interpretation.SPE Annual Tech. Conf. & Exhib. SPE-71569-MS. doi:10.2118/71569‑MS
     https://doi.org/10.2118/71569-MS [Google Scholar]
  6. Namazova, G., Molina, J., Shchipanov, A. (2021). Evaluation of Well Interference and Injection Performance from Analysis of Time-lapse Pressure Transients.82nd EAGE Annual Conf. & Exhib. doi:10.3997/2214‑4609.202113102
     https://doi.org/10.3997/2214-4609.202113102 [Google Scholar]
  7. Shchipanov, A.A., Berenblyum, R.A., Kollbotn, L. (2014). Pressure Transient Analysis as an Element of Permanent Reservoir Monitoring.SPE Annual Tech. Conf. & Exhib. SPE-170740 MS. doi:10.2118/170740‑MS
     https://doi.org/10.2118/170740-MS [Google Scholar]
  8. SuzukiS., (2018). Using Similarity-Based Pattern Detection to Automate Pressure Transient Analysis.Abu Dhabi International Petroleum Exhibition & Conference. SPE-193285-MS. doi:10.2118/193285‑MS
     https://doi.org/10.2118/193285-MS [Google Scholar]
/content/papers/10.3997/2214-4609.202335019
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Unsupervised Classification of Flow Regime Features in Pressure Transient Responses
Conference Proceedings 2023, 1 (2023); https://doi.org/10.3997/2214-4609.202335019
/content/papers/10.3997/2214-4609.202335019
/content/papers/10.3997/2214-4609.202335019
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