Feature Extraction and Pattern Recognition in Time-lapse Pressure Transient Responses – a new paper published in Geoenergy Science and Engineering

Posted on August 15, 2024

Feature Extraction and Pattern Recognition in Time-lapse Pressure Transient Responses, by V. Starikov, A. Shchipanov, V. Demyanov, K. Muradov, was accepted for publication and available online with Geoenergy Science and Engineering. The publication is one of the outcomes of AutoWell project –  Automated Well Monitoring and Control, 2022-2025,  PETROMAKS2 funded project  supported by Research Council of Norway, ConocoPhillips, Sumitomo, Wintershall Dea and Aker BP.
R&D partners: NORCE, University of Stavanger and Heriot-Watt University.

The paper introduces a novel PTA-feature extraction and pattern recognition methods for analysis of time-lapse pressure transient responses and their Bourdet derivatives. The pattern recognition exploits unsupervised classification to extract PTA-features, associated with different flow regimes commonly used in PTA. Each pressure transient in a time-lapse series is first processed by an autonomously fine-tuned algorithm that measures the signal’s distance to an ensemble of physically meaningful responses defined by PTA-feature library, thus breaking the transient into a series of likely PTA-features. A set of hyperparameters is used in the unsupervised classification, where an optimization procedure is employed for automated tuning of the hyperparameters to a particular transient. Subsequently, recognition of underlying patterns governed by sequences of these PTA-features in the time-lapse transient responses is performed. Testing of the combination of these new methods through synthetic and field cases is further carried out with verification via comparison with expert’ interpretation results. Added value to the previously introduced PTA metrics, which provide on-the-fly well and reservoir performance analysis, is finally demonstrated. The proposed pattern recognition method improves reliability and automates the calculation of the PTA metrics.

The developed methodology serves as a new tool for knowledge extraction from big well monitoring datasets, available in the companies operating in the oil and gas industry, as well as the emerging industries such as carbon capture and storage and geothermal energy production. The article concludes with a discussion of the main advantages and limitations of the suggested feature extraction and pattern recognition methods. Besides the combined use of the methods described in this article, these methods may also be integrated with conventional physics-based approaches widely used in the industry for well data interpretation and reservoir simulations, improving their performance and efficiency for big data sets.
Figure 13

Unsupervised Model vs. Expert flow regime detection on real data: Horizontal injector (a) and Horizontal producer (b)

View the highlights of the work in a IGE GeoEnergy seminar talk by Vitaliy Starikov: Feature Extraction and Pattern Recognition in Time-lapse Pressure Transient Responses 

The proposed method capitalises on the novel methodology for transient identification developed within AutoWell project. LMIR/TPMR methods identify transients from PTA data, then the proposed unsupervised pattern recognition algorithm detects the sequences flow regime intervals in the identified transients.