Improvement of flow coefficient estimation with limited well test data for real-time condition analytics of choke valve

• Main Authors: Visawameteekul, T., KAM, Tin Seong, Thamvechvitee, P.
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: Databases and Information Systems; Numerical Analysis and Scientific Computing
• Online Access: https://ink.library.smu.edu.sg/sis_research/9348; https://ink.library.smu.edu.sg/context/sis_research/article/10348/viewcontent/24OTCA_34759_4.pdf
• Institution: Singapore Management University

The study discusses a method for monitoring the internal condition of choke valves to predict sand erosion using flow coefficient (Cv). The method calculates the Cv of the choke valve by utilizing eight parameters and compares it to the newly manufactured value to generate warnings. However, the availability of spot-check well test data can significantly impact the model's efficiency if tests are performed infrequently. To address this issue, the Extended Cv monitoring method is proposed in this paper. The main purpose of this study is to develop a model for estimating Cv value in the absence of well test data. This will enhance the present Cv monitoring system, which currently only monitors the valve when the well is being tested. This study aims to bridge a gap in the Cv monitoring approach by evaluating wellhead operational data and dynamic well test data instead of relying on only static well test data. The proposed supplemental data capture dynamic features and are collected continually, which allows us to analyze the internal status of choke valves in a continuous manner. Three representative wells from the Greater Bongkot South asset are chosen as showcases for the study. The study result indicates promising results for choke valve real-time condition monitoring. The proposed method has been proven to enable online condition monitoring in the absence of well test data. By predicting valve condition, warnings can be generated to limit operation and prevent potential harm to plant integrity and personal safety. The Extended Cv monitoring method overcomes the limitation of the well test-based model, making it more efficient by utilizing continuously measured parameters data and employing machine learning techniques. This paper provides a useful reference for future studies to forecast Remaining Useful Life (RUL) of choke valve. The method presented in this study has the potential for expansion to other wells and has the potential to be applied in other industries facing similar issues. Overall, this study provides a valuable contribution to the development of methods for monitoring and predicting the internal condition of choke valves to address the challenges of sand production in the oil and gas industry.