Self-Adjusting Anomaly Detection Model for Well Operation and Production in Real-Time

Snyder, Jon; Scott, Stuart L.; Kassim, Rashid

doi:10.2118/195234-ms

Cited by 4 publications

(1 citation statement)

References 4 publications

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“…It identifies unusual patterns in sensor data, enabling early detection of anomalies, preventing accidents and equipment failure, monitoring environmental variables, reducing risks, and proactively responding to suspicious activities. Data integrity and anomaly detection are crucial for preventing errors and cybersecurity breaches [1][2][3][4][5].…”

Section: A Relevance Of the Subjectmentioning

confidence: 99%

Applicability of Anomaly Detection Knowledge from Computer Science and Mathematics Publications to Oil and Gas Research

Chigarev

2024

Energy Systems Research

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Anomaly detection in equipment processes is crucial for the oil and gas sector. Algorithms for detecting anomalies in measured data are best understood in Computer Science and Mathematics. Therefore, a possible transfer of knowledge from the latter area to the former can have a profound impact. This paper explores the potential for the knowledge transfer by analyzing bibliometric data of Computer Science and Mathematics papers published in MDPI journals, as well as publications available on SPE search platform. The research shows that the main algorithms extensively studied in MDPI publications and found in SPE publications, which address the anomaly detection problem, include Random Forest, Support Vector Machine, Long-term Memory Method and Recurrent Neural Network. The main advantages and disadvantages of these methods are briefly described. This paper provides examples of classical and highly cited publications that describe the work of these algorithms along with examples of articles that illustrate their application in the oil and gas industry. The sections of SPE disciplines with the largest number of publications using the algorithms that are frequently used for anomaly detection are presented.

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Section: A Relevance Of the Subjectmentioning

confidence: 99%

Applicability of Anomaly Detection Knowledge from Computer Science and Mathematics Publications to Oil and Gas Research

Chigarev

2024

Energy Systems Research

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Systematic evaluation of abnormal detection methods on gas well sensor data

Tang

Wang

Zhu

et al. 2021

2021 IEEE Symposium on Computers and Communications (ISCC)

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Automated Real Time Anomaly Detection Model for Operation and Production Data at Scale

Fuad¹,

Demon²,

Husni³

2020

Day 1 Mon, November 09, 2020

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Oil and gas industry have evolved towards digitalization and data are fully utilized for decision making, cost optimization, improve in efficiency, and increase productivity. Upstream sector in oil & gas produce a huge number of operation and production data in a real-time platform. It is tedious process that somehow impractical and inefficient to quality check and analyze all available data manually (Subrahmanya et al., 2014). By using machine learning algorithm, this can be improved to automate data quality check at scale. On top of that, imputation can also be implemented to substitute on missing data and future forecast in real-time. In a case of this study, a huge data was collected from more than 30,000 tags/sensors in real-time. The real-time data were collected up to seconds and quality check need to be done up to each data collected. Firstly, each equipment tags/sensors had been checked and arranged with P&ID drawing. Then, API was developed with the real-time platform. In this project, percentile of machine learning was applied and developed to quality checked the operation and production time-series data at scale. Lastly, the process was customized to other offshore platforms in the field. In addition to automated data quality checking, machine learning algorithms were also used to calculate missing information based on the underlying relationship between data points. These approaches would reduce time needed to maintain quality and reliable data for further analysis and usage. As a result, percentile in machine learning successfully automate the process of data quality check for more productivity and efficiency. The percentile was applied to understand, validate, and monitor data at scale. Anomalies were detected in real time that allows operators to analyze further on any possibility in faulty, damage, or loss. All the outliers, missing or wrong data were also recorded and visualized in a dashboard. The model also provides additional statistic to define stale and bad data on top of automated define parameters. These features have improved efficiency of data acquisition and preparation. As conclusion, the model assists operator in monitoring daily operation and production data efficiently. Data quality and reliability is the key factor in asset management to ensure operator trust on produced data. The quality checked data could be utilized for further analysis, troubleshooting, and decision making.

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Self-Adjusting Anomaly Detection Model for Well Operation and Production in Real-Time

Cited by 4 publications

References 4 publications

Applicability of Anomaly Detection Knowledge from Computer Science and Mathematics Publications to Oil and Gas Research

Applicability of Anomaly Detection Knowledge from Computer Science and Mathematics Publications to Oil and Gas Research

Systematic evaluation of abnormal detection methods on gas well sensor data

Automated Real Time Anomaly Detection Model for Operation and Production Data at Scale

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