Statistical analysis of offshore production sensors for failure detection applications / Análise estatística dos sensores de produção offshore para aplicações de detecção de falhas

Abstract

Detecting the early stages of failures is an old concern of petroleum industry. In order to tackle this problem, a novel sensor analysis methodology is proposed. The assessment of production sensors' behavior, individually or in a group, leads to a better understanding of failure modes during oil and gas production. Thus, Principal Components Analysis and Logistic Regression are incorporated as multivariate statistical modeling for studying the impact of different anomalies in production sensors. Therefore, a deep statistical analysis of these sensors can strengthen assumptions for supporting the modeling process of early fault detection systems. Based on a reliable public data set containing data from real wells, the application of the PCA approach combined with a Logistic Regression resulted in better visualization and understanding of some failures that occurred during petroleum production, such as the abrupt increase in BSW (Basic sediment and water), spurious closure of DHSV (Down hole Safety Valve), severe slugging, flow instability, productivity loss, quick restriction in PCK (production choke), scaling in PCK and hydrate formation in production lines. The two statistical approaches were used as a combined method to provide useful information regarding the failure modes in the dataset. Also, the dataset presented two classes that are important for anomaly detection in oil wells: “normal” and “abnormal”, which allow detecting when production is outside its normal condition. Then, using the production sensors analysis with failure data can help to formulate better detection algorithms. By using PCA and Logistic Regression it was possible to identify which set of variables is better for detecting a specific type of problem. The application of these techniques boosts the modeling of early detection systems in oil and gas production. Besides, the assumptions led to conclusions about how to put groups of sensors and abnormalities together and how much time a well stands in a steady normal condition. Other conclusions showed the significance of transient information for fault detection modeling and the need for individual wells analyses. Hence, using PCA for treating and transforming the data brings important contributions for early fault detection modeling, once it allowed insight into how sensors and abnormal events can be related. Consequentially, the present paper has significant novelty contribution: it raises important assumptions that help to build solid knowledge about the anomalies behavior and help researchers to implement a better modeling strategy.