A Data Driven Management Strategy to Reduce the Environmental Impact of Upstream Production Plants

Cadei, Luca; Loffreno, Danilo; Camarda, G.; Montini, Marco; Rossi, Gianmarco; Fier, P.; Lupica, D. S.; Corneo, Andrea; Lancia, Lorenzo; Milana, Diletta; Carrettoni, M.; Purlalli, Elisabetta; Carduccu, Francesco; Sophia, Gustavo

doi:10.2118/195615-ms

Cited by 8 publications

(1 citation statement)

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“…For example, [3] highlights the advantages of employing data-driven methods to detect and prevent undesired operational conditions in an oil and gas producing facility. Since energy efficiency and environmental impact are key issues in the oil and gas industry, [4] shows the benefit of the adoption of a predictive model in forecasting the Stationary…”

Section: Related Workmentioning

confidence: 99%

Supervised Anomaly Detection in Crude Oil Stabilization

et al. 2022

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In recent years, pervasive digitalization has affected the industrial world, including the oil and gas sector. With more and more data becoming available, Machine Learning algorithms have become a promising tool to improve Predictive Maintenance operations. In this work, we have designed an alerting system that notifies the site operator with an adequate advance when an anomaly is going to occur. In particular, we focus our analysis on the stabilization column of an Oil Stabilization Facility to prevent the column bottom temperature to overcome safety boundaries. The experimental analysis demonstrates that our system provides reliable results, in terms of both identified anomalies and false alarms. In addition, the system is currently under deployment on the company computing infrastructure and the first working version will be available by the end of May 2022.

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Section: Related Workmentioning

confidence: 99%

Supervised Anomaly Detection in Crude Oil Stabilization

et al. 2022

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Advanced Analytics for Predictive Maintenance with Limited Data: Exploring the Fouling Problem in Heat Exchanging Equipment

Cadei

Corneo

Milana

et al. 2019

Day 3 Wed, November 13, 2019

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The current oil and gas market is characterized by low prices, high uncertainties and a subsequent reduction in new investments. This leads to an ever-increasing attention towards more efficient asset management. The fouling effect is considered one of the main problems drastically affecting asset integrity/efficiency and heat exchanger performances of critical machineries in upstream production plants. This paper illustrates the application of advanced big data analytics and innovative machine learning techniques to face this challenge. The optimal maintenance scheduling and the early identification of workflow-blocking events strongly impact the overall production, as they heavily contribute to the reduction of down-times. While, machine learning techniques proved to introduce significant advantages to these problems, they are fundamentally data-driven. In industry scenarios, where dealing with a limited amount of data is standard practice, this means forcing the use of simpler models that are often not able to disentangle the real dynamics of the phenomenon. The lack of data is generally caused by frequent changes in operating conditions/field layout or an insufficient instrumentation system. Moreover, the intrinsic long duration of many physical phenomena and the ordinary asset maintenance lifecycle, cause a critical reduction in the number of relevant events that can be learned. In this work, the fouling problem has been explored leveraging only limited data. The attention is focused on two different equipment: heat exchangers and re-boilers. While the formers involve slower dynamics, the latter are characterized by a steady phase followed by an abrupt deterioration. Moreover, the first ones allow a proper scheduling of cleaning interventions in advance. On the other hand, the second forces a much quicker plant stop. Finally, heat exchangers are characterized by few episodes of comparable deterioration, while re-boilers present only a single episode. Regarding heat exchangers, a dual approach has been followed, merging a short-term, time-series-based model, and a long-term one based on linear regression. After having isolated a number of training regions related to the fouling episodes that showed a characteristic behavior, it is possible to obtain accurate results in the short-term and to capture the general trend in the long-term. In the case of re-boilers, a novelty detection approach has been adopted: first, the model learns the equipment normal behavior, then it uses the features learned to detect anomalies. This continuous training-predicting iteration also leverages the user feedback to adapt to new operating conditions. Results show that in an "young digital" industry, the use of limited data together with simpler machine learning techniques, can successfully become an automatic diagnostics tool supporting the operators to improve traditional maintenance activities as well as optimize the production rate, and finally the asset efficiency

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A Business-Oriented Framework to Evaluate Advanced Analytics for Predictive Maintenance: Measuring Benefits-Effort Tradeoff

Cadei

Corneo

Milana

et al. 2019

Day 3 Wed, November 13, 2019

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The use of advanced analytics techniques has become pivotal for the Digital Transformation of the Oil and Gas Industry. Most of these models are used to predict and avoid the off-spec behaviors of both equipment and functional units of the plant and also for predicting overshooting events in advance allows plant’s operators to avoid production down-time. From a Machine Learning perspective, predicting off-specs situation and peaks in time signal is a complex task, due to the great rarity of events. For the very same reason, using standard data science measures – like Area Under the Curve (AUC), Recall and Precision – can lead to misleading performance indicators. In fact, a model that predicts no off-spec would have a high AUC just because of the unbalanced classes, leading to many false alarms. In this paper we present a business-oriented validation framework for big data analytics and machine learning models applied to a upstream production plant. This allow to evaluate both the effort required to operators and the expected benefit that could be achieved. The validation metrics defined take the classical Data Science measures to the business domain. This allow to adapt the model to the very specific use case and end user addressing the specific upstream plants constraints. This framework allows to define the optimal tradeoff between effort required and preventable events, providing statistics and KPIs to evaluate it. Normalized Recall (NR) takes into account both the percentage of events intercepted and the effort required, in terms of Attention Time (AT), when the operator should pay attention to the equipment involved. Plant operators can now have an idea of the results they can achieve with respect to the maximum effort required. Moreover, to prove the goodness of the model, we defined the lift in the NR as the ratio of the model NR and the NR that would be obtained by randomly distributing the same number of alarms. We applied this framework to specific use cases obtaining an expected recall of 40-50% with an expected effort of 5-10% of the time (considering more than 6 months). The effort is actually lower, since the operator is not requested to be fully committed to the alarm. The innovative framework developed is able to demonstrate the real operating capability of the analytics implemented on field, highlighting both the effort required to operators and the accuracy of machine learning tools.

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A Data Driven Management Strategy to Reduce the Environmental Impact of Upstream Production Plants

Cited by 8 publications

References 1 publication

Supervised Anomaly Detection in Crude Oil Stabilization

Supervised Anomaly Detection in Crude Oil Stabilization

Advanced Analytics for Predictive Maintenance with Limited Data: Exploring the Fouling Problem in Heat Exchanging Equipment

A Business-Oriented Framework to Evaluate Advanced Analytics for Predictive Maintenance: Measuring Benefits-Effort Tradeoff

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