Among the AID/APOBECs -a family of DNA and RNA deaminases-APOBEC1 physiologically partakes into a complex that edits a CAA codon into UAA Stop codon in the transcript of Apolipoprotein B (ApoB), a protein crucial in the transport of lipids in the blood. Catalytically inactive mutants of APOBEC1 have a dominant negative effect on its activity, as they compete for the targeting to the ApoB mRNA. Here we show that catalytically inactive chimeras of APOBEC1 restricted to different compartments of the cell present different abilities to titrate APOBEC1-mediated RNA editing, and that the ability of APOBEC1 to interact with these mutants is the main determinant for its activity. Our results demonstrate that dimerisation, a feature common to other APOBECs targeting DNA, is not required for APOBEC1 activity on mRNA. Furthermore, APOBEC1-mediated RNA editing is a dynamic process where interplay among the components of the editing complex is regulated through the balance between availability of A1CF, one of APOBEC1 cofactors, and nuclear degradation of APOBEC1.
This paper highlights the development and results of an innovative tool for prediction of process upsets and hazard events associated with production operations of an oil and gas field. Summarily, this software can give recommendations on actions to mitigate or avoid operational issues, maximizing the asset value, while maintaining the highest safety and environmental quality. This in-house developed tool is based on big data analytics techniques such as machine and deep learning algorithms. The workflow developed allows predicting future events and the related influencing variables. This is done thanks to a powerful machine-learning algorithm specifically selected for the physical problem analyzed. The inputs come from a heterogeneous data-lake, composed by historical data, real-time series, maintenance reports, chemical analysis and operator experience. The workflow developed starts processing and enhancing this huge amount of data in order to train and validate the selected algorithm. Finally, the tool is fed with real-time data from the field, predicting potential events and prescribing possible actions to avoid problems that jeopardize the production and the integrity of the asset. The tool has demonstrated the capability to predict in advance operational upsets occurring within the entire production system avoiding issues, maximizing the field availability. The case illustrated in this paper focuses the attention on the process section of an upstream oil field. In particular, process upsets of the sweetening unit, such as H2S out of specification, are analyzed since they affect not only the field production, but also the asset integrity and the environmental emissions. Several Big Data Analytics have been tested and presented in this paper, along with different methodologies of input-data pre-conditioning. Results related to the application of the tool on normal operations show a significant impact in terms of down-time reduction and production optimization. The possibility to have alerts and information a few hours in advance gives to the operator the ability to reach the asset operational target, which is not only related to the management of critical events but also to the achievement of the maximum level of production thanks to the definition of an optimal configuration of operating parameters. The tool highlights also the main parameters affecting the prediction suggesting corrective actions to prevent and mitigate risks and occurring critical events. The innovative characteristics of the tool are the ability to take advantage of a huge amount of field data and to simulate complex phenomenon through mathematical-statistical methodologies, based on machine learning algorithms. Thanks to this innovative approach, it is possible to quickly predict possible hazardous events and consequently find the optimum asset configuration. This produces positive effects in the field short-term production optimization and the long-term maintenance strategies, maximizing its value and minimizing associated risks.
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
Energy companies are latecomers to digitization with respect to other business, but new technologies like Big Data, Cloud infrastructure and Artificial Intelligence offer great opportunities. Here we present an integrated approach to the digitalization of an O&G plant aiming to offer operator safety enhancement, production optimization and reduction of the environmental impact to maximize the asset value. This has been accomplished by complex and continuous work powered by the people who are the engine and the real target of the digital transformation process. In the key study hereby presented, an all-round effort has been made to empower the operator's everyday work with digital and innovative tools supporting reservoir, maintenance, production and HSE workflow. Starting from a number of various legacy systems, a single integrated dashboard was built: The Integrated Operation Centre (IOC). IOC is now available on PC and smartphones to all site personnel both at the operational and managerial level. New innovative systems were developed and deployed into IOC to capitalize on the data acquired during years of plant activities. Machine learning and advanced analytics solutions provide new daily insight on how to efficiently schedule maintenance operations and avoid off-specs and downtime on critical equipment, while complex production optimizers help technicians react to unexpected situations and maximize production. Via IoT (Internet of Things) and portable devices, new tools and workflows were deployed onsite to ease the work and enhance the safety of workers with focus on usage of PPE and providing rapid information to locate workers during emergency situations. People from both site and company headquarters ensured the success of the digital transformation by working together in an Agile Method during the development phase and by coaching in the roll-out phase. New professional roles, like data scientist and big data engineers, joined effort with experienced operators to ensure the success of this journey. This cooperation was at the basis of a comprehensive change management effort, which ensured a smooth and constant change in the way the personnel thinks, acts and reacts. This, we believe, is at the very heart of any fundamental transformation, being it digital or not.
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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