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Electric Submersible Pump (ESP) account for over 60% of artificial lift methods used globally and contribute significantly to the CAPEX and OPEX of a project. They tend to be the least reliable component in the system with an average life-span of 2 years. This paper demonstrates how artificial intelligence was used to unlock insights from sensor data around an ESP to understand the operating conditions which lead to a trip and failure of these systems. Autoencoders were used for the detection of anomalous behavior in an ESP and the determination of the root cause of an anomalous event. Autoencoders are neural networks trained to reconstruct input data. They have an encoding and decoding section, the encoder compresses the input vector, while the decoder reconstructs the original input from the compressed vector. This process allows the network to understand the patterns in a dataset. We trained the network on stable operating data from a 2-years historical data dump of 97 sensors. This allowed the model to understand the patterns of stability in an ESP. The autoencoder was developed using the Python programming language along with the Keras deep learning framework. It had 7 layers with the exponential linear unit as the activation function for training. During reconstruction, the autoencoder never produces a perfect reconstruction of input data, it, however, performs a good reconstruction on data similar to what it was trained on. In our case, the model reconstructs stable data well and struggles with unstable data. The reconstruction error is used to distinguish a normal event from an anomalous event because it increases prior to an event and reduces as the system returns to stability. During the historical time period, the ESP experienced 5 major trips, three of them were due to gas locks while the other two were due to electrical issues. The model was able to detect the gas locks on average 5 hrs in advance and electrical issues several days in advance before the actual events. The top ten sensors responsible for each event were determined based on the relative magnitude of the individual sensor reconstruction errors, the validity of this output was confirmed by the Subject Matter Expert. Autoencoders can make non-linear correlation between features in a dataset and have been used for anomaly detection in images and other fields, this paper demonstrates their usefulness in intelligent surveillance of ESPs. This solution is currently used for near real-time intelligent surveillance of ESPs with the ability to send out email notifications whenever any sensor strays away from stability.

Unintended loss of uptime (trips) in gas compression systems is one of the top causes for unscheduled deferment across hydrocarbon production facilities. Compression failures and the deferments they cause have been at similar levels for the past 5–10 years. Causes for compressor failures could be attributed to lack of or inappropriate maintenance, incorrect operating practices and integrity issues, as identified in the Oil and Gas UK compressor study. The focus of this research paper is on compressor systems on production facilities that have major production deferments associated with them. In this paper, an advanced machine learning approach is presented for determining anomalous behavior to predict a potential trip and probable root cause with sufficient warning to allow for intervention. One class support vector machines (SVM) and rate of change based outlier detection have been utilized to classify abnormal operation and detect the specific variables contributing to instability respectively. Development of the algorithms started with data from more than 2,000 sensors on the low-pressure compressor as well as processes tied to the compressor. This initial data set was reduced to more relevant tags by feature selection methodologies. Two separate, one class SVM models were then trained on one years normal working data to identify abnormal behavior considering the multivariant approach. An outlier detection algorithm was developed to identify and rank major contributors for potential faulty behavior of the compressor. The algorithms are trained and tested in R and a near real-time, online implementation is scheduled using Alteryx platform which provides new predictions every 10 minutes. The results are then visualized on a Spotfire dashboard and when initiated, the model flags abnormal behavior via automated email to the end user. At present, the algorithms have improved the identification efficiency with a mediam detection time of seven hours. With upper detection time as high as few days, investigation and remedial action is possible. As this field progresses and identification time increases, the application of machine learning for compressor failure has potential to revolutionize maintenance strategies and mitigate against the now-periodic downtime of compressors across the industry. Current efforts to identify anomalous compressor behavior and degradation of performance are limited to traditional exception based surveillance, using pre-determined limits and manual univariate observation of critical compressor variables. This paper presents application of scalable machine learning as more advanced methods of failure classification. We can now utilize the vast catalog of historical and real-time data to build smart algorithms allowing engineers to go beyond basic anomaly detection mechanisms. This predictive maintenance approach has huge potential to save production deferments caused by downtime associated with rotating equipment failures.

Microparticle separation process has a variety of application varying from application in biological and biomedical industries for analysis and diagnosis, in biogas manufacturing to separate phases as well as in defense sector for detection of biological weapons like anthrax. Available electrical, magnetic, acoustic and various other methods are either very costly or not portable. The proposed design of micro scale cyclone separator is low cost as well as portable and easy to manufacture. Huge cyclone separators are widely used in various industries since decades but due to lack of research in micro scale cyclones no direct and sufficient data is available. This research attempts to develop a microscale cyclone separator and study the effect of parameters like inlet velocity on pressure drop in a micro scale cyclone separator. It further studies the effect of particle size on collection efficiency through Computational Fluid Dynamics (CFD) approach. CFD analysis has been proved very efficient for calculations in larger cyclones and hence is used as a tool in this study as well, though experimental verification is recommended. Computational experiments were performed using FLUENT. The results obtained are compared with various empirical relations developed for huge cyclone separators and similarities and dissimilarities in trends are analyzed. Finally a multi-cyclone model is proposed to obtain higher collection efficiency.

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