Oil and Gas operators now have the possibility to collect and leverage significant amounts of data directly at the extremities of their production networks. Data combined with Industrial Internet of Things (IIoT) architecture is an opportunity to improve maintenance of assets, increase their up-time, reduce safety risks and optimize operational costs. However, to turn data into meaningful insights, Oil and Gas industry needs to fully take benefit of Machine Learning (ML) models which are able to consume real-time data and provide insights in isolated locations with scarce connectivity. These ML models need to be precise, robust and compatible with Edge computing capabilities.
This paper presents an analytics solution for rod pumps, capable of automated Dynagraph Card recognition at the wellhead leveraging an ensemble of ML models deployed at the Edge. The proposed solution does not require Internet connectivity to generate alarms and addresses confidentiality requirements of Oil and Gas industry. An overview of the employed ML models as well as the computing and communication infrastructure is given. We believe the given outline is insightful for the petroleum industry on its road to digitization and optimization of Artificial Lift systems.
Associative memories are data structures that allow retrieval of previously stored messages given part of their content. They, thus, behave similarly to the human brain's memory that is capable, for instance, of retrieving the end of a song, given its beginning. Among different families of associative memories, sparse ones are known to provide the best efficiency (ratio of the number of bits stored to that of the bits used). Recently, a new family of sparse associative memories achieving almost optimal efficiency has been proposed. Their structure, relying on binary connections and neurons, induces a direct mapping between input messages and stored patterns. Nevertheless, it is well known that nonuniformity of the stored messages can lead to a dramatic decrease in performance. In this paper, we show the impact of nonuniformity on the performance of this recent model, and we exploit the structure of the model to improve its performance in practical applications, where data are not necessarily uniform. In order to approach the performance of networks with uniformly distributed messages presented in theoretical studies, twin neurons are introduced. To assess the adapted model, twin neurons are used with the real-world data to optimize power consumption of electronic circuits in practical test cases.
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