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.
As data processing capabilities improve electronic device performance, it becomes necessary for Oil and Gas operators to understand how computing power can be harnessed at the extremities of their production network by deploying Edge Analytics solutions.
This paper will discuss adapting the Industrial Internet of Things (IIoT) in the Upstream Automation domain, specifically in Artificial Lift assisted production, and will shed light on how Edge Analytics can be leveraged to deploy Machine Learning Models (MLMs) directly at the well-head.
This paper outlines the challenges and constraints related to deployment of Machine Learning solutions for rod pump abnormal states recognition and diagnosis at the wellhead. Those abnormal states may lead to a failure or to non-optimized production. Particular focus is on two main aspects: 1) Develop a robust Machine Learning model & IIoT architecture to predict rod pump failure directly at the wellhead, 2) Ensure high level of pump failure prediction through Machine Learning to ensure operator confidence.
To the best of our knowledge, this is the first-of-its-kind IIoT Edge Analytics solution which provides operators with the capability of automated Dynagraph Card recognition directly at the wellhead via Machine Learning models. This solution also addresses end-user requirements in terms of confidentiality and communication infrastructure.
With the advent of IIoT enabled Edge Analytics, and its ability to run Machine Learning based inference at the extremities of a production network, it has become essential to enable Operators and Subject Matter Experts to transfer their knowledge to Edge Computing Devices.
This paper discusses the application of Edge Analytics enabled Augmented Intelligence for wells operated by Electric Submersible Pumps, where Machine Learning and Pattern Recognition Models help detect anomalous events in multivariate time-series data. These Models runs on Edge Computing Devices where identify newly discovered and well known ESP performance patterns that can be labelled by a Subject Matter Expert. Once these patterns are identified and tagged, the Models are retrained and pushed back to the Edge Computing Device, where they continue to detect and predict patterns in real-time.
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