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There is an increased demand for contactless and/or low touch activities as well as a requirement for most product delivery and services to be such. This paper aims to demonstrate how drilling parameter optimisation in real-time provides a drilling team with an Edge based solution that can continuously improve performance and avoid problems without the need of subject matter experts. Results from testing the Edge-System on multiple wells from several operators are presented related to auto-calibration of real-time prediction models and for optimization of drilling parameters. This system based on cloud technology with Model based reasoning in Artificial Intelligence (AI) is able to give real-time and forward advice for operational parameters, ref SPE-204074-MS The key enabler for such system is "automatic" auto-calibration of models which is used for multiple forward-looking and what-if calculations to find optimal drilling parameters within the well envelope ahead. A simplified configuration has been made so that the rig-team can operate and maintain the system without the need for subject matter experts. "Automatic" Auto-calibration at stable conditions and/or during ramping conditions removes the need for subject matter experts. Such a system helps reduce operational costs as the rig-team can operate the system without the need of back-office support. Comparison will be made to document operational improvements.

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One of the most common issues faced in the Drilling industry is a Stuck Pipe situation. Stuck Pipes lead to huge losses in cost, energy and productive time. The objective of this paper is to predict stuck pipe incidents prior to their occurrence by harnessing the power of machine learning and deep learning models. Stuck incidents are some of the most difficult and challenging situations. The proposed method uses a two-step model and available historical data from prior drilling operations to predict the occurrence of such an incident well in advance so that it can be avoided. The first step of the model performs time series predictions using a Recurrent Neural Network (RNN) with Walk Forward Validation. The second part of the model uses a Random Forest Classifier to classify the predictions from the previous step and determine if a stuck situation is likely. The classification model is pre-trained using historical drilling operational data collected from old wells. It is possible to determine how far back the model should look at the data and how far ahead it should predict. For the purpose of this paper, the model looks back 5000 timesteps and predicted 3000 timesteps ahead. One timestep is 2 seconds in this case. This model would be able to reduce nonproductive time, and help make drilling operations cleaner, faster and greener. The biggest benefit of such a model is that it can learn on the go and would not require manual intervention. Also, the model can upgrade and modify itself to changes in the drilling operations.