A real-time deep-learning model is proposed to classify the number of shale drill pieces on offshore drilling using real-time monitoring video stream analysis. Unlike the traditional, time-consuming approach to video analysis, the proposed model can implement real-time classification and achieve exceptional accuracy. The methodology consists of three modules. Compared to results manually tagged by engineers, the model can achieve very accurate real-time results without image dropouts.<\/p>\n\n\n\n
There is a complete workflow for many oil and gas companies to help maintain and clean wells. A well-designed workflow can help promote good integrity and reduce drilling risks and costs. The traditional method requires human observation of shale mining, a hydraulic model, and a torque and traction model; the operation involves several cleaning cycles. This continuous manual monitoring of the cutting volume on the slate shaker has become an obstacle in the traditional workflow and cannot provide a consistent assessment of the pit cleaning status because human labour is not always available, and the torque and pull operation is discreet, consisting of a break between two cycles.<\/p>\n\n\n\n
Most previous work has used image analysis techniques to perform a quantitative analysis of the number of slices. The traditional method of image processing requires considerable functional engineering work. Because raw data is often noisy with missing components, preprocessing and adding data play an essential role in streamlining and productivity the learning model. On the other hand, the deep learning framework automatically detects the representations needed to detect functions or classify raw data. This will help overcome difficulties in setting up and operating the equipment in harsh environments, and the need to collect data for an offshore volume monitoring system can be alleviated.<\/p>\n\n\n\n
This study aimed to demonstrate the possibility of building a system for automatically monitoring the cutting volume in real-time at a remote location with limited bandwidth for data transmission. Minimum hardware requirements for data collection include the following:<\/p>\n\n\n\n
A deep neural network (DNN) has been adopted, which allows image processing and classification of cutting volumes of a shale shaker at a distance from the drilling rig. Specifically, the described model implemented convolutional neural networks as extractors of functions and classifications. The main benefits of this study can be summarised as follows:<\/p>\n\n\n\n
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