During well testing operations, an incorrect burner combustion may pose a risk of adverse impact on the environment or people. The combustion efficiency is assessed by personnel who observe the flame. This practice lacks consistency and may pose challenges, from environmental and safety considerations to data quantity and quality. An automatic alarm triggered by suboptimal combustion is needed. In this paper, we propose a solution that uses a deep neural network that learns from flame videos to define the quality of the combustion.
Flame features that help determine the combustion quality are the flame color, the color of the smoke around the flame and the flame shape and pattern, among many others. To extract the features relevant to the problem and qualify the combustion, we performed supervised learning using a convolutional neural network. We trained the network using videos of flames from burner combustion; the network extracts relevant features and classifies the flame in video images as acceptable or undesirable.
We labeled a set of flame videos obtained from well testing operations. The videos include various flame conditions, in day and night scenarios and acceptable and undesirable combustion contexts. We trained the convolutional neural network using the labeled flame videos.
The network prediction accuracy is 100% on the training set and 96.8% on the test set. The misclassified images are in the transition periods between acceptable and undesirable combustion events.
We demonstrated the potential of a deep learning solution using a convolutional neural network for classifying the combustion quality of burner images as acceptable or undesirable. The results are promising, and they show that this solution is a good candidate for real-time burner efficiency monitoring and automatic alarm triggering and optimization.
