Multiple Oil Pad Detection Using Deep Learning

Abstract

Abstract. Deep learning (DL) algorithms are widely used in object detection such as roads, vehicles, buildings, etc., in aerial images. However, the object detection task is still considered challenging for detecting complex structures, oil pads are one such example: due to its shape, orientation, and background reflection. A recent study used Faster Region-based Convolutional Neural Network (FR-CNN) to detect a single oil pad from the center of the image of size 256 × 256. However, for real-time applications, it is necessary to detect multiple oil pads from aerial images irrespective of their orientation. In this study, FR-CNN was trained to detect multiple oil pads. We cropped images from high spatial resolution images to train the model containing multiple oil pads. The network was trained for 100 epochs using 164 training images and tested with 50 images under 3 different categories. with images containing: single oil pad, multiple oil pad and no oil pad. The model performance was evaluated using standard metrics: precision, recall, F1-score. The final model trained for multiple oil pad detection achieved a weighted average for 50 images precision of 0.67, recall of 0.80, and f1 score of 0.73. The 0.80 recall score indicates that 80% of the oil pads were able to identify from the given test set. The presence of instances in test images like cleared areas, rock structures, and sand patterns having high visual similarity with the target resulted in a low precision score.

Optimization of Targeted Differential Interferometric Measurements for Wellpads Detected by Mask Region-Based Convolutional Neural Network in the Tengiz Oilfield of the Caspian Sea Coast

“…The studies by Wang et al [20] and Giri et al [21] presented identical precision and recall values with our results but for Faster RCNN, SSD, and RetinaNet deep learning models. The primary difference between these studies and ours is that these models generated bounding boxes for wellpads, whereas Mask R-CNN generated actual boundaries of wellpads through the instance segmentation technique, which performed pixel-level segmentation on detected wellpads.…”

“…They also created some confusion for the model, and it would also be reasonable to separate them into a different class. It was also mentioned in the referenced study by Giri et al [21] that by grouping the false positives together into different categories for use as target classes for training, one can expect the model to learn the differences among the classes during the model training process. This would contribute to significantly reducing false positives.…”

“…Many research studies have evaluated the performance of different deep learning methods, software, and imagery in terms of accuracy and speed in the context of recognizing wellpads and the consequent quantification of temporal changes [19][20][21][22]. To the extent of our knowledge, a deep learning-based approach for the recognition of wellpads has never been applied to the coastal oil and gas fields of the Caspian Sea.…”