Offshore Oil Spill Detection Based on CNN, DBSCAN, and Hyperspectral Imaging.

Offshore oil spills have the potential to inflict substantial ecological damage, underscoring the critical importance of timely offshore oil spill detection and remediation. At present, offshore oil spill detection typically combines hyperspectral imaging with deep learning techniques. While these methodologies have made significant advancements, they prove inadequate in scenarios requiring real-time detection due to limited model detection speeds. To address this challenge, a method for detecting oil spill areas is introduced, combining convolutional neural networks (CNNs) with the DBSCAN clustering algorithm. This method aims to enhance the efficiency of oil spill area detection in real-time scenarios, providing a potential solution to the limitations posed by the intricate structures of existing models. The proposed method includes a pre-feature selection process applied to the spectral data, followed by pixel classification using a convolutional neural network (CNN) model. Subsequently, the DBSCAN algorithm is employed to segment oil spill areas from the classification results. To validate our proposed method, we simulate an offshore oil spill environment in the laboratory, utilizing a hyperspectral sensing device to collect data and create a dataset. We then compare our method with three other models-DRSNet, CNN-Visual Transformer, and GCN-conducting a comprehensive analysis to evaluate the advantages and limitations of each model.

Parameter prediction of coiled tubing drilling based on GAN-LSTM.

With the increasing development of coiled tubing drilling technology, the advantages of coiled tubing drilling technology are becoming more and more obvious. In the operation process of coiled tubing, Due to various different drilling parameters, manufacturing defects, and improper human handling, the coiled tubing can curl up and cause stuck drilling or shortened service life problems. Circulation pressure, wellhead pressure, and total weight have an important influence on the working period of coiled tubing. For production safety, this paper predicts circulation pressure, ROP, wellhead pressure, and finger weight using GAN-LSTM after studying drilling engineering theory and analyzing a large amount of downhole data. Experimental results show that GAN-LSTM can predict the parameters of circulation pressure, wellhead pressure ROP and total weight to a certain extent. After much training, the accuracy is about 90%, which is about 17% higher than that of the GAN and LSTM. It has a certain guiding significance for coiled tubing operation, increasing operational safety and drilling efficiency, thus reducing production costs.