Drilling fluid processing: preparation, maintenance and continuous conditioning
The following discussion is an overview of the operation of drilling fluids at the rig site, giving an insight to those less versed in the art of drilling oil and gas wells. Fluid processing at the rig site is currently a prime target for higher levels of mechanization and process control, leading towards automation. The drilling fluids' composition influences its properties and dynamic behaviour, and the various drilling fluid components and additives all have specific purposes related to the drilling process. The process of drilling fluid preparation and mixing on the drilling rig is described, and the means of maintaining suitable properties during the drilling process are discussed. Means and requirements are presented for enabling continuous conditioning of the drilling fluid to suit the variations in the wellbore and formation, pointing out current challenges in measurement, instrumentation, mechanization, and control that need to be solved. It is clear that a higher level of automation is required if we wish to achieve this goal.
Towards cleaner and more productive maintenance in petrochemical facilities: Mechanization and an assessment method
Abstract Maintenance in petrochemical plants is often characterized as labor intensive and may give rise to such problems as being costly, diminishing productivity, and emitting pollutant. To mitigate such problems, managers have tried mechanizing the maintenance tasks. This study elaborated the concept of mechanization, proposed a method named Petrochemical Maintenance Mechanization Assessment (PEMMA), which can help assess mechanization levels of the maintenance tasks and provide corresponding improvement recommendations This study presented the development process of the PEMMA method and applied the method in the context of Singapore. Results showed that the mechanization level of the maintenance tasks in Singapore is relatively low. The developed method is arguably the first to be presented and therefore, it contributes to the existing body of knowledge. In addition, the developed method is beneficial to the practice as well, because it can help diagnose and then improve the mechanization levels of petrochemical plants, which would eventually make the petrochemical industry more productive and cleaner.
Research on Fracture Recognition in Well Logging Images: Adversarial Learning with Attention
Semantic recognition of fractures in well logging images is vital for engineers to implement oil and gas exploration. An essential approach to accomplishing the object is to adopt deep learning based on convolutional neural networks. However, due to the lack of annotated labels in well logging images, it is scarcely available to directly train the semantic segmentation network. In this paper, we explore a domain shift model attempting to achieve domain adaptation from one annotated dataset to our target well logging images. This model's core is to utilize adversarial learning, including generator and discriminator, which can prompt the model to generate fracture segmentation similar to the source domain in the target domain. For enhancing the domain adaptive model further, an attention module is introduced, which can suppress redundant noise in semantic segmentation results. We demonstrate that our proposed model performs well by extensive tests and ablation experiments in the ultrasonic well logging images.