Res2-Unet+, a Practical Oil Tank Detection Network for Large-Scale High Spatial Resolution Images

Oil tank inventory is significant for the economy and the military, as it can be used to estimate oil reserves. Traditional oil tank detection methods mainly focus on the geometrical characteristics and spectral features of remotely sensed images based on feature engineering. The methods have a limited application capability when the distribution pattern of ground objects in the image changes and the imaging condition varies largely. Therefore, we propose an end-to-end deep convolution network Res2-Unet+, to detect oil tanks in a large-scale area. The Res2-Unet+ method replaces the typical convolution block in the encoder of the original Unet method using hierarchical residual learning branches. A hierarchical branch is used to decompose the feature map into a few sub-channel features. To evaluate the generalization and transferability of the proposed model, we use high spatial resolution images from three different sensors in different areas to train the oil tank detection model. Images from yet another sensor in another area are used to evaluate the trained model. Three more widely used methods, Unet, Segnet, and PSPNet, are trained and evaluated for the same dataset. The experiments prove the effectiveness, strong generalization, and transferability of the proposed Res2-Unet+ method.

Accurate Localization of Oil Tanks in Remote Sensing Images via FGMRST-Based CNN

Object localization is an important application of remote sensing images and the basis of information extraction. The acquired accuracy is the key factor to improve the accuracy of object structure information inversion. The floating roof oil tank is a typical cylindrical artificial object, and its top cover fluctuates up and down with the change in oil storage. Taking the oil tank as an example, this study explores the localization by combining the traditional feature parameter method and convolutional neural networks (CNNs). In this study, an improved fast radial symmetry transform (FRST) algorithm called fast gradient modulus radial symmetry transform (FGMRST) is proposed and an approach based on FGMRST combined with CNN is proposed. It effectively adds the priori of circle features to the calculation process. Compared with only using CNN, it achieves higher precision localization with fewer network layers. The experimental results based on SkySat data show that the method can effectively improve the calculation accuracy and efficiency of the same order of magnitude network, and by increasing the network depth, the accuracy still has a significant improvement.

Analysis on the escape phenomenon of oil mist from turbine lower guide bearing based on VOF model

To analyze the cause and mechanism of oil mist escaping from the lower guide bearing during the operation of a turbine, the oil-gas mixture in the lower guide bearing was numerically simulated by using the VOF two-phase flow model and the SST turbulence model. The influences of different sealing clearances and speeds on the flow field and the oil-gas distribution in the oil tank were studied, and the escaping characteristics of oil mist were analyzed. The results show that increasing the clearance of the labyrinth seal will reduce the pressure difference between the inside and outside of the oil tank and reduce the driving force for the escape of the oil mist. However, increasing the clearance will increase the turbulence of the flow field, the number and volume of bubbles in the lubricating oil, which will lead to the uneven distribution of oil and gas and reduce the motion stability of the lower guide bearing. The change of speed will affect the normal use of the shaft-collar pump. High speed will aggravate the generation of bubbles in the oil tank and increase the possibility of cavitation in the lower guide bearing structure. In engineering practices, it is necessary to comprehensively consider factors such as oil mist escape phenomenon, economic benefits, process manufacturing and assembly, and adopt the lower guide bearing structure with a suitable labyrinth seal clearance.

Reliable Detection of Water Cut in Inlet Piping Under Stratified Flow Conditions for Mature Fields and WIO and OIW Emulsion Layers in Crude Oil Settling Tanks with Novel Sensor Technology

Objectives/Scope Mature fields operations, which are almost 70% of today's production have a high water cut content. For each barrel of oil produced there can be 3 or more barrels of water. This means that operational conditions are challenging and might not be ideal for the facilities. In crude oil tanks, one of the most crucial operation parameter is the settling time of oil and water. Especially with heavy oils and high water cut, the operational conditions can be challenging with the existence of emulsion/rag layers in the tank. Most common level detection instrumentation struggle with detecting the proper interface levels leading to faulty control that can cause costly remediation and loss of revenue. This paper presents a novel solution by applying electric tomography pipe and probe sensors. Methods, Procedures, Process In electrical tomography, multiple electrodes are attached on the surface of the sensor and excitations are applied to some electrodes and responses are measured from other electrodes. Assuming a fast separation in the following crude oil tank, the operator expects the flow being stratified already in incoming trunk line. In real life this is not often the case: The incoming flow is turbulent meaning that there is no clear water/oil interface. To overcome this a pipe sensor is needed to monitor the flow regime and hence there is a possibility to control the a) chemical feed and b) flow speed to get the flow stratified. As soon as the flow is stratified in a trunk line it will be guided to a crude oil settling tank for an additional separation. In this tank there is a possibility to apply a probe sensor to monitor reliably the emulsion layer between water and oil. This allows settling time, process parameters and chemicals to be optimized to get a clear separation and hence improving the oil and water quality for a further processing. Results, Observations, Conclusions Results from pipe sensor operation in crude oil pipelines will be shared. The results will show an accurate water cut profile across the pipe cross section even under stratified flow conditions. Additionally, probe sensor results in a crude oil tank operation will be shared and hence confirming the reliability and robustness of the probe sensor operation in tanks. One of the key features of the pipe and probe sensors is the full functionality even under severe contamination with deposits on the sensor surfaces. The operational principle of this method will be shared and verified by experimental results. Novel/Additive Information The sensor technology for the tank inspection and piping uses novel electrical tomography with compact electronic and fast-acting computation with high resolution. This type of technology for settling tank application is new.