ABSTRACT

The subsea environment of the ocean is complex, and it is very easy to cause damage to the offshore production facilities, the complete and effective hydrographic data analysis of the operating waters is the key factor to ensure that the offshore production facilities complete the optimization calculation. In this paper, in order to enhance the understanding of the laws of deep-sea subsea hydrology, a deep ocean hydrographic element acquisition device based on edge computing technology is proposed. The device can collect seawater temperature, salinity, current speed, and other hydrological environment data, it can realize real-time and efficient edge calculation of hydrological data, so that it can ensure real-time transmission of data, and then create an information database of hydrological environment elements in the operating waters. The device is connected to a winch fixed on the platform deck by steel cable, and the automatic lowering of the device is finished by using an automatic console. After the device into the water, the data acquisition device begins to operate. Data is collected at designed water depth points and the device completes the calculation of the data optimization, the data through the cable transmission to the platform surface console device, so far, the device finishes a data collection. Then, the device repeats the process until the completion of all the water depth points. In order to reasonably explain the applicability of the device in seawater, the finite element model of the hydrologic acquisition device is established by using OrcaFlex software. The offset of the device is taken as the reference index, and the environmental conditions are selected for finite element simulation analysis. Based on edge computing model, the use of the device described in this paper can reduce the processing time of the data, greatly release the arithmetic resources in the center of the offshore operation facility and improve the utilization rate of the arithmetic resources.

INTRODUCTION

Marine hydrological environment is complex, due to the different sea areas, the hydrological law has a greater difference. So, the combined effect of the sea current and wave load will have a great impact on the production facilities of the offshore platform. For the comprehensive calculation of the structural strength and safety, we need a large number of hydrological data as the analysis support. The traditional way of obtaining hydrological data, often faced some problems, such as slow data analysis, data inaccuracy, take up the center of the arithmetic resources and so on. It was noted (Manu lgnatius et al, 2023) that an intelligent wireless data transmission scheme is proposed, it used Acoustic Doppler Current Profiler (ADCP) with edge computing. By using this scheme, it has reduced the overall cost and improved the safety of device operation. It was noted (Abhishek Sharma et al, 2020) that an edge IIoT solution was proposed, which is based on the theory of edge computing. It has increased oilfield well data's ability through a combination of physical and data-driven, which in turn enables the management of remote well health and safety and ultimately improves well performance. Shukla, S et al. (2023) discussed that several new wells in the Green Field, located in western Libya, used an edge computing platform to complete the virtual traffic calculation. This solution can increase production capacity significantly and enable the rapid digitization of the field. Miguel Gonzalez et al. (2022) discussed that a new mud viscosity/density system based on electromechanical tuning fork resonators, which is integrated into edge computing systems, it not only can improve data collection but also accelerate deployment of machine learning models. It was noted (Jinxin An et al, 2021) that an autonomous underwater vehicle (AUV) was introduced, which improves the safety and reliability of the AUV by establishing a safety analysis framework. Nithiwat Siripatrachai et al. (2021) discussed that a machine learning platform deployed on edge computing. The platform can autonomously control the optimization of unconventional and non-conventional gas wells, the result shows that this method is able to reduce fluid loading, manual intervention and increase production. Mario Torre et al (2020) described a trainable multiphase flow metering system based on machine learning that enables real-time data measurements from hundreds of Wells. Jason D. Flanagan et al (2016) discussed that an ADCP deployed off the west coast of Ireland. They analyzed and compared wave data which was collected under extreme conditions. Zhong Cheng et al (2023) introduced that the importance of data acquisition and data processing of digital oilfield construction. Based on the theory of edge computing, this paper proposes a deep ocean hydrological element acquisition device and method, which can realize the collection of hydrological elements in different locations and different waters, and it also greatly releases the central computing power resources to realize the intelligence of data collection.

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