Intelligent Decision Making and Optimization of Artificial Lifting Based on MR Multi-Phase Flow Detection

Quantitative information regarding multi-phase flow of oil, gas and water in wells or pipelines are very important in guiding the artificial lifting parameters optimization and tool selection. At present, there is no reliable technology available which can accurately measure split flow of multi-phase fluids without separating oil, gas and water. So far, the multi-phase flow measurement technique commonly adopted globally is to carry out by phase separation after produced fluid entering the gathering station through the pipelines, where the content of each component is measured separately. The indirect measurement methods are usually with low-efficiency, high-cost and delay-delivery, and hard to reflect the real instantaneous fluid producing properties at wellheads or pipelines. Therefore, it is urgent to seek for accurate and reliable multi-phase flow detection devices and methods that can meet the monitoring demands for oil and gas resources. This paper proposed a nuclear magnetic resonance (NMR) device and analytical methods for detecting multi-phase fluid. At the same time, it puts forward the intelligent decision-making and optimization technology based on measurement, cloud computing and automatic control. As a green, efficient and accurate method for oil and gas detection, the NMR can realize online measurement for each component of multi-phase flow. Then based on the internet and large data analysis technology to achieve artificial lifting parameters optimization, while based on automatic control technology to achieve artificial lifting equipment negative feedback control. This progress helps to apply the NMR technique in petroleum industry to achieve green, efficient, real-time and low-cost multi-phase flow measurement. Combined with large data, Internet of Things (IOT) and automatic control technology to achieve intelligent artificial lifting technology and system.