Artificial Lift Real-Time Monitoring Digitalization Method: An Advanced Approach with Artificial Intelligence to Achieve Efficient Well Surveillance by Utilizing SCADA

Abstract

Abstract In Block X, there are a limited number of engineers and a vast number of wells that require to be monitored on a daily basis. Before 2018, engineers must visit each well to survey and monitor its status. This process proved to be inefficient due to the shortage of engineers which was aggravated by arduous journeys to remote areas, and long response time during test line leakage. To improve the quality of surveillance and artificial lift systems, Medco E&P proposed the installation of Supervisory Control and Data Acquisition (SCADA) monitoring systems. SCADA was installed using Global System Mobile Communication (GSM) modem in ESP wells while SRP wells used Rod Pump Controlled modem. Data from both artificial lift wells is sent automatically to the user's platform through the installed system. By implementing this SCADA system, the data can be acquired more efficiently without the need for engineers to be present in the field. This leads to an effective surveillance monitoring activity where well status can be determined remotely and shorten response time when problems arise. Since the installation of the SCADA system, the average loss potential oil of ESP wells was decreased to around 24% while the down duration was decreased by 33%. These percentages correspond to a savings of $20,000 per month in LPO (Loss Potential Oil) after the implementation of SCADA. The test line leakage problem can be solved through creating a virtual metering feature using machine learning based on the SCADA data acquired. This virtual metering is used to predict the liquid rate of each well using already existing well parameters without the need for well testing. The SCADA data can be further used for further analysis to prevent Sucker Rod Pump (SRP) pump failure and increase lifespan of the pump by determining SRP Unit Balance.