The main goal of all production operators is maximizing production and net profit out in a very safe and environmentally controlled manner. When producing difficult fluids from marginal, mature and brown onshore oil fields, Sucker rod (Beam and Progressive Cavity) Pumping systems are typically the most common artificial lift methods used. The primary challenge is to extend the lifting equipment run life, especially the downhole components. For years, operators have been looking for a reliable and accurate way to automatically control these types of lifting systems to improve their reservoir recovery factor by maximizing production. The best-proven way for operators to make proactive decisions for well / field optimization, is to have a fully automated closed-loop monitoring and control solution for the entire field of artificially lifted wells. This paper will show how the well automation and real time downhole measurements are used in real time to control and optimize the operation parameters and well production to obtain maximum benefits. Some case histories for Beam and Progressive cavity pumping systems from different oil fields will presented.

In oil and gas production, the multiphase flow rate is one of the most important measurements at the wellhead because it is essential for production allocation, flow assurance, and production surveillance. Multiphase flowmeter devices (MPFM) provide accurate measurements. However, this performance comes at high capital and operating costs that are not economically viable for all wells within aging or on mature fields. A real-time platform based on current technologies offers methods to deploy and automatically update the reduced-order models (ROM) to edge field devices. This approach is used specifically to provide a solution to estimate flow rates in real time at the field level, with and accuracy performance close to what the MPFM delivers, but at a reduced capital and operating costs. Edge field devices can provide the required computing power to run data-driven ROM models on the field side using neural network algorithms, trained to calculate well multiphase flow rate with acceptable accuracy. Periodic updates are performed on the data-driven models on the cloud, and the updated models are downloaded to the edge field device. In this way, the models based on neural networks, deployed and running at the edge field device level, are automatically adapted to changes of the well flow regime over the well's life cycle. An auto-trainable, machine-learning-based, multiphase estimated flow metering system running at the wellsite has been deployed and implemented. The system uses real-time and well test data, mathematical well models, neural-network-based data-driven models, which are automatically trained and updated on the cloud. The solution includes implementation in a real-time digital oil field automation system, which provides the technology platform required by this virtual flow metering system. The system offers a cost-effective way to obtain well flow rate measurement estimates with an acceptable accuracy. This virtual metering system can be used on hundreds or thousands of oil wells simultaneously using real-time data. The system provides oil and gas operators with good real-time flow metering estimates of their wells, accurate enough for production surveillance and allocation.