Actual Field tests performed in heavy and extra-heavy oil wells in San Tome, Venezuela reveal that axial load does have a direct relation with Progressive Cavity Pump (PCP) downhole conditions. The differential pressure across the pump is easy to infer by just measuring the surface axial load of the system and surface pressures. This fact is particularly useful to diagnose a PCP (known to be very difficult to do for this system). Good inferences on downhole conditions allow PCP optimization to be done much easier and more efficiently. It has been proven in beam pump applications that the dynagraph chart (axial load vs. pump cycle) can be used to diagnose and optimize the pump and the well. The same principle is used for PCPs, except a rotational effect has to be taken into account. Of course by using the rest of the operational data that assist with construction and recognition of operational patterns, the PCP diagnostics becomes more exact and efficient for the production engineer.

The work herein presented reveals field tests of the system used to optimize PCP wells, based on the important usage of axial load and several other variables (current, pressures, etc) with a special ingredient that has a tremendous impact in optimization: Artificial Intelligence and Automation. The mathematical principles are also presented to encourage the usage of axial load in a multivariable mathematical model in order to complete a good optimization scheme for these wells. The hardware tools in the well completion needed to perform such a task are discussed as well, including the axial load measuring device.

The benefits of operating a PCP system using a Progressive Cavity Pump Optimization System (PCPOS) are substantial, as described in this paper. The idea is to reduce downtime, workovers, improve system operating response time and equipment useful life, while optimizing the well production.

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