Abstract
During the mid-twentieth century, Dr. Sam Gibbs developed math that continues today to serve as the basis for the downhole monitoring and control of many wells. However, analytical methods based on this calculation, including wave equation techniques, assume vertical, single-well pads with little to no side loading. Modern E&P development has shifted to multi-well horizontal well pads with shallow kickoffs uphole and backbuilds downhole with high dog-leg severity, which causes high friction and dynamic conditions in the pumping system.
The Gibbs wave equation and fillage calculation do not take into account key wellbore forces such as mechanical or Coulomb friction arising from rod on tubing wear due to deviation in the well. Friction due to deviation can result in downhole dynamometer card shapes that distort fillage calculations and downhole card analysis. Additionally, most rod pump control systems are based on Programmable Logic Control systems (PLC). PLC systems are simple to program but are limited in their computation capabilities and are unable to accommodate sophisticated mathematics. Increased computational capabilities are required to execute higher-order mathematics that accurately calculate downhole parameters and enable well autonomy.
One approach to driving autonomous well classification and optimization of setpoints is the deployment of a system that is capable of real-time analysis and higher-order mathematics. An Internet of Things (IoT) device with high-performance computational capabilities and direct communication with a cloud-based analytics software platform was developed with the capabilities to execute higher-order mathematics, artificial intelligence and machine learning on high resolution data, sampled in real-time from the rod pump control system.
Equinor deployed this technology on 50 wells in the Bakken. The 50 wells chosen are highly representative of "typical" Bakken horizontal wells. The device was connected into the legacy rod pump controller via Modbus connection. Immediate differences in key downhole parameters were observed when comparing the results from the traditional rod pump controller to the IoT device. The higher-accuracy physics-based inputs feed into machine learning algorithms, which dynamically classify wells into key operating states of under-pumping, over-pumping, and dialed in.
Using improved downhole information, Equinor was able to automate well optimization setpoint decisions, resulting in reduced well volatility, better pump efficiency, and increased pump fillage. Equinor was able to achieve these improvements while maintaining production in all cases. By identifying wells that were over-pumping and under-pumping to optimize SPM setpoints, Equinor was able to achieve higher efficiency outcomes with equal or increased production. For wells that were under-pumping, Equinor was able to increase oil production by up to 33%. For wells that were over-pumping, Equinor was able to decrease the number of strokes by 11% and increase pump efficiency by 14%.
