In 2022 Akinê completed an 18-month Demonstration Project on brownfield wells in SE Saskatchewan. This project, supported by Alberta Innovates, aimed to demonstrate that electric cost per unit of production (power intensity) can be significantly reduced by decreasing total power consumption and power demand with the use of an advanced automation solution that includes machine learning augmented control coupled with a modern variable frequency drive.
Keeping operating costs per barrel of oil as low as possible is critical for every oil producer. Power consumption by artificial lift is often the single most significant component of the operating cost, followed by workover and labor costs. In addition, GHG emissions are tightly coupled to power consumption, and with the expected rise in the cost of emissions, lowering power intensity provides the most crucial opportunity for upstream operations cost reduction.
In Q4 of 2020, the Akinê Ecosystem including RAVEN IIoT controllers, Akinê Live platform and Akinê Expert Service, was deployed on 24 existing wells of varying operating characteristics in SE Saskatchewan. Typical of any mature oil field, after 4+ years of initial production from horizontal wells, a significant production decline and increase in the volume of produced water makes electricity the single highest cost of operations. Prior to the deployment of Akinê’s solution, they each of the 24 wells was separately evaluated to understand the main drivers of electricity cost and collected. Over two years, Akinê collected historical electricity usage data and production history for each well to serve as their basis of comparison pre and post-deployment.
Each existing well was equipped with an Akinê RAVEN IIoT controller with Akinê VFD and paired with the existing Nema D motor. The installed high efficiency Akinê VFDs resolved power demand and poor power factor costs associated with artificial lift operation. Specifically, peak power demand during startup was reduced by 95% and power factor during normal operation was increased by 46%.To evaluate the long-term impact of Akinê’s technology, data was collected for 18 months to fully understand and measure how much energy intensity can be reduced and if there are any additional benefits.
RAVEN controllers optimize artificial lift by autonomously responding to changes in artificial lift performance and adjusting the operating regime when necessary, while simultaneously gathering high-quality, real-time data for the Akinê Live cloud platform. There, analytics powered by Artificial Intelligence, Machine Learning and Direct Science are employed to develop deterministic, actionable and transparent real-time insights used by cloud -based autonomous control and by humans. With the ability to automatically identify any productivity issues and react to them automatically, remotely, and by initiating immediate on-site visits when necessary, all small reductions in productivity are determined in real-time and resolved or compensated for to ensure that all available fluids at the bottom of the well are produced on the surface at all times.
Following 18 months of deployment on 24 brownfield wells, Akinê’s technology lowered energy consumption by over 37% and generated a 14% improvement in cumulative oil production. This demonstrates that total reduction in energy cost alone makes similar brownfield projects possible with as little as a one-year payback period for wells with high water cut. While Akinê did not have specific targets for workover reduction and increased efficiency of field staff, there was a significant reduction in the failure rate and workover cost, power and GHG emissions associated with workovers. In addition, Akinê identified that with the Akinê Ecosystem, operation of the field could be fully transitioned to Operating by Exception, thus significantly increasing the effectiveness of field staff, lowering labour costs, power consumption (truck fuels) and energy intensity associated with personnel mobility by at least 50%.
While local autonomous control is being implemented by RAVEN IIoT controllers based on AI generated insights, operators interact with the platform when and how they want. If a system is unable to overcome the challenges on its own, Akinê Live alerts operators for additional support to resolve the issue. Operators know with certainty which wells are underperforming, need immediate or delayed attention, and can operate the field by exception thanks to integrated wellhead cameras providing scheduled and on-demand Visual Site Inspection. Now, rather than indiscriminately driving from well to well every day, operators can not only visually inspect each wellhead and adjacent piping numerous times during the day, but they can also focus their attention on automatically identified problem wells and proactive maintenance. At the same time, production engineers can easily understand and evaluate available inflow for each well and command a production regime that is most desirable for the formation. Reservoir engineers, production engineers, and operators collaborate and interact with the data and each other for the most successful outcome for each individual well and for the whole field.
The Akinê Ecosystem is a highly cost-effective technology that helps increase the profitability of the brownfield and is widely considered a must in any greenfield.