Production Optimization

Challenge:
CNX Resources had under-optimized wells with increased liquid loading frequency which was resulting in increased GHG emissions, decreased gas production, and overall reduced workforce efficiency.                                                                                                                                        

Solution:
Ambyint’s InfinityPL allowed CNX to optimize well productivity with improved analytics, develop autonomous management of plunger triggers/setpoints, improve production efficiency, with only data integration with no additional hardware.

Impact:

• Reduced GHG emissions by 48% through reduced liquid loading and well venting events
• Increased natural gas production by 4% with predictive maintenance and autonomous management of control system setpoints
• Scaled up solution to additional wells after successful pilot results
  
• Enhanced workforce efficiency by reducing number of screens, simplifying workflows, and automating manual tasks

Challenge:
Bowline Energy needed to optimize and upgrade rod lift wells that had high failure rates resulting in significant equipment downtime and deferred revenue.

Solution:
Ambyint’s InfinityRL allowed for scaled production workflows without additional headcount and delivered autonomous operations for setpoint management and rod lift optimization.              

Impact:

• Improved Mean Time Between Failures (MTBF) by 28%, reducing workover costs, well downtime, and deferred production
• Expansion to all wells resulted in 10x ROI through production gains, OpEx reduction, workforce efficiency, and GHG reduction
• Reduced GHG emissions by 10% through reduced electricity consumption

Challenge:
Laredo Petroleum had wells that run on ESP which needed constant monitoring and tuning of operating parameters to achieve efficiency and production targets. Finding the optimal combination of operating parameters was also a difficult and time-consuming process.

Solution:
Softserve developed an a custom application to optimize Laredo’s ESP performance with a combination of ML-based and physics-based models. The models were trained in several months from 50 ESPs. The application included interactive visualization of simulation scenarios and recommendation of control parameters.

Impact:

• Real-time dashboards provide summary statistics, forecasts, and recommendations for better decision-making
• Pilot objective was estimated to see additional 400 BPD. Now they have achieved improved production by >800 BPD across 29 pilot wells
  
• Reduced downtime, better maintenance practices, reduced OpEx
  
• The solution scaled to 100+ wells in production in several months.
• The solution is highly flexible and could be adapted to a vast variety of ESP system.