Boost Oil Production By Optimizing Operating Parameters of ESP with AI

SoftServe helped Laredo build a comprehensive solution on Amazon Web Services (AWS) using Deep Neural Network (DNN) algorithms and optimization techniques, including extensive visualization and interpretation for end users. It models the entire system by simulating telemetry and production with different scenarios. Also, it allows Laredo Petroleum to find the best possible controls to meet constraints such as motor temperature, voltage, intake pressure, and gas or water volume. The algorithm can be adapted to various ESP systems and provides operators with optimal daily control.

Laredo Petroleum

Laredo Petroleum, is a leading energy company that focuses on the acquisition, exploration, and development of oil and natural gas properties. Operational efficiency and production optimization are among the key company priorities. Currently, Laredo Petroleum is investing in digital technologies to ensure asset integrity, reduce operating costs, and increase production rates.

Solution outcomes:

1. An ML model with high prediction accuracy of oil/gas/water production determined from data collected during several months from 50 ESPs
2. An automatically retrained pipeline is deployed to the AWS environment
3. Interactive visualizations of simulation scenarios
4. Streamlight dashboard as a prototype platform for parameter recommendations testing
5. Combination of ML- and physical-based models

• [Data Management - Data Visualization]
  

  Real-time dashboards that provide summary statistics, forecasts, and parameter recommendations for better decision-making.

• [Efficiency Improvement - Productivity]
  
  • A highly flexible solution that can be adapted to a variety of ESP systems
  • Reduced downtime, better maintenance practices, and reduced OPEX