SoftServe > Case Studies > Boosting Oil Production with AI: A Case Study in Energy, Oil & Gas

Boosting Oil Production with AI: A Case Study in Energy, Oil & Gas

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 Boosting Oil Production with AI: A Case Study in Energy, Oil & Gas - IoT ONE Case Study
Technology Category
  • Analytics & Modeling - Machine Learning
  • Sensors - Utility Meters
Applicable Industries
  • Oil & Gas
Applicable Functions
  • Maintenance
Use Cases
  • Asset Health Management (AHM)
  • Leakage & Flood Monitoring
Services
  • Data Science Services
  • Testing & Certification
The Challenge
Oil and gas companies need to raise productivity, lower production costs, avoid downtime, and prevent asset failures. Manual methods for ESP monitoring and optimization are time-consuming, expensive, and prone to human error.
The Customer

Laredo Petroleum

About The Customer
Vital Energy is a leading energy company that focuses on the acquisition, exploration, and development of oil and natural gas properties. They prioritize operational efficiency, production optimization, and digital transformation. ESP-operated wells play a strategic role in their digital transformation strategy.
The Solution
SoftServe helped Vital Energy build a comprehensive solution on Amazon Web Services (AWS) using Deep Neural Network (DNN) algorithms and optimization techniques. The solution models the entire system by simulating telemetry and production with different scenarios, allowing operators to find the best possible controls to meet constraints. It also provides real-time dashboards, forecasts, and parameter recommendations for better decision-making.
Quantitative Benefit
  • The ML model developed by SoftServe had high prediction accuracy of oil/gas/water production. The solution delivered real-time dashboards, forecasts, and parameter recommendations for better decision-making. It also provided a highly flexible solution that can be adapted to a variety of ESP systems.

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