Boosting Oil Production by Optimizing ESP Operating Parameters with AI
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 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. Operational efficiency and production optimization are among the company’s key priorities. Currently, Vital Energy is investing in digital technologies to ensure asset integrity, reduce operating costs, and increase production rates. ESP-operated wells play a strategic role in Vital Energy's digital transformation strategy. The company is constantly monitoring and tuning the operating parameters of these wells to achieve high efficiency and production targets.
The Challenge
Vital Energy, a leading energy company, was seeking ways to increase productivity, lower production costs, avoid downtime, and prevent asset failures. The company's Electrical Submersible Pumps (ESP) played a critical role in their operations, but the manual methods for ESP monitoring and optimization were time-consuming, expensive, and prone to human error. Vital Energy wanted to create an AI-based solution for ESP optimization that would automatically recommend ESP operating parameters, provide visualization of all necessary data, and allow for feedback, input limitations, and operating constraints.
The Solution
SoftServe partnered with Vital Energy to build a comprehensive solution on Amazon Web Services (AWS) using Deep Neural Network (DNN) algorithms and optimization techniques. This solution models the entire system by simulating telemetry and production with different scenarios. It allows Vital Energy 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. The solution includes an ML model with high prediction accuracy of oil/gas/water production, an automatically retrained pipeline deployed to the AWS environment, interactive visualizations of simulation scenarios, a Streamlit dashboard for parameter recommendations testing, and a combination of ML- and physical-based models.
Operational Impact
Quantitative Benefit
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