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Boost Oil Production By Optimizing Operating Parameters of ESP with AI

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 Boost Oil Production By Optimizing Operating Parameters of ESP with AI - IoT ONE Case Study
The Challenge

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.

About The Customer

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.

ESP-operated wells play a strategic role in Laredo Petroleum's digital transformation strategy. New and in-service wells that run on ESP need constant monitoring and tuning of operating parameters to achieve high efficiency and production targets. Laredo wanted to create an AI-based solution for ESP optimization that will automatically recommend ESP operating parameters, give the ability to visualize all the necessary data, and have a way to leave feedback, input limitations, and operating constraints.

The Customer

Laredo Petroleum

The Solution

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

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