SoftServe Case Studies Boosting Oil Production by Optimizing ESP Operating Parameters with AI

	Boosting Oil Production by Optimizing ESP Operating Parameters with AI SoftServe

Boosting Oil Production by Optimizing ESP Operating Parameters with AI

SoftServe

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
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. Read More
About 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. Read More
Customer Name	Laredo Petroleum Read More
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. Read More Log in to view content
Contents

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

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.

About 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.

Customer Name

Laredo Petroleum

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.

Impact #1	SoftServe's solution helped Vital Energy transition to data-driven ESP optimization, achieving their business goals. The solution provided real-time dashboards that offer summary statistics, forecasts, and parameter recommendations for better decision-making. It is a highly flexible solution that can be adapted to a variety of ESP systems. As a result, Vital Energy experienced reduced downtime, improved maintenance practices, and reduced operating expenses.

Impact #1

SoftServe's solution helped Vital Energy transition to data-driven ESP optimization, achieving their business goals. The solution provided real-time dashboards that offer summary statistics, forecasts, and parameter recommendations for better decision-making. It is a highly flexible solution that can be adapted to a variety of ESP systems. As a result, Vital Energy experienced reduced downtime, improved maintenance practices, and reduced operating expenses.

Benefit #1	Initial pilot achieved a production increase of ~400 BPD across 29 test wells
Benefit #2	Client was later able to get 800 BPD from around 50 wells
Benefit #3	Scalability in months from 29 to more than 100 wells in production

Benefit #1

Initial pilot achieved a production increase of ~400 BPD across 29 test wells

Benefit #2

Client was later able to get 800 BPD from around 50 wells

Benefit #3

Scalability in months from 29 to more than 100 wells in production

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Overview

Boosting Oil Production by Optimizing ESP Operating Parameters with AI

Operational Impact

Quantitative Benefit