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Fueling efficiency: Battling ESP failures in remote

In the fossil fuel industry, operational efficiency and component reliability are pivotal for seamless business processes. The down-hole mining sector often encounters energy sources deep under the earth’s surface and in remote locations that need innovative energy extraction solutions. However, challenges such as unexpected failures of Electrical Submersible Pumps (ESP) can lead to extended downtime that costs companies millions of dollars.


Revolutionizing downtime reduction for a global energy giant

Our client, one of the largest integrated energy companies in the world, needed a way to shorten downtimes by proactively identifying breakdowns before they happened. The oil wells’ remote locations and the pumps’ depths compounded the challenge of addressing failures quickly. Traditional maintenance practices typically involve periodic inspections or scheduled maintenance at fixed intervals, which can be time-consuming or not viable when a well is continuously running. Unplanned failures are common and can lead to extended downtime, further impacting productivity and profitability.

Addressing the unforeseen

Unplanned failures of ESPs were a critical problem for our client. These unexpected breakdowns cause significant downtime, impacting production schedules and operational profitability. Minimizing downtime is incredibly important to avoid multi-million-dollar losses, primarily due to the weeks it takes to schedule, travel to, and repair non-producing wells.

Smart ESP management: reducing downtime, boosting ROI

Periodic replacement and fixed-interval maintenance are not profitable when doing so requires stopping the production of a well for potentially no reason. If an ESP was found to be running fine and had plenty of life left, the well experienced significant downtime (and lost revenue) for little to no return. Our client needed a more intelligent and proactive approach. The goal was to allocate maintenance resources just in time to schedule replacement before an ESP failure occurs, optimizing maintenance costs and ESP lifetime value and reducing unplanned downtime.


Mastering ESP failure prevention with deep learning

Fractal needed first to understand the types of ESP failures to address the challenge. We analyzed the challenge and root cause analysis data to do this.

Armed with this knowledge, we harnessed the power of Deep Neural Network (DNN) models, leveraging historical sensor data, to effectively differentiate between standard and aberrant equipment conditions. Drawing upon our extensive proficiency in predictive maintenance solutions and a comprehensive grasp of our client’s data and operations, we engineered a model capable of forecasting equipment failures well in advance, enabling the scheduling of repairs with minimal disruption to operations.

  • Deep Neural Network
  • Autoencoder Model
  • Domain Expertise & Analysis
  • Customized Machine Conditions
  • Additional Model Enhancements
  • To differentiate between normal and deviated equipment states
  • An input to the primary CatBoost model
  • To gain insights from SMEs and physics-driven failure mode analysis
  • To learn from other similar machines to enhance model accuracy
  • To increase precision and timeliness in predictions
  • Improved accuracy in predicting the condition of equipment
  • Enhanced capacity to predict equipment conditions
  • An accurate representation of the intricate conditions that equipment may face
  • Improved accuracy of the model in addressing ESP system conditions
  • A significant boost in the precision and timeliness of equipment condition predictions
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Our Offering

Engineered ESP success through predictive maintenance precision

The base Cat Boost model employed an autoencoder model as a feature input, and to refine our approach, we leveraged domain expertise from subject matter experts (SMEs) and conducted Physics-driven failure mode analysis. We further customized the model by incorporating information about each specific machine’s operating conditions and insights gathered from other machines to improve the model’s accuracy. Fractal deployed this solution on Azure, using Azure Data Lake for storage, Databricks for data analysis, and Azure Machine Learning for model training.


Maximizing Uptime and Profitability

Instantaneous Impact

Implementing the predictive maintenance model resulted in a substantial reduction in unplanned downtime. By identifying equipment issues at least two weeks before they escalated, the company could schedule maintenance in time to get a team out to the remote site before the ESP failed, leading to additional revenue generated and optimized maintenance operations.

Sustainable Gains

Over time, the benefits of this solution will continue to grow. In particular, the model’s ROI will continue to improve as additional failure data is generated. This will reduce or eliminate downtime before a team can repair a well. It will also decrease false positives for pumps with remaining useful life. This would help see a substantial revenue increase from the additional uptime of pumps in the field.

  • The model significantly reduces the downtime between a failure and a response, resulting in additional revenue.
Profitability Enhancement
  • Knowing what has failed and where leads to reduced turnaround time and increased profitability.
Cost Savings & Revenue Growth
  • Substantial cost savings and revenue growth are realized through intelligent and proactive maintenance.
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