Case Study: Optimizing Maintenance Costs and Enhancing Safety for a Large Oil & Gas Company
The Challenge
A leading Oil & Gas company experienced high maintenance costs and an increase in defective fractionators, affecting their overall operational efficiency. Performing routine maintenance, they aimed to optimize costs without compromising safety. The company sought to leverage their historical data to better understand the conditions leading up to failed units and identify early warning signs for preventive action.
The Solution
We partnered with the Oil & Gas company to develop a data-driven approach to predict and prevent unit failures. Our team of experts:
- Collaborated with the company’s process engineers to identify valuable data insights related to fractionator performance and failure.
- Built data pipelines to aggregate, clean, and model the relevant data from various sources.
- Applied machine learning algorithms to identify patterns and correlations between different factors and the likelihood of unit failure.
- Developed a user-friendly report that listed each unit’s risk score, color-coded for easy interpretation. Users could drill down to understand the contributing factors for each risk score.
- Integrated the report into the company’s daily workflow, providing actionable insights during morning projection meetings.
The Results
By leveraging Digital Warden’s expertise in data engineering, machine learning, and business intelligence, the Oil & Gas company achieved:
- Over $50,000 in maintenance cost savings within the first three months of usage.
- Improved safety and reliability of fractionators by proactively addressing potential failures.
- Enhanced decision-making and preventive maintenance planning through daily data-driven insights.
The successful implementation of the report continues to drive cost optimization and safety improvements, showcasing the value of data-driven decision-making in the Oil & Gas industry.
