Intro
Saltwater disposal (SWD) pumps play a critical role in oil and gas operations by safely disposing of produced water. Yet, these pumps often run inefficiently, driving up energy use, maintenance costs and downtime. In a recent Society of Petroleum Engineers (SPE) case study, engineers applied AI-driven, KPI-based time series analytics to optimize SWD pumping performance. By transforming vast amounts of operational data into actionable insights, they improved efficiency, reduced costs and extended equipment life. Here’s a human-centered look at how they achieved it—and what you can learn.
Background
Operators typically rely on manual routines or simple threshold alerts to manage SWD pump schedules. But these methods overlook subtle patterns in pressure, flow rate and power consumption that signal impending issues. The SPE team set out to build a data-driven solution that would:
1. Monitor multiple key performance indicators (KPIs) in real time.
2. Detect anomalies before they become failures.
3. Adjust pump schedules dynamically for optimal efficiency.
Their approach combined cloud data ingestion, time series databases, and machine learning models designed for streaming data. Over six months, they deployed the system on 15 SWD wells in one of North America’s largest shale fields.
Step 1: Defining and Collecting KPIs
The first task was to decide which metrics would truly reflect pump health and performance. After workshops with field engineers, the team selected:
• Dynamic pressure differentials at intake and discharge points
• Flow rate stability over sliding windows
• Motor power consumption and efficiency
• Vibration signatures from key bearings
Sensors originally installed for basic monitoring were repurposed to feed a high-frequency data pipeline. Readings were sent every 30 seconds to a cloud data lake, where they were cleansed, time-aligned, and stored in a scalable time series database.
Step 2: Building the Analytics Stack
With data flowing centrally, the next step was analysis. The team chose open-source tools and custom Python scripts to keep costs in check and maintain flexibility. The stack included:
• Anomaly detection algorithms based on Isolation Forest and Seasonal Hybrid ESD
• Unsupervised clustering (k-means) to group similar operating regimes
• Regression models to predict energy use and maintenance windows
• A real-time dashboard for engineers to review alerts and trends
Models were trained on historical data from the previous year. This “warm-start” allowed the system to recognize normal seasonal shifts in water chemistry, temperature and pump load.
Step 3: Real-Time Monitoring and Alerts
Once the analytics stack was live, each well generated an average of 12,000 data points per day. The AI layer processed this stream, raising alerts when:
• Pressure deltas deviated more than 3 standard deviations from the clustered norm.
• Flow rate oscillations suggested early mechanical friction or blockages.
• Power consumption spiked without corresponding increases in throughput.
Instead of generic “pump is off” messages, the system provided contextual explanations—“High discharge pressure at 1:23 AM likely due to build-up in the filter screen” or “Flow variance over the past 15 minutes indicates bearing wear.” This allowed field teams to take targeted actions before a full shutdown.
Step 4: Dynamic Scheduling for Efficiency
Beyond alerts, the SPE team introduced a scheduler that adjusted pump cycles based on predicted load and maintenance windows. By analyzing seasonality and daily demand curves, the system recommended:
• Optimal start and stop times to flatten power peaks.
• Reduced run hours during low-demand periods.
• Planned maintenance during predicted low-production days.
These dynamic schedules were sent to the pump control systems via APIs, enabling semi-autonomous operation under engineer supervision.
Results and Benefits
After three months of live operation, the project delivered measurable gains:
• 12% reduction in energy costs due to smoother load profiles.
• 18% decrease in unplanned downtime by catching issues early.
• 22% longer intervals between major overhauls thanks to less mechanical stress.
• 15% improvement in overall pump availability.
Engineers also reported higher confidence in their decisions, as the AI explanations matched field observations 9 times out of 10.
Human Impact
Beyond the numbers, the project transformed how teams approached SWD operations. Instead of reactive firefighting, engineers could focus on process improvements and strategic planning. They spent less time on routine checks and more on analyzing root causes, mentoring junior staff and driving continuous improvement.
3 Key Takeaways
1. Data-Driven Focus: Selecting the right KPIs and ensuring high-quality data feeds are critical first steps for any AI implementation.
2. Contextual Alerts: Providing clear explanations for anomalies builds user trust and streamlines decision-making.
3. Dynamic Automation: Semi-autonomous scheduling can balance efficiency with human oversight, driving significant cost savings.
3-Question FAQ
Q1: How much historical data do I need to start?
A1: You should aim for at least 6–12 months of clean, high-frequency data covering different seasons and operating modes. This ensures your models capture normal variability.
Q2: Can the system adapt to new wells or changing conditions?
A2: Yes. Unsupervised clustering and online learning algorithms allow the solution to recalibrate as new patterns emerge, with minimal human intervention.
Q3: What infrastructure is required?
A3: A modest cloud environment with a time series database, an analytics server (or managed AI service), and secure APIs for data ingestion and control. Field sensors should support at least 30-second reporting intervals.
Call to Action
Ready to boost your SWD pump performance with AI? Download the full SPE case study for in-depth technical details, best practices, and code snippets. Visit the SPE website, become a member for exclusive access, and join our upcoming webinar to see a live demo, ask questions, and discover how to tailor this approach to your assets.
By putting data at the heart of SWD operations and pairing it with flexible AI tools, you can turn pumps from a cost center into a source of operational excellence—and help your team work smarter, not harder.
