Near-complete elimination
of support tickets
Production engineers faced pressure to optimize workflows and reduce manual processes. They spent hours on repetitive data entry, manual model setup, and analysis using external tools.
SLB developed a Python SDK with Anaconda’s embedded distribution, enabling automated model building, batch simulations, and direct integration with analytics workflows.
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Powering Production Innovation: How Anaconda and SLB Collaborated to Transform Flow Simulation with Python Automation
SLB’s Pipesimâ„¢ steady-state multiphase flow simulator has led the market in flow simulation for more than 40 years, transforming production engineering across the oil and gas industry. Now, by embedding automation capabilities through Anaconda Platform, Pipesim enables petroleum engineers to move faster, work smarter, and build the data-driven workflows that modern energy operations demand.
The Challenge: Meeting Modern Engineering Demands
Today’s production engineers face heightened pressure to optimize workflows, reduce manual processes, and deliver insights faster than ever before. Engineers found themselves spending countless hours on repetitive data entry, manual model setup, and time-consuming results analysis. Simulation results sat isolated, requiring engineers to follow a tedious process to export data manually and analyze using external tools.
Perhaps most challenging was the growing disconnect between simulation tools and modern digital infrastructure. Connecting simulation outputs with broader digital platforms required complex workarounds that often broke down in practice. Meanwhile, a new generation of engineers increasingly expected Python-native workflows that aligned with modern data science practices.
The Solution: Enterprise-Grade Python Integration
Recognizing this workflow shift, SLB developed a comprehensive Python Software Development Kit (SDK) that transforms how engineers interact with Pipesim simulator models. Rather than being limited to traditional point-and-click interfaces, where they spend valuable time on repetitive tasks, engineers can now programmatically control every aspect of their simulation workflows. This enables them to spend time on high-value analysis that looks at ways to optimize production, reduce costs, and identify new revenue opportunities.
This Python SDK fundamentally changes the engineering workflow by enabling automated model building from existing data sources and templates. Engineers can execute batch simulations across hundreds of scenarios in minutes rather than days, while extracting structured data that feeds directly into advanced analytics and machine learning workflows.
To ensure reliable, enterprise-grade delivery of these Python capabilities, SLB collaborated with Anaconda, which brings decades of experience in enterprise Python distribution, serving over 50 million users and 95% of the Fortune 500.
The embedded solution delivers Python capabilities through a two-component approach that leverages the Anaconda Platform. The environment includes a curated set of scientific computing packages maintained by Anaconda, integrated with SLB’s proprietary modules for direct interaction with the Pipesim simulation engine. This design combines robust Python ecosystem support with SLB’s domain-specific simulation expertise.
This architecture ensures that petroleum engineers can immediately begin working with Python-enabled workflows, eliminating complex environment setup, dependency conflicts, and security concerns.
Transformative Impact Across Operations
The Python-enabled Pipesim simulator has delivered measurable benefits in automation and workflow efficiency. SLB customers have observed improvements in operational efficiency alongside meaningful enhancements to their engineering capabilities.
Support overhead has virtually disappeared, with organizations seeing near-complete elimination of installation and environment-related support tickets — representing significant cost savings for both IT departments and engineering teams. More importantly, production engineers now tackle repetitive simulation tasks that previously consumed days of manual effort in a matter of hours or minutes.
Perhaps most significantly, the solution has sparked a cultural shift within engineering organizations. Young engineers are actively pursuing Python training specifically to leverage Pipesim simulator’s automation capabilities, viewing these skills as essential for career advancement in an increasingly digital industry.
Technical Innovation: Reimagined Workflows
Beyond basic automation, the integration has enabled innovative approaches that address longstanding engineering challenges while making Python accessible to petroleum engineers regardless of their programming background. Most Pipesim simulator users are not traditional Python developers, but the embedded integration allows them to leverage sophisticated Python capabilities through low-code interfaces that deliver high-impact automation and insights.
Traditional Excel integration often relied on older add-in technologies that caused significant performance issues, including application freezing and slow response times. SLB’s engineering team developed a sophisticated communication architecture that provides responsive user interfaces within Excel without application freezing.
Engineers can now perform Monte Carlo “what-if” simulations across thousands of parameter combinations, obtain automated visualizations for sensitivity analysis, and connect their models to real-time data streams. The embedded Python packages handle the complex computational work behind the scenes, allowing engineers to focus on analysis and decision-making rather than coding.
Product Transformation in Action
The Python integration has unlocked use cases that extend far beyond traditional steady-state simulation, with energy companies worldwide leveraging these capabilities to support operational improvements. For example, one company used Pipesim simulator’s Python capabilities to develop comprehensive automated hydraulic models covering more than 1,500 wells and 1,500 miles of production infrastructure.
This approach facilitated more efficient model creation, reducing the time required from months to days by incorporating automated workflows. The company also implemented data management systems integrating production databases, geographic information systems (GIS), and simulation models, supporting a streamlined analytical environment.
The system’s analytical capabilities contributed to more proactive operational decision-making. By implementing custom analytics, users could identify and address potential bottlenecks before they impacted production. This capability supported earlier intervention during changing market conditions and enabled timely evaluation of curtailment strategies, infrastructure expansion, and operational adjustments.Â
Other implementations have enabled well-performance monitoring with real-time alerts, production forecasting integrated with economic models, and infrastructure planning through capacity analysis.
The Embedded Distribution Advantage
This solution is set apart by SLB and Anaconda’s deep understanding of the unique challenges facing oil and gas operations, where software failures can halt production and cost millions in downtime.
In energy sector environments, where safety and reliability are paramount, Anaconda’s rigorous package scanning and verification processes provide the security assurance that operations teams demand. Comprehensive software bills of materials deliver complete transparency into every component, which is critical for regulatory compliance and security audits that are standard in energy infrastructure.
All packages undergo extensive interoperability testing specifically designed to eliminate the dependency conflicts that can bring down critical production systems. This testing ensures that engineers can focus on optimizing wells and managing assets rather than troubleshooting software conflicts during time-sensitive operations.
The solution includes professional-grade support that understands the 24/7 nature of energy operations, along with built-in governance capabilities that support the stringent IT security requirements common in energy companies. Comprehensive audit trails and package management help organizations maintain compliance with industry regulations while supporting safe innovation for engineering teams.
Looking Forward: The Future of Simulation
As the energy industry continues its digital transformation, SLB and Anaconda are positioned at the forefront of innovation. The collaboration is actively exploring cloud-native deployment options, machine learning integration for automated model calibration, and real-time simulation capabilities for dynamic production management.
The combination of SLB’s deep domain expertise and Anaconda’s comprehensive Python platform creates a powerful foundation for addressing the oil and gas industry’s evolving challenges while maintaining the reliability and accuracy that engineers depend on.
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