Data Driven Engineering EPCM is revolutionizing how engineering teams operate within oil and gas projects. The engineering phase itself generates immense volumes of data, ranging from design specifications and 3D models to simulation results and critical supplier information. Forward looking EPCM firms are now strategically capitalizing on big data and advanced analytics to significantly improve engineering outcomes and overall project performance.

By harnessing this vast data effectively, these firms can uncover actionable insights. This enables them to:

• Optimize designs
• Proactively predict potential issues
• Accelerate project schedules

This article discusses key strategies by which EPCM companies are transforming data into a potent strategic asset, a core principle of effective Data Driven Engineering EPCM.

Building an Engineering Data Warehouse for Data Driven Engineering EPCM

A key enabler of truly data driven engineering is the creation of centralized engineering data lakes or warehouses. In traditional projects, critical data often resides in isolated silos. These can include individual engineer’s files, separate discipline specific systems, and disparate contractor databases.

Modern EPCM firms are investing heavily in advanced platforms that aggregate and meticulously organize all engineering data for easy access and robust analysis. For example, AVEVA offers an Engineering Data Warehouse solution. Companies like Shell have adopted this to underpin their ambitious digital twin initiatives (Source: AVEVA). By implementing an engineering data warehouse, Shell ensures a common digital thread. This provides decision makers with contextualized information, flowing seamlessly from design through operations.

This single source of truth approach dramatically reduces time wasted searching for the latest documents or laboriously verifying data integrity. Similarly, Technip Energies standardized on a single data model for project controls and connected it with engineering systems across their vast 15,000 employee organization (Source: Hexagon Case Study). As a direct result, they:

• Digitalized both cost control and materials management
• Can now retrieve vital information, like material takeoff status, in seconds instead of days

These examples highlight that establishing a robust data architecture is foundational to leveraging analytics in Data Driven Engineering EPCM. To further understand the critical importance of data organization, consider exploring The Strategic Imperative of Data Governance in EPCM.

Analytics for Design Optimization and Risk Mitigation in Data Driven Engineering EPCM

Once engineering data is centralized and clean, powerful analytics can be applied to drive better engineering decisions. EPCM firms are using algorithms to analyze historical project data and live design data to spot complex patterns that humans might easily miss. For instance, machine learning models can compare thousands of past piping designs to suggest the most efficient layouts for a new refinery, reducing material costs and complexity.

Predictive analytics can issue timely warnings to engineers if a chosen design route or equipment selection has led to issues in similar projects previously. This allows for proactive risk mitigation. Bechtel’s approach to big data exemplifies this. They partnered with data scientists to apply deep learning to the sequencing of construction megaprojects (Source: Bechtel).

This effectively treats the construction plan like a complex game. It helps find optimal sequences that humans might not intuitively see. While this directly impacts construction, the model relies on rich engineering and execution data, reinforcing how intertwined data driven design and construction are. Additionally, project teams are implementing:

• AI driven clash detection
• Rule based design checks

These tools automatically flag inconsistencies or non compliance with engineering standards in real time. They serve as invaluable “digital assistants” to engineers in Data Driven Engineering EPCM.

Real Time Dashboards and Informed Decision Making in Data Driven Engineering EPCM

Decision makers at EPCM firms and their clients benefit immensely from real time analytics dashboards that surface key metrics and trends. Modern engineering dashboards might display the progress of deliverables, design deviations, and even carbon footprint implications of design choices.

When paired with predictive models, these dashboards can accurately forecast outcomes. For example, they can predict if the current pace of engineering will meet the needed schedule. They can also highlight if potential late supplier data could impact downstream activities. EcoSys (Hexagon) and similar project performance tools are often configured to integrate engineering data for this purpose. This is exemplified by Technip Energies using EcoSys across all projects globally (Source: Hexagon Case Study).

The payoff from Data Driven Engineering EPCM is significant. One Technip Energies initiative cut data gathering times from 10 days to just 10 seconds by integrating systems, empowering managers to make timely decisions with confidence. In practice, a project manager can now drill into live data to understand the status of every engineering document or query outstanding. They can then reallocate resources if analytics show a bottleneck forming. This proactive management, deeply grounded in data, greatly reduces the likelihood of surprises later in the project.

Conclusion: The Necessity of Data Driven Engineering EPCM

For industry professionals, the message is clear. Those who build strong data infrastructures and robust analytics capabilities are gaining a profound competitive edge. They not only deliver projects more efficiently but also provide the transparency and predictability that clients increasingly expect. As engineering becomes ever more complex, a data driven approach is transitioning from a nice to have to an absolute necessity in the EPCM world.

To further explore how data centralization underpins these advancements, consider reading our insights on Maximizing Asset Lifecycle Value Through Digital Transformation. Ready to harness the power of Data Driven Engineering EPCM and elevate your project delivery?Contact our experts today to explore how our expertise can drive your success in leveraging advanced data and analytics for your EPCM projects.