Engineering know-how and experience always have been central to the design and safe operation of an E&P facility. The growing scarcity of experienced workers is creating an experience gap that engineering models can help bridge. They provide a common language for capturing ideas in design models and leverage the embedded “experience” to help optimize asset performance in operations.

Industry business challenges
Today, several business challenges are raising the importance of evolving the use of modeling technology to address a complete asset life cycle through financial discipline, re-examining existing assets, and the shortage of skilled and experienced engineers.

Financial discipline is the pressure to reduce capital costs, make investment decisions quicker, and achieve smoother startups, giving greater focus to engineering excellence goals. With the cost and risks associated with finding new reserves in deeper waters and more remote locations growing, E&P companies are looking to mature fields for “new” reserves to achieve business success.

The role of process modeling has evolved from being focused on individual calculations to addressing integrated conceptual and front-end engineering design problems. (Charts courtesy of Aspen Technology Inc.)

The trend of a growing shortage of skilled and experienced engineers is having a notable impact on the E&P industry, which will continue over the next decade. Use of simulation models to capture process design knowledge and experience for use by incoming workers who are well-educated but inexperienced helps overcome this trend.

Key trends in process modeling
The role of process modeling has evolved from being focused on individual calculations (e.g., steady-state mass-and-energy balances) to addressing integrated conceptual and front-end engineering design (FEED) problems such as economic analysis, safety and environmental impact, energy use, operability analysis, and equipment design.

Process models developed for engineering design now are being used in plant operations. Owner-operators are increasingly using models to support operating decisions to optimize processes in real time and to improve the accuracy of planning systems. This evolution and use of best practices results in asset optimization and better business outcomes.

Better capital decisions: Economic work process
The integration of economic analysis with process design yields sizeable benefits. By connecting process simulation with cost estimation systems, costs can be calculated and tradeoffs made concurrently.

These estimates can include a realistic view of operating cost as well as capital. Process engineers do not need to wait until a formal package is handed over to the estimating department before gaining accurate understanding of the economic differences between alternative designs. This saves time and leads to better decision-making.

Fluor has realized the benefits of integration between process engineering and economic evaluation as “Cost Optimized Design,” including:
• Performing front-end estimates that enable greater focus on technology/cost tradeoffs early;
• Improving quality of estimates using a globally standardized tool, using discipline experts to define the estimate, and by increased communication among disciplines; and
• Fostering cost awareness during design so that process engineers realize the impact of decisions.

Operability of a design: Operability workflow
The goal of the operability workflow is to leverage the same steady-state unit operations models from design into dynamic models. The use of dynamic models for safety and operability analysis clarifies whether a design simulation solution is stable under real-world dynamic conditions. The engineering process to convert a steady-state model to dynamic now is more automated and easier.

Integrating process engineering workflow can increase engineering efficiency and reduce time for basic engineering.

Offshore operations place a premium on operational integrity. Xodus Group employs dynamic process models to analyze real-time data to provide a predictive view on asset performance. This allows advanced warning of potential threats and root-cause analysis of hydrate blockages, which also has helped to increase production. Using the models also provides data to support other business decisions including allocation, commissioning, gas quality, and data validation.

Build models once and reuse often: Conceptual to basic to detailed design workflow
Integrated basic engineering solutions is another technology-based advancement that is opening new ways to share knowledge, collaborate more effectively, and complete the entire design process in less time. The heat and material balance and flow sheets from simulation studies are direct inputs into the basic engineering process. Multiple disciplines define the FEED and then pass that information and process simulations to detailed design. Making this process seamless improves speed and accuracy.

WorleyParsons links together process simulation, basic engineering, and detailed design into a workflow providing an estimated 25% increase in engineering efficiency and 30% reduction in time for basic engineering.

Reuse design models again: Moving models into asset operations
Process models developed during design phases represents a significant engineering effort and knowledge base. Operators have a validated tool to test changes in conditions and learn about capability. Additional benefits include engineering productivity and reduced capital expenditure/asset lifecycle costs.

Application of process models to plant operations spans a spectrum from offline steady-state simulation to debottlenecking analysis through to closed-loop, real-time optimization for optimum process performance.

Process models often are used offline with real-time data to help evaluate the impact of process changes or adjustments to a facility. These models also can be used to evaluate the capital/operating changes required to accept a new production stream into an existing facility.

When there is a validated, rigorous simulation of an asset, using it in real time can provide great insight into asset performance and identify improvement possibilities such as:
• Implementing alerts on actual performance versus capability and identifying where things are underperforming and why;
• Understanding the implications of changes to operating conditions;
• Understanding energy use and environmental footprint;
• Monitoring key performance indicators to avoid problems and disruptions;
• Reconciling conflicting plant data (e.g., measured mass flows in and out of a unit may not normally balance); and
• Optimizing the process with real-time controls that can consider many competing variables simultaneously such as profit, emissions, energy use, throughput, variation in well production, temperature, etc.

BP leveraged rigorous models of its Trinidad and Tobago asset to analyze a number of real-world operational scenarios that included, among others, meeting gas demands while maximizing condensates production, bringing on a new high-yield well, and analyzing the extra gas production potential. Preliminary results identified a potential 7% extra condensate production, equivalent to 1,900 b/d of oil.

Future directions for lifecycle modeling
The biggest challenges in integrated engineering include collaboration among engineering disciplines and moving rigorous analytical models into the operating environment. These challenges have driven new innovations in modeling tools such as modularized modeling systems, models in engineering, and common engineering data backbone.

Process modeling systems can be designed for use in a modular fashion throughout an asset’s life cycle. One example is the ability to integrate with the US National Institute of Standards and Technology’s ThermoData Engine physical properties database. This functionality is needed in many aspects of process modeling, and having it modularized so it is available when needed is an industry innovation. There is only one solution provider that has separated its comprehensive physical properties information bank as a reusable resource, making it available as a “standardized component” to ensure maximum flexibility and consistency.

Another example is modeling discrete units or processes in the plant so they can be included or excluded based on the operating situation. These components are simulated independently, but connected as part of a bigger model. They are used by simulating optimization, basic engineering, economic evaluation, and advanced process control applications.

Models in engineering provide the capability to leverage models later in the design process, including basic design, startup, and control. Modeling can be performed in-plant without intervention of design engineering. Models also can solve operational issues quickly, which makes equation-oriented solvers a necessity for optimizing complex facilities.

Common engineering data backbones provide a life cycle database that incorporates unit operations models; process, equipment, and instrumentation data; and control information to facilitate lifecycle optimization.

Process engineering models developed during conceptual design are increasingly being leveraged later in the design process and in operations. This is enabled by a variety of technical developments that make these analytical models usable by other disciplines and plant staff. The benefits that already have been achieved can be measured concretely in saving money, manpower, and time.

Furthering those results, future innovations in process simulation software will lead to additional modularization of unit operation models and improve ease of use. By integrating that capability with work processes, rigorous models will become an even more valuable tool and more widely used in the optimization of E&P assets.

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