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Focus must be on fit-for-purpose; advances seen in surface-facilities modeling; BP Azeri is one good example.
In a not-yet-published survey, research analyst firm CERA has found that “integrated production-system models” are today being applied in at least 3% of global liquids production in the petroleum industry. Judson Jacobs, director, upstream technology, says CERA believes this is “just the tip of the iceberg” in terms of the contribution these models can make toward helping maximize oil & gas field production.
The key to greater use and benefit from an integrated model, said Jacobs, is that it be “fit for purpose,” and support decision making such that the effort of maintaining the model is justified in the eyes of those charged with its use.
Integrated production-system models — also referred to as “integrated asset models” or “integrated production models” — typically combine reservoir model and simulator; well models; and surface-network model in a single higher-level model. The integrated model may have its own optimization engine. For true operations support, actual production data will be fed into the optimizer from a data historian. By these means, analysis and what-if scenarios can be applied across an entire asset.
Integrated models are actually quite prevalent in the petroleum industry, said Jacobs. However, the many instances in which such models are used for weekly or monthly planning purposes — as opposed to true on-going operations support — were not included in the CERA survey results. Neither was evidence of growing use of integrated models in gas fields included in the survey.
Benefits of integrated production-system models are based on having greater understanding of the complex interactions across a petroleum reservoir, surface facilities, and, in some cases, even its supply chain and refinery networks.
“Today, the most common uses of integrated models are associated with large artificial-lift systems, and maximizing overall field production,” Jacobs said. Another important, growing use is flow assurance in subsea environments.
Operators are most likely to support integrated models when they deliver answers to something more concrete than vague promises of overall optimization. “They are most effective in addressing a specific challenge or opportunity,” said Jacobs. “For example, say a company’s goal is to maintain plateau production of an offshore gas asset. Yet drilling additional wells increases water cut across already-producing wells. A better understanding across the full suite of wells can be attained by means of the integrated model.”
In the process and refining industries, use of integrated models is quite common, said Jacobs. “Refining is a margin business and anything that increases efficiency to incrementally increase those margins is quickly embraced. We’re seeing some of those same pressures being brought to bear in the upstream sector.”
Yet several factors inhibit greater use of integrated models upstream. “While there are some technical challenges in integrating reservoir simulators, what’s inhibiting greater use of integrated models isn’t the technology,” said Jacobs. “The model should serve as a common language for interaction. But production engineers and facility engineers don’t always share the same incentives. The challenge is to align the organization so that the model is maintained and its recommendations heeded.”
Sticking to the surface
“Today the E&P space is looking to reduce costs with process-modeling and optimization applications, as a refiner would, but it also wants to increase recovery rates,” said Mike Strathman, Aspen Technology vice president, industry consulting. “To do that, you need to optimize what’s done on the surface — just as for the subsurface — through use of an integrated asset model.”
The focus of the oil-industry service companies is on the subsurface. AspenTech focuses on surface facilities. “For all their strengths, the service companies have no way to truly model a compressor. On the other hand, point-product software providers may have means to aggregate data, but typically don’t have the tools that can be used to simulate a gas plant.”
One good case example of how surface facilities can impact overall field optimization was discussed in SPE paper 118454, presented at the SPE Gulf Coast Section 2008 Digital Energy Conference, and authored by N. Jalilova and A. Tautiyev, BP Azerbaijan; and J. Forcadell, J.C. Rodriguez, and S. Sama, Aspen Technology.
The paper looks at use of an integrated production model at BP’s Azeri Field, one of company’s top investments worldwide, and possibly “one of the most complex” oil systems in the world.
There, offshore facilities include three production platforms and a gas-processing platform. Each production platform has capabilities for oil separation, flash gas compression, and gas dehydration. The gas processing platform has four parallel compression trains driven by 25-MW gas turbines, which supply high-pressure gas for reservoir injection and well lifting. The on-shore terminal has four oil-stabilization trains, oil- and gas-receiving facilities, and a gas dew-pointing unit.
“What BP has done is model the environment, integrated with real-time data from AspenTech’s IP.21 data historian, to support operations planning,” said Strathman.
The heart of the tool, the SPE paper says, is a high-fidelity asset-wide model linked to an optimization solver receiving input from the historian. Using first principles and bespoke algorithms it constitutes an “accurate representation of all pressure-flow relationships, material balances, and energy equations from the well bores to product delivery points.”
The paper further states: “The impact of a well-flow increase on the performance of the onshore flash gas compressors, or the consequences of temporary unavailability of one oil stabilization train on platform production plans… require usage of a unified decision-making framework.”
What companies are beginning to understand, said Strathman, “is that they need models to understand dynamism and they can’t do it without modeling the surface facilities. At BP Azeri, to deal with the environment, the well models are integrated with our surface-facility modeling environment, Aspen Real-time Deployment.”
Moreover, since completion of the BP Azeri project, AspenTech has introduced what it calls “Aspen Online Deployment,” to simplify combining models with real-time data. The company says it is a robust way to support greater use of models within operations, and allows users to focus on asset optimization rather than integration tasks.
Capacities and constraints
As previously stated, the Azeri system constitutes a complex network of pressure-flow interactions. Gas and the oil compression, pumping, and transport systems are tightly coupled. Capacity constraints can arise anywhere from the well heads to terminal delivery points. Business factors also influence best-use decisions.
In most cases, what constrains maximum oil production is the capacity to use associated gas by either injecting it into the reservoir or transporting it per pipeline specifications, based on four factors:
• Gas injection compressor performance
• Gas dehydration column pressure setting on the gas-processing platform
• Propane refrigeration circuits’ performance
• Low-pressure separators’ pressure setting on the production platform
Through use of the integrated production-system model, it was found that, “In a typical case, where the optimizer was run to predict the maximum attainable oil production,” the pressure set points could be optimized, “to increase production an average of 3%.”
Besides using the optimizer to find ways to co-relate offshore and onshore constraints so as to increase oil production, the optimizer also has identified process behaviors — including bottlenecks under different production scenarios — that present opportunities for performance improvement.
Throughout history, the idea of using the model of a reality to control that reality has proved to be of endless fascination to philosophers, scientists, and engineers — not to mention witches and warlocks. The twentieth century’s tremendous advances in mathematical logic and computerization have made that dream a real possibility today.
Yet the single greatest challenge to greater use of integrated production-management models in today’s petroleum fields may be answering the asset manager’s simple question, “If I don’t use it, will this field still produce oil tomorrow?”