Why models now for the oil and gas industry?

It's not about theory. It's about practice as the oil and gas industry enters the era of model-based solutions for projects.

By Kevin Parker February 7, 2017

It’s been noted that scientists and engineers don’t talk about theories the way they used to. Instead they talk about models. Not only that, but even in everyday talk, theory sometimes seems to imply just words, while a model is only a step away from action and value creation.

Making models begins in childhood. The taxonomy on your laptop or PC for storing documents is a kind of model, and industrial process control is about logic models to its core. The trend to talk about modern scientific and engineering projects in terms of modeling has been happening for at least 75 years.

The difference today is that we’ve entered the era of model-based solutions.

As Laith Amin, SVP for digital enterprise North America, Advisian, said, "The idea of an algorithmic model for the plant has become more practical and less theoretical. Machine learning is the first instance of a kind of algorithmic model."

Two kinds of models are discussed in this issue of Oil & Gas Engineering: reservoir models, widely used in oil and gas exploration and production; and 3-D reality modeling, used to model plant infrastructure and much more. 

Thinking with models

The back of a napkin is a kind of model that connects visual and verbal thinking in an important way, said David Goldberg, an author and an expert on higher education. A model is a system that represents one or more facets of another system, Goldberg said. Typical types include solid, prototype, graph, equilibrium and dynamic equations, and computer simulations. He also notes that good modeling involves knowledge about more than just engineering and science.

A physical model is a concrete representation that it is distinguished from mathematical models, which are more abstract, and may be descriptive or analytic. A descriptive model is about a system’s logical relationships such as the whole-part relationships that define a parts tree, interconnections between its parts, and functions that its components perform-these then would include 3-D geometric representations.

An analytic model describes mathematical relationships, such as differential equations that support quantifiable analysis about a system’s parameters. Analytic models can be further classified into dynamic and static models. Dynamic models describe the time-varying state of a system, whereas static models may represent the mass-properties estimate or reliability prediction of a system or component.

Workflow makes it go

The engineering modelling solutions being introduced commercially into the process-production and discrete-manufacturing industries will combine multiple kinds of simulators into a single modeling environment.

This is important because in the multidiscipline collaboration needed to design, develop and construct, for example, a utility plant in an oil-and-gas industry facility, it is hugely efficient if the workflow of the multiple design iterations involved is fast and efficient.

This is just the case with Schneider Electric Software’s just-introduced SimSci SimCentral for process industries, which combines fluid flow, steady state and dynamic simulation in a single environment.

"These independent simulation tools have established use cases and workflows," said Tobias Scheele, SVP software, global solutions, Schneider Electric Software, "but unifying them in a single environment takes the complexity out of the workflow. The translators used in the past to allow combined use of the simulators resulted in loss of fidelity."

Kevin Parker is a senior contributing editor to Oil & Gas Engineering magazine.

Original content can be found at Oil and Gas Engineering.

Author Bio: Senior contributing editor, CFE Media