how you can make your project greater than the sum of its parts

Systems thinking and systems dynamics are two key components to systems theory and have been the mainstay of many industries since the middle of the twentieth century. Systems thinking is the approach of studying and understanding systems of all types. Systems dynamics is the methodology and mathematical modelling of these often-complex systems. The foundational theory is that the interrelationships between component parts and subsystems are as important to understanding the behaviour of a system as the individual parts themselves.

The development of systems theory

Systems theory was initially deployed on managerial problems; however, it was soon expanded to move beyond this limitation[1]. Systems dynamics is commonplace within the aerospace and automotive industries where, working with control theory, engineering teams deploy the techniques to model, optimise and control the interrelationships of performance requirements of a system. This allows the development teams to quickly ascertain the effect of planned and unplanned changes throughout the holistic system; it also allows the engineers to identify where a change will and will not have an impact such that they can focus their attention on the areas sensitive to the change. In an oil & gas analogy, the model allows engineers to simulate the ripple of impact through a process design caused by a change in reservoir performance, for example. The aerospace industry has gone as far as to incorporate systems theory into the recommended practice for development of civil aircraft and systems, and methods for conducting the safety assessment process on civil airborne systems and equipment. In this application, the systems model is a critical facet of the functional safety requirements in an application where the failure of a signal component can have a dramatic effect on the safety of the overall system.

Both automotive and aerospace industries tend to use a pure engineering application of systems theory, where they model the interrelationships of mechanical, electrical and fluid subsystems. However, if we look at the root of systems theory, as a means to tackle corporate managerial problems with socio-economic variables, we can see how there exists for a holistic model incorporating the wider range of variables in an oil field, such as commodity economics, fiscal tariffs, geopolitical considerations, and supply and demand characteristics as well as the pure technical challenges of oil & gas exploration and production.

Systems theory in oil & gas

With systems thinking allowing for better decision making, the incorporation of a wide array of variables from, for example, gas economics to reservoir performance; and a better understanding of functional safety, we have to ask why systems thinking is not commonplace in the oil & gas industry.

The oil & gas industry has been using dynamic models to test the impact of interdependencies for a long time; linked flowline network and facilities dynamics are now common practice to design systems including the entire control system, and many operators run transient models of their assets to predict forward compositional changes and operational issues.  Another example is the use of risk based design, where the oil & gas industry no longer designs flare and blowdown systems to codes, rather it simulates the complex interactions to determine blowdown rates. However, these examples are testing technical interdependencies. The real value rests in testing the entire system of interactions of uncertainties and therefore possible perturbations in the strategic, technical and commercial frames.  Understanding these interdependencies allows us to identify the key value drivers and derive insight from these in order to optimise the value contributors and to understand the uncertainties / risks and their possible impact on value.

In 1973, Roger Naill used systems dynamics to model natural gas production and exploration between 1900 and 2020[2] and in 1985 John D. Sterman and George P. Richardson used a systems dynamics approach to evaluate techniques for the estimation of exhaustible resources[3].

However, beyond these and a handful of other high-level approaches, the oil & gas industry has tended to shun the systems dynamic approach, often citing the wicked problems facing the industry as being too complex. This is a failure to recognise systems theory as a means of balancing a holistic approach to problem solving with the necessary reductionist approach to make the problem simple enough to be manageable.

The io systems approach

io is different. By working with a systems mindset, io captures the project value drivers, translates those drivers into requirements and models the entire project system. This can be purely a technical model; however, the real value lies in the breadth of experience of the io team, who develop a techno-economic model encompassing the holistic variables, which can affect the performance of a project in the context of value drivers. This approach allows io to create a dynamic model of a project, which is resilient to macro changes, such as commodity price fluctuations and reservoir uncertainties, and micro changes, such as component performance or specification changes. The advantage of this model is that a vast array of options can be simulated, facilitating rapid decision making with increased certainty.

An io case study

An example of this is where io developed a systems model to rapidly evaluate numerous scenarios and successfully accelerated a client’s decision making process such that the project delivery timeframe was reduced by 18 months and capital spending was reduced by 30%. This was achieved by developing a holistic and dynamic model incorporating systemic components including, amongst others, a commercial model, reservoir model and compression model, all in the context of the client’s value driver of maximising NPV. This model was provided to the client’s team, who could then continue to use it to test the validity and certainty of decisions made during the subsequent phases of the project. Systems theory was fundamental to the success of this study, as was the collaboration between the client’s team and io’s wide range of experts including economists, petrophysicists and development engineers.

What can io do for your project?

The io approach expedites option selection while improving the decision quality and certainty: it is faster and more rigorous than traditional methods. By delivering the model, io allows the client to continue to evolve the complexity and accuracy of the model as additional information and insights become available during the project. Should a client’s value driver(s) change, the model can be adapted to reflect this change and quickly re-identify the optimum solution without requiring a full recycle. As we are dealing with discontinuities, for example, there is no infinite compressor solution, the models are limited by existing technologies and design constraints: they are used to identify the key interdependencies and to draw critical insights. However, as computing power and digitalisation continue to increase, the possibility for systems models to be founded on more robust project lifecycle management data, reservoir data and asset models, all linked and solved through many thousands of simulations, offers the potential for a comprehensive digital twin of an entire asset or business model.

The io systems approach is applicable for brownfield upgrades, greenfield concept selection, portfolio master planning, and decommissioning of assets and fields. Further to all this, with its well established foundation in functional safety, the io systems dynamics approach can be used to develop a safer project, not simply one with lower cost and greater certainty.

To find out how io can make your project value more than the sum of its parts, contact us at hello@iooilandgas.com.

[1] https://en.m.wikipedia.org/wiki/System_dynamics

[2] https://public.wsu.edu/~forda/Resource%20Economics%20Jan%202012.pdf

[3] http://onlinelibrary.wiley.com/doi/10.1002/for.3980040208/abstract