Optimal Operations in the Oil and Gas Industry

A company's operations are often a complex web of jobs and processes constrained by many rules, material constraints and human factors. Finding the right balance among all of the different constraints to achieve optimal operating efficiency can be a daunting task. Icosystem uses simulation modeling and evolutionary computation to find that balance.

At an oil and gas technology and professional services company, a typical job on a rig involves a crew of one or two engineers and one or two operators working for up to several days without much sleep. Generally, several tons of equipment must be shipped to the rig and back to the base for each job. Where and when the next job is going to be is, to a large extent, unpredictable more than a few days in advance, and even then cancellations are always a possibility.

Each job consists of a number of "services," ranging from nuclear and sonic tests or rock sampling to magnetic resonance measurements and seismic tests. Each service requires specific equipment and the appropriate skill set. Before and after each job, a significant amount of maintenance and testing is required. Each crew is usually on duty for 14 days and then off for 7 days. Resignations, terminations, transfers and promotions affect up to 30% of the workforce in a year. All of these characteristics impose strong constraints on how the business operates and influences how the organization can respond to deep changes in its environment, such as a market drop or rebound.

The business unit we worked with wanted to know what organizational levers it could pull to improve the utilization of its resources, decrease the amount of business turned down or reduce the number of failures. Levers ranged from changing the 14/7 schedule to adding or shutting down crews, or even changing the pricing policy.

Icosystem built an agent-based model of the organization to explore the consequences of pulling one or more of these levers. The problem was that pulling levers could have consequences that propagated through the organization, leading to unpredictable results. For example, shutting down a crew decreases cost but also reduces the ability of the business unit to service its customers. Icosystem quickly discovered that field service managers, the engineers who assign jobs to crews and make sure the appropriate equipment is available, are key to the business' operations. They are the most complex agents in the model. By modeling their behaviors (how they respond to a range of situations) we were able to replicate the dynamics of the business with a high degree of accuracy.

Using real 2001 data as a benchmark, our simulations came within 5% of the actual value across multiple dimensions (total revenues, failures, revenues per category across 23 categories of services, accepted and rejected trips, etc). Using the model, Icosystem then tested the impact of the various levers under a variety of scenarios. We discovered, for example, that modifying the 14/7 schedule could have a potentially deleterious impact on the rate of on-the-job service completion. We also found that under a market expansion, the current configuration would do well up to a 10% market increase, beyond which recruiting new crews becomes necessary. Using its approach, Icosystem was able to suggest new operational designs to its client.