The low price environment has forced companies to tighten their belts, become leaner, and focus on core strengths. The need to “do more with less” has become pronounced. Efficiency and reliability are essential strategies to harness during the downturn.
Increasingly, new strategies and techniques that augment the efficiency and reliability of O&G operations will be sought out by operators and contractors alike. Failure to reduce the cost of an incremental barrel is akin to extinction in this new environment, and efficiency and reliability are key to achieving cost reductions while maximizing productivity.
Oilpro recently spoke with Dr. Gerard Gaal, Senior Reliability Consultant at Lloyd’s Register Energy-Drilling, about a new reliability model that he has developed. Key players in the industry around the globe have already begun adopting his techniques throughout the world. Dr. Gaal’s model has achieved a synthesis of safety, reliability, and cost effectiveness.
Increasingly, new workflows, innovation in process, and new methods of oilfield optimization will be adopted by O&G companies. Dr. Gaal’s reliability model is one such new idea to consider in a time when the old “its the way we’ve always done it” mantra simply doesn’t cut it anymore.
What was the genesis of your reliability model? What needs were you responding to?
Dr. Gerard Gaal: It was during my academic training that I developed the methodology regarding reliability. After I started with Lloyd’s Register Energy, I started applying the model to downstream refineries to improve reliability. When I joined Lloyd’s Register Energy-Drilling, began to apply these techniques to drilling rigs.
I first started by finding out from our clients- the people on the rig- what was keeping them up at night; what their major equipment-related concerns were. Our surveyors on the rigs provide us with a great deal of practical information about our clients’ challenges. They told me about downtime and about the costs. Then I started thinking that we can use the techniques I applied elsewhere for drilling rigs. (I previously applied my reliability techniques in the construction sector and in downstream refineries.) The surveyors have hands-on experience; they know things first-hand. Communication and collaboration is essential to the outcome of a project. I cannot do this in isolation. This is not like the academic research process.
A major concern of ours is that a failure of equipment equals a financial loss for clients. The reliability model seeks to minimize this by providing specific recommendations for preventive maintenance. When you look at a BOP, for example, it might be at the bottom of the ocean for weeks. If a failure occurs and the BOP has to be brought back on deck, repaired, and then sent back down, that could cost a company more than $10 million. Instead, with the reliability model, the BOP is assessed while it is still on deck- before you start drilling the well. You can look at the numbers and say, ‘These, these and these components are doing fine; but from my numbers I can tell by the end of the well they will have deteriorated to a level that I find [from the client’s perspective] unacceptable.’
The BOP is still there before you start drilling the well for multiple weeks. With the reliability techniques, you basically have your proverbial crystal ball. The reliability model gives you a benefit because you can start looking into the future, do your preventive maintenance, and then start drilling the well.
What are some of the key characteristics of your reliability model?
Dr. Gerard Gaal: The first step is collecting data over time on a number of components on a rig to determine when they are likely to fail. In this way, we are able to determine which components require preventive maintenance. The collected data shows us how old the rig components are, when they were last replaced, and then I can apply my reliability techniques to predict what is the Mean Time To Failure (MTTF). Then, I can start optimizing the right frequency for doing the preventive maintenance.
Can you illustrate the importance of determining Mean Time To Failure, and how this central feature of your reliability model helps operators reduce costs?
Dr. Gerard Gaal: The benefit of determining the MTTF is the ability to gauge how likely a particular component will fail. For example, when you have a single point of failure, you don’t want to go too far. You can do a cost-benefit analysis weighing the costs of downtime versus the costs of preventive maintenance: these two have to be balanced, and then you have the optimum maintenance frequency.
Once you know the MTTF, then you can start optimizing your maintenance. Here’s a parallel example: Your retirement fund. Insurance companies first determine the MTTF of mankind, then they know how much money they’ll have to spend on people who retire. It’s the same thing on a drilling rig. Once you know the MTTF, you know the likelihood of a single point of failure. If the likelihood of failure becomes so significant, and you know unproductive time becomes so expensive, you can judge it wise to invest a few thousand dollars preventatively in replacing components rather than having the equipment fail and cost you much more.
A similar approach applies with your car. You replace the tires, oil and air filter preventatively. It’s a relatively small investment, but the outcome is that your car still takes you home tonight and you prevent having to spend a lot of money on costly repairs down the road.
Your reliability model was recently applied in the field by one of your clients, Swift Drilling. Can you explain how your model was applied in this real-world scenario?
Dr. Gerard Gaal: Dutch-based Swift Drilling recently used my model on a project in the southern part of the North Sea. Swift launched a pilot project to optimize the preventive maintenance of a mud pump with statistical analysis of failure and replacement data. Now, failure of a mud pump could cost you a large amount of money, when a backup is not on hand. Swift was looking for a better solution to more precisely predicting the optimal time to conduct preventive maintenance and parts replacement.
Using my reliability model, we conducted statistical analysis of failure and replacement data. The analysis of the failure and replacement data resulted in the Mean Time To Failure (MTTF), with which the future probability of failure of a system can be determined. The censored usage until failure data information was used in this analysis. This gave the number of working hours at the time of failure of the component. After that, we determined the MTTF. With that, the future probability of failure was known. And once we knew this, the expected cost of failure was also determined.
Swift could then optimize investment in preventive maintenance and the needed number of spare parts. It’s important to say that the value of this analysis is that the results are not general; they are specific. They give a specific recommendation for the system on the rig concerned- in this case the mud pumps. This sets this model apart from others.
In the end, the reliability model determined optimal usage time to preventatively replace components on the mud pumps and avoid down time when both mud pumps are needed. Also, the optimum number of spare parts to keep in inventory was determined. This allowed Swift to maintain cost-effective inventory levels while minimizing risk of down time due to missing spare parts.
The Reliability Model & Lloyd’s Register Energy’s Broader Vision
Eric Flynn, Lloyd’s Register Energy-Drilling’s Global Marketing Manager, concluded our conversation by relating how Dr. Gaal’s reliability model fits in with the company’s broader vision.
“Lloyd’s Register is more than 250 years old and our core mission has always been to make operations safer. What companies are realizing is that the safest companies also happen to be the best performing companies. A lot of the things we apply to ensuring safety also improves the performance of companies, and reliability is an example of that.”
“A lot of companies, especially with the low prices, are realizing that they’re going to need to reduce their production costs and that improving the reliability of their people, systems and equipment is certainly a way of doing that…So we are on a technological path that the reliability model fits within.”
“Two years ago, we developed a risk model which is in use in the Gulf of Mexico right now with a major drilling contractor on several different BOPs. Dr. Gaal’s reliability model is a natural progression. We are also developing fiber optic sensor technology so we can start putting real time data into the reliability model and start producing real time condition monitoring…that’s the technological direction we’re going.”
“We’ve got a number of different groups working on these technologies and bringing them together to improve the reliability…And it’s not just the reliability of the equipment…but of the equipment, systems and the people.”
Dr. Gerard Gaal earned his PhD in Civil Engineering from Delft University of Technology, located in the Netherlands, in 2004. His academic pedigree includes time as a research scholar at the University of Michigan and the Delft University of Technology. He joined Lloyd’s Register as a consultant in 2004, and by 2010 had been promoted to the role of Senior Consultant for Lloyd’s Register Energy Americas. In 2012, he became the Senior Reliability Consultant for Lloyd’s Register Energy-Drilling.
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