In an industry like oil and gas, where asset failures and unscheduled downtime can be catastrophic to the bottom line, few things are as critical as having an efficient and effective system in place to monitor and repair assets. It’s for this reason that predictive maintenance is becoming an increasingly popular approach in oil and gas operations.
Predictive maintenance is the process of analysing patterns in machine sensor data to predict impending asset failures before they occur. These predictions allow operators to prepare for failures by either performing the needed maintenance on the asset while it is not in use or bringing in a replacement asset beforehand.
This method of asset monitoring is a genuine cost-saver. Research by the Electric Power Research Institute has found that the annual cost of scheduled maintenance, performed at regular, predetermined intervals, is $24 per horsepower. Reactive maintenance, which performs maintenance on assets after they have run to failure, costs $17 per horsepower per year – though this is before considering the additional costs of the safety hazards or operational damage that may be incurred by asset failure. Predictive maintenance was found to be by far the most cost-effective method, costing only $9 per horsepower annually and including no hidden costs or dangers.
Traditional troubles
Unfortunately, many oil and gas operators are aware of the benefits of predictive maintenance, but still find it beyond their reach. Predictive maintenance has traditionally required teams of data scientists and technicians to constantly monitor and analyse incoming sensor data from all assets. This is hardly a scalable approach even before factoring in the increasing scarcity of data scientists, who are in high demand but very short supply. In 2016, according to Stitch Incorporated, it was reported that there were 6,500 total people listing themselves as data scientists on the LinkedIn website, but 6,600 job listings for data scientists in San Francisco alone.
Data scientists are a hot commodity, and the companies that are lucky enough to have scooped some up can’t afford to waste them on routine data collection.
Furthermore, these manually created predictive models take a great deal of time to create, perform poorly in extreme or unusual conditions, and can’t adapt to changing conditions–alter one variable of the asset being monitored, and the model is instantly rendered useless.
The missing piece of this puzzle is automated model building (AMB). Simply put, automated model building is artificial intelligence (AI) that builds AI. AMB technology uses machine learning techniques to create powerful, flexible predictive models in a matter of weeks or even days. These models can continually learn and refine themselves over time and require minimal human involvement and even less data science or programming expertise to create, implement, and use.
Automated model building is particularly well-suited for the oil and gas industry, as its models perform well even in tasks with sparse data. Traditional predictive models require data from a minimum of fifteen failures to function properly, but the vast majority of drilling operators will elect to perform preventative maintenance on assets before they fail, and therefore don’t have much in the way of failure data to train a model. AMB doesn’t need those fifteen failures, as it is able to find trends and build and train reliable models in data sets with few or no failures at all. These models are still far more accurate and robust as compared to their traditional counterparts.
Case study
In one case, a major exploration and production (E&P) operator used AMB to quickly build a solution capable of predicting workover needs and production in oilfields. This E&P operator was looking to minimise costs by using an automated, reliable solution to monitor the state of all the wells in a field. Specifically, it wanted to do so by assisting process engineers in identifying wells in need of maintenance, providing those engineers with insights on the potential return of individual wells to help prioritise maintenance work, and predicting failures far enough in advance to minimise unscheduled downtime.
Using an AMB platform, the operator was able to build new models in a matter of days using a fairly limited data set of well static properties, such as location, reservoir characteristics, and monthly oil, water, and gas production. These models had dramatic results, allowing the operator’s process engineers to predict workover, rod change, and cleaning operations needs in 12 out of 17 wells across the field with 70 to 80% accuracy.
The models were able to predict asset failures and other maintenance needs around three to six months in advance.
Finally, the models developed with AMB revealed insights on production potential for individual wells, thus allowing engineers to focus their efforts on the most promising wells.
All in all, the E&P operator was able to dramatically increase its insight into its wells and their maintenance needs, at a far lower cost and in far less time than would have been possible with a more traditional approach.
This is why automated model building is the future of predictive maintenance: because it makes predictive maintenance accessible to operators in a way it never has been before and yields better results than any other approach.
