The Effects of Machine Learning on Asset Performance Management

Nowadays, executives in the oil and gas industries have discovered new methods of maximizing the value and reliability of their respective assets with asset management. This asset performance management is, in turn, powered with the help of machine learning and an industrial-grade Internet of Things (IIoT). By synergizing asset performance management with machine learning, companies are gaining the ability to extend the lives of their assets while achieving optimum reliabilities.

Gross Margins Eaten by Unplanned Downtimes

It has been found that an average of 15 percent gross margins is eaten simply because of unplanned downtimes. That being said, the best performances of production schemes without asset performance management is said to result in an opportunity loss of 5 percent. If both these losses were to be eliminated, production management and maintenance will have to work hand-in-hand while figuring our new ways to produce better results.

The Traditional Approach for Reliability

Traditional methods of achieving reliability in production processes were done by building principle forms of the asset while tuning it with the help of real-time data. While this was being done, production units were also seen to implement rules and corrective factors for accuracy. Finally, the respective outputs of models were also compared in order to determine and highlight deviation statistics that set them apart from efficient conditions.

These traditional methods, however, simply used to analyze data captured from assets. This is because of the lack of organizations’ abilities to have a look at upstream factors in order to identify casual behaviors that tended to degrade assets. Due to this, it was impossible to signal companies other than times when damages of assets became evident. By the time these detections were made, it was probably already too late for contingencies.

The Conventional Approach for Reliability

Conventional methods of predicting performance had been engineered about 40 years ago and worked in a manner to be based on engineering equations. These equations were synergized with rule engines and statistical techniques but most of them still relied on human output.

Machine learning, on the other hand, has only recently been introduced in the pipeline and solves the problem in the very same manner – only with differing levels of accuracies and human involvement.

How Machine Learning Improves Asset Performance

Modeling techniques have been known to require extensive skills and experience to be matched with calibration techniques in order to be successful. Firstly, the principles of machine learning need to be understood in relation to specific behaviors of assets. Once this has been done, dynamic and real-time models can be built that offer predictions and forecasts of the behavior of assets at any given point of time. While doing so, they also offer an in-depth understanding of what levels and qualities of performance should be expected.

Solving the problems of unplanned downtimes and disruptions is do dynamic and challenging in nature that human involvement in asset performance management is quite redundant. There are thousands of variations that can be seen occurring simultaneously in gargantuan processes. This is why some modeling techniques find it hard to predict exactly what kind of trends or patterns in performance should be expected and will lead to unplanned events.

First Principle Models vs. Machine Learning

Engineering or First Principle Models tend to only portray perceived, expected or estimated bases that are in terms of hygienically clean and best case performance scenarios. ‘How often does a particular mechanical asset run in this manner?’ ‘Does it work in the same manner at throughputs of 30, 50, 100 and 110 percent?

Machine learning, on the other hand, tends to learn in terms of the real world and actual behaviors of assets – and that too, in every possible condition. These conditions will also include seasonal variations, diverse operating campaigns, and changing cycles of duty of shutdown/start-up. What’s more? Machine learning also takes into account deteriorating levels of mechanical performances and processes.

Machine learning also has the ability to mine asset data and processes in order to trigger early warnings. By doing so, it is doing all the heavy lifting in order to look for patterns in all the processes that might signal future problems in assets.

This is done by firstly identifying behaviors of the various set of processes which are known to be root causes of degradation. In order to identify these processes much earlier on in the depreciation phase, machine learning implements risk analysis and continually predicts possibilities of failure in the assets. These predictions are usually made weeks or even months before the actual bottleneck occurs and hence provides ample time to coordinate, plan and take appropriate actions.

In traditional approaches, executives were only known to react to situations. Machine learning, on the other hand, uses its respective applications to come up with models that do not employ factors such as the traditional thermodynamic polytropic equations and material/heat balance. They do not even use the traditional statistical interpretations, logic and rules. They simply measure signatures of failure while continuously adapting to real-time data. Traditional approaches, however, used to only model machines.

It’s Now Time to Modernize

If oil and gas companies are still relying on simple first principle models, it’s high time they modernize their asset performance management game. Using a combination of their current asset management program and cutting edge machine learning, companies will be able to experience powerful detection and retention against the riskiest operating conditions and processes.

With the help of these combinations, companies will also be able to explain certain explicit conditions whenever they are implementing their form of asset performance management. Here, machine learning will fine-tune and calibrate their assets models automatically without the need for redundant programming rules and extensive human guidance.

Asset performance with machine learning is truly the best of both worlds. It’s timely, accurate and provides the best feedback for the best possible levels of performance!