Deep Learning Applications & Advanced Analysis in Hydrocarbon Refineries

Deep learning is an umbrella term that covers many things. In a strict sense, it is using computer programs to do what intelligent humans could do, and often doing it even better. Deep learning (cognitive science), which is the most popular computer science course now in USA and European universities.

Five Attributes of Deep Learning

The cognitive tasks of AI can be divided into five categories:

(1) Perception.

(2) Learning.

(3) Forecasting.

(4) Reasoning.

(5) Coordinating.

With perception, deep learning can understand the environment with sensing, and detect and recognize occurrences; is that smell a fuel leak? From this it can learn by synthesizing that information into knowledge; this could be learning the relationship between temperature set points and distillate yield.

Extract value from the generated data by being able to forecast with high precision. We can simulate outcomes such as reservoir performance (IPR) at various topside operating conditions.

When it comes to solving logical problems, or reasoning, deep learning can make decisions or suggest the best solutions; given what I know, what is the optimal distribution of my products at different terminal sites?

Despite the expanding range of problems deep learning can solve, there is one thing which no deep learning program has been able to replace humans in: defining the problem itself.

Given the obvious benefits that can be derived from adopting ML in refineries, what are the challenges that downstream oil and gas companies face when they embark on a program?

One of the biggest mistakes that companies make is that they embark on deep learning without first defining the problem. They collect lots of data, but do not know what to do with it, since they do not know what problem they are trying to solve by collecting all this data.

Machine Learning roles in Dynamic Nodal Analysis

One of the most popular subcategories today is machine learning. In fact, machine learning has become so popular that many people equate machine learning to deep learning.

Machine learning is popular because it overcomes scientific unknowns through large quantities of historical data, and hence has made fortunes for companies that in the past found their data too complex to interpret.

Machine Algorithm Definition:

Machine learning is based on pattern recognition, and machine learning methods consider all data as either inputs (features) or outputs (prediction). Multiple inputs are fed into an algorithm that produces an output. If the output does not match the actual data, the algorithm is tweaked to do better next time. This is called training in machine learning.

Because machine learning relies on large quantities of data about the same subject, it is better at very focused problems and parameters, such as what is the relationship between vibration and engine failure?

Machine learning behaves poorly when the problem is a system problem with more complexity, such as a refining process or a logistics supply chain for oil that has many moving parts, which prevents repeating patterns.

It can also struggle when most of the information is domain specific, such as the pressure setting on the steam boiler that has a certain relationship with the steam energy generated and subsequently the processes in the distillation column. Such domain-specific information from the data cannot be utilized unless an engineer or data scientist has spent time to structure and correlate the data to correctly represent the relationship between them; this is something that machine learning cannot replace. The cost of this manual work is often ignored when companies want to train their data. They end up not having meaningful conclusions.

Another problem occurs when time and sequence are important. Most machine learning programs do not incorporate time-based patterns. For example, the best way to predict the loading queue at the terminal in the next hour is to count the current queue length. Fuel demand estimates at a retail fuel station require information such as which month of the year and which day of the week it is in order to predict more accurately. This is where time series come in. The central point that differentiates time series problems from most other statistical problems is that in a time series observation are not mutually independent. Rather a single chance event may affect all later data points.

Yet, existing time series technology alone does not solve all the new problems either. Enterprises are trying to aggregate and store all data in time series format, which understands time, but misses all domain correlations. This correlation across the domain of operations is critical for gaining contextual intelligence. Even though historian has been a familiar technology to first use, it is not sufficient.

Companies should consider the nature of the problems before they invest. You need the right AI tool based on the problem you have defined. Define the problem first, so that you can select the right tool. Do not make the auto industry’s mistake.

Categories of problem in downstream oil and gas

1-Scheduling/ allocation/ coordination problems:

2-Process optimization:

3-Monitoring, detection, faster responses:

4-Supply chain logistic:

Terminal Efficiency

In downstream terminals, maximizing loading efficiency can have a significant impact on the performance of terminal operations. Scheduling is a complex process (truck arrival time, terminal queue, loading bay queue, loading time) and the multiple combinations of trucks that require different products, against the required volumes and flow rates from pumps into different loading bays. The number of calculations becomes exponential as you consider all the variables in this process and becomes a nearly impossible task for humans.

Today’s manual process is typically experience based with some amount of guesswork, which does not optimize terminal operations. With predictive analysis from A-Stack, these different variables can calculate optimized scheduling, to determine for each truck which particular loading bay it should use. It minimizes overall queuing for the terminal and maximizes loading efficiency. It improves supply chain logistics.

Conclusion

Five categories of intelligence can be concluded based on various modes of operations and different areas of applications in oil and gas industry.

1 Future intelligence: the ability to forecast future events with good confidence & high accuracy based on the learning from current events.

2 Historical intelligence: the ability to understand what happened.

3 Contextual intelligence: the ability to correlate multiple factors in a context and make sense of what is happening.

4 Domain intelligence: the ability to deepen domain knowledge/science

5 Logical intelligence: compute numerous logical conditions simultaneously and find the solution.

Until next time,

Emad Gebesy, Founder of Optimize Global Solutions, Subject Matter Expert