6 APPLICATIONS OF MACHINE LEARNING IN OIL AND GAS

Published in

Kagera

7 min readMar 4, 2020

The issue of environmental sustainability is a major concern for governments and players from the oil and gas industry worldwide. The negative impacts as a result of activities carried out by oil and gas companies have been a major tool not only for the livelihood and health of people but more so to the environment, such as pollution.

The risk of environmental pollution, hazards and severity can be and will be reduced with the use of machine learning and deep learning in the years to come. There has been an amazing application in chemical engineering, process safety, process control tuning, advanced dynamic and process optimization. We can see that the Oil and Gas industry when we talk about software especially machine learning solutions didn’t have any innovation in the last 15 years.

If you are a manager in the Oil and gas, you are afraid that Hazzard will happen and you can’t reduce the risk. What to do about it?

1. Predictive analytics for MPPL (Multiple Pipeline Product)

Model-based, predictive analytics lends itself to crunching multiple data sources to pinpoint risks for pipeline integrity management, while analytics for process monitoring and measurement evolve to better discover crucial variances. Kageera and Optimize Global Solutions vendors are starting to use rules and heuristic techniques to spot deviations impacting the accurate understanding of flow, production, and gas quality. Through the ability to set thresholds on deviations, measurement software can help spot problems such as missing data, suspect data, or uncollected data.

Predictive analytics for pipeline integrity is emerging, but engineers must advise analytics experts on underlying data sources and configuring KPIs.

Measurement is crucial to the midstream, as are ways of converting measurement and flow data into production reports. With solutions, look for analytics and reports that can quickly update trends and generate new reports as new data from metering or gas analysis become available.

The foundation for analytics goes all the way back to plant and asset design. Install enough instrumentation to drive better predictions.

To work well, an analytics program might identify the need for some newer instrumentation, such as replacing gas charts with EFMs, investing in new acoustic leak detection technology, or online corrosion measurement transmitters.

2. Boosting the Business Values via PyRISK™ Dynamic QRA The Frontier of Dynamic QRA and Static QRA

The oil and gas industry has been using static quantitative risk assessment (QRA) and related studies for more than 50 years to evaluate risks of major accident hazards. It is applied to demonstrate a risk to the public and employees as part of complying with regulatory requirements, which vary worldwide.

Static QRA studies typically play an important role early in the design stage of a project’s capital expenditure (CAPEX) phase; for example, when evaluating concepts, optimizing design, and establishing cost-effective risk management.

Currently, Static QRA for oil and gas assets usually provides an overview of risk at a fixed reference point in time. Such studies involve hundreds, sometimes thousands, of scenarios covering operating conditions, manning levels, and maintenance activities. The cost can be substantial, and a study may need repeating every few years. Despite this, some operators have recognized a need to utilize Dynamics QRAs as a fundamental decision-support tool for their projects and operations beyond the explicit regulatory requirements. By doing so, they gain insights into how to save costs and maintain or increase production efficiency while operating safely.

Some operators are now moving beyond static QRA models to make better use of the huge volume of data that are collected in a process involving considerable time, effort, and cost. They are starting, or planning to, update data more frequently using a dynamic approach to risk-based safety management. New cloud-based tools, combining secure data storage and analytics, are assisting them on this journey in pursuit of potential cost, operational, and safety benefits.

PyRISK™ on the Quantitative Risk Assessment project which is expected to save 60% of the work effort through the full utilization of Machine Learning.

The outcome is a faster and better decision support and simpler and easier risk communication in all phases of an asset’s life cycle. We estimate that PyRISK™ can reduce the overall cost of safety studies by up to 40 to 60% over the lifetime of an asset. Dynamic Risk Estimation & Decision Making

Kageera’s product in cooperation with Optimize Global Solutions for PyRISK™ unlocks opportunities for machine learning in risk management. It supports customers already using PyRISK™ to perform Dynamic QRA to get even more value from the data at no additional cost.

The PyRISK™ approach offers significant cost advantages. Our estimate is that an operator may end up spending 30–40% less on safety studies over the 25- to the 30-year lifecycle of a medium- to the large-sized asset, while getting much more value out of the data generated in their QRA studies.

3. PySEP ™

PySEP™: An integrated and holistic approach of delivering bespoke models which includes an immense diversity of process modeling, automation & optimization, Linear Programming, data analytics and machine learning solution for wide-ranging applications for offshore processing, gas processing, LPG, refineries, and chemical plants. PySEP™ Solutions provide the following functions, but not limited to:

Field life cycle analysis and project evaluation.
Reservoir management and dynamic nodal analysis.
Flow assurance and hydro-dynamic slug management.
Refinery linear programming.
Operator training simulation.
The data-driven decision platform empowers senior management to take the best-suited decisions in a timely manner.
Avoid unplanned downtime.

4. PyFCC™

PyFCC™: Machine learning smart solution for Fluid Catalytic Cracking delivered with out-of-the-box integrated architectures that seamlessly integrate the flow of information between operating facilities and final predictive model to sort out through the field complexities. The out-of-the-box integration is giving an opportunity to:

Proactively predicts the FCC yield in response to any potential change in the refinery feedstock.
Prediction of kinetics parameters and overall conversion efficiency weighing the actual operating conditions.
The data-driven decision platform empowers senior management to take the best-suited decisions in a timely manner.
Avoid unplanned downtime.

5. Remote operation and performance shutdown using IOT (PyMACHINA™)

Machine learning, Deep learning and the Internet of Things (IoT) that could potentially revolutionize the oil and gas industries. Having already made quite a storm in various other industries including consumer electronics, this couldn’t come at a better time for the oil industry as it currently faces dramatic drops in the price of oil.

Remote operation with use of IoT (there is no need for people checking anything at platforms
Remote notifications alarms and performance shutdown

Every 20 years we have a massive explosion of platforms we can reduce this risk with deep learning algorithms and IoT.

Remote operations with the use of Deep Q learning and robotics (PyROBOT)

THE RESULT:

No accidents, no harm to the people, no damage to the environment that is the goal of our solutions in the oil and gas industry.

6. Deep Learning risk detecting and predictive diagnostics (PyVision™)

In an industry where oil prices are constantly rising, it has become imperative to act on operating costs, productivity and lead times in order to have better returns on investment.

There are different categories of Computer Vision such as image processing (including image recognition), facial recognition, optical character recognition. This variety means that CV can be useful for many different types of industries, and various practical use cases. Here are some concrete examples of CV applications that are currently in production:

Smart Video surveillance system to help diagnose pool fire for example.
Quality control automation of preventing fire in a refinery

Conclusion

Everything mention above entails smart and quick solutions, a big reduction in operating expenses of companies and consequently fewer overheads. This recipe of success would help the entities to survive throughout the wild competitions and reduce oil prices.

On the other hand, process and chemical engineers should start pulling up their socks and refresh their minds by recovering all basics and fundamentals of mathematics to be utterly equipped with the proper knowledge when they use ML and its application in the O&G industry. In order to have successful machine learning or deep learning solution you have to have:

Initial screening with Experienced Process Flow Assurance Engineer and Machine Learning Engineer with a proven record in the field
An agile action plan with applicable and rising risks
Digest and analyze numerous sources of data