Applications of Machine Learning in Oil & Gas

[Mohamed]

By Eng.KC Cheung

Accurate Modelling
Accurate Modelling - Machine Learning in Oil & Gas Industry

One of the most noticeable impacts of machine learning in oil & gas focused industries is how it transforms discovery processes.

Applications employing machine learning in oil & gas enable computers to quickly and accurately analyse huge amounts of data.

This includes being able to sift precisely through signals and noise in seismic data.

After this information has been gathered and analysed modern software applications can construct accurate geological models.

This allows operatives to predict, accurately, what is beneath the surface before drilling has begun.

It is believed that, if adopted over the entire industry, this will decrease the number of dry wellheads by 10%.

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Current Applications in Modelling
One current application of machine learning in oil & gas can be seen in the Dutch Central Graben in the North Sea.

By using machine learning in oil & gas in this manner has allowed engineers to auto-track a Jurassic seismic horizon.

This has been done with only a few manual seed points.

The latest generations of algorithms are producing more detailed and accurate results than any previous modeling.

These algorithms also don’t lose their accuracy when asked to analyse difficult terrain.

Faults or stratigraphically complex areas can be accurately mapped in detail.

There is always a need for the models to be checked.

However, so far, this application is proving itself to be quicker and just as accurate as a human model

[Mohamed]

Machine Learning can Improve Drilling Operations
Despite these developments, scientists have been slow to fully realise the benefits of machine learning in oil & gas industries.

Multivariate modelling is now becoming the most reliable way to develop resource plays.

This allows users to maximize tools such as NPV, IP30, or EUR.

One of the leaders in this sector is Drilling Infos smart application DI Transform.

This software solution allows for user-directed extraction of geophysical and geological data, robust data QC, and powerful model building.

This information can be implemented in a number of different ways.

One of the most useful applications is in the building of detailed, accurate models.

A detailed, accurate and reliable model, like those constructed by machine learning, is priceless.

It allows you to know exactly where to drill and what you will be drilling through.

This allows problems to be solved almost before they are encountered.

By using these models companies can save money and increase productivity.

It is clear that this will be an invaluable application for oil & gas operations.

[Mohamed]

Pinpointing Exactly Where to Dig with Machine Learning
Geoscience consulting firm Rock Solid Images (RSI), specialise in borehole characterisation.
RSI is a leading firm in the field of interpreting seismic data with well log data.
This is largely because they have been able to reliably combine complex geologic models with established rock physics methods.
Combining this information allows them to reliably pinpoint where, under rock formations, oil and gas is present.
RSI is currently involved in a project that aims to reduce the risks that come with exploration drilling.
If they are successful it could be a significant moment for the oil & gas industry.

[Mohamed]

Process Optimization
RSI is also working to use machine learning algorithms that can accurately model remote, hard to reach locations.

This is being done by using models of well explored and documented areas that have similar geology.

It is hoped that, in time, this prediction mapping ability will become portable.

For example, these models will be able to accurately map Barents Sea rock properties using geological information gathered in mid-Norway.

This application of machine learning in oil & gas operations can help to optimize the exploration and drilling processes.

Every time a well is dug, many fields of data are recorded.

This raw data is analysed by a petrophysicist before being fed into sophisticated software.

One such application is RSIs rockAVO software, this allows pattern recognition to take place.

Consequently, users of the software can read the rock physics of existing wells.

This information gives the user a detailed picture of the geology of the area.

By studying this known information RSI is aiming to predict the geology elsewhere in the region.

This means that less boreholes or test holes need to be dug, saving time and money.

By using machine learning this way we can make informed decisions about where to dig.