Machine Learning in AI In Oil And Gas
Leader: Machine Learning can change decision making based on the data that is expansive. It gives the computers the ability to learn without being programmed.
The oil and gas industry has been slow to leverage technologies, such as the Internet of Things, Machine Learning, and Artificial Intelligence (AI). Nevertheless, progressive companies have begun to implement these technologies to drive incremental value for their organization.
Artificial Intelligence helps to make strategic decisions from the vast pool of data that is available. It gives the computers the capability to learn, without being programmed. Algorithms can explore a gamut of data in procurement businesses, even public, private, and company-owned information.
Machine learning oil and gas
AI has the potential to disrupt the oil and gas industry. It recognizes the subtle patterns in data and helps to predict when servicing is required. The best part is that AI oil and gas systems comprehend operational elements better, and generate predictions instantaneously. And with a gamut of data, we can recognize stress points and extrapolate.
Now let us look at how Machine Learning in ai is applicable in the Oil and Gas industry.
Capital planning oil and gas software businesses are now leveraging Machine Learning algorithms for case-based reasoning. Machine Learning algorithms work by searching databases of known problem cases in real-time to recognize cases that are similar to the issues being encountered. Whenever a similar description is identified, the system drills deeper to understand what kind of actions were taken to identify the issue. Oil and gas companies can use algorithms to examine data like weather patterns to predict demand.
Today, qualms in reservoir exploitation are high when trying to find out how a rock formation responds to an induced hydraulic fracture treatment. This method can be achieved by making suitable use of complex data through Machine Learning. Interestingly, the outline of pattern recognition via Machine Learning in ai enables speed in history matching. Machine Learning software is a combination of artificial neural networks, fuzzy set theory, and genetic algorithms. Machine learning techniques in oil and gas lay emphasis on reservoir and production management optimization of hydraulic fracturing, and reservoir simulation.
Drill Floor Automation
Attaining real-time information on ongoing operations holds paramount importance in drilling operations. So, modern rigs have ample sensors to measure vessel operation and information about the down-hole drilling environment. Machine Learning enables advanced computer-based video interpretation that helps to achieve continuous and precise assessment of several different occurrences via pre-existing video data. In fact, developments in Machine Learning augment performance across a range of video-based sensing tasks. Therefore, Machine Learning in ai can be used to improve safety, improve efficiency, and reduce costs.
Moreover, the use of Machine Learning in oil and gas does not stop with exploration and production, as several operators in the petrochemical refining sector lay emphasis on these algorithms to improve the performance of their facilities.
However, apart from a few companies, the oil and gas industry lags, when it comes to leveraging Machine Learning. The breakdown of information silos and making data available to decision-makers will be an important factor to improve the situation in the coming years.
Benefits of A.I and Machine Learning techniques | Machine learning vs ai
According to DNV GL, digitalization was a key priority for all the companies in the oil and gas sector, as it aims to automate all operations.
Artificial Intelligence and Machine Learning techniques will assist the entry-level staff, fix the knowledge gap created by millions of hours of on-the-job-experience, who leave the industry in one swoop.
Machine learning oil and gas
The oil and gas industry will turn to machine-led intelligence to drive improvements such as enhancing processes at the front end of the hydrocarbons life cycle or provide support for inexperienced specialists.
Other AI applications
Intelligent robots: Robots are designed with AI capabilities for hydrocarbon exploration and production in order to enhance productivity.
Virtual assistants: Online chat platform aids customers to direct product databases and at the same time, processes general inquiries with the help of natural language.