Have you ever wondered how to use machine learning in the oil industry? This branch of artificial intelligence has brought numerous benefits to the industrial sector. Thanks to its application, companies can benefit economically, logistically, operationally and much more. On this occasion, we will talk about the relationship between machine learning and the oil industry.
Let’s get to it!
Here are some of the applications of Machine Learning in the oil industry
Minimization of errors and failures
With the help of intelligent data processing, it is possible to create machine learning models that can be used to detect when a production error is about to occur. Thanks to this, it is possible to take preventive actions to avoid the problems that these failures generate for the company’s schedule and finances.
This error prediction system with machine learning for oil, in turn, supports the constant improvement of processes and benefits the quality of the organization in its different products or services.
Controlling environmental impact
With its air emissions, hazardous waste and other pollutants, the oil industry has a significant and negative impact on the environment. Through ML, oil and gas companies can make predictions about the impact their operations will have on the environment, allowing them to minimize the resulting consequences.
Optimization of drilling processes
With the help of machine learning, companies can improve their operations through the use of data.
Through these insights, they can, for example, determine where to drill, evaluate the convenience of using certain cutting tools, test different hydraulic fracturing techniques, among others.
Logistics and inventory optimization
Machine learning processes provide essential information for managing companies’ logistics and inventory processes. By means of intelligent data analysis, it is possible to determine the best transport routes for logistics or to analyze historical data to forecast demand.
Project management
Projects deployed by the oil industry are known to be large, highly complex and financially demanding.
By using machine learning facilities, decision making is optimized, future scenarios are foreseen and accurate risk analysis is performed, as well as the analysis of trends relevant to the organization.
All this increases the probability of project success, minimizing unforeseen events or costly errors.
Reducing risk for employees
With artificial intelligence it is possible to dig into large amounts of data, sensors and historical information to find risk factors and predict workplace accidents.
Maintenance support
Error prediction allows companies to plan and deploy preventive maintenance actions to avoid damage to machinery or systems used during operations.