Machine learning is a rapidly growing area in the oil and gas industry. It can be used to analyze seismic data, well logs, and other geological data to identify potential oil and gas reservoirs. Machine learning algorithms can also analyze production data and identify patterns that can be used to improve well performance. Overall, machine learning has the potential to improve efficiency, increase production and reduce costs in the oil and gas industry, says data and analytics giant GlobalData.
GlobalData’s thematic report Machine Learning in Oil and Gas provides an overview of machine learning technology and its growing importance in oil and gas operations. It also highlights the work of leading oil and gas companies such as BP, ExxonMobil, Saudi Aramco, Shell and Total Energy in developing and implementing machine learning tools to address business problems.
GlobalData Oil & Gas Analyst Ravindra Pranik commented: The former has impacted global energy demand, while the latter has wreaked havoc on oil and gas supply chains following sanctions against Russia, the world’s largest energy supplier. This has created a need for enhanced oversight and performance optimization across all functions including project design, construction, logistics, inventory management and maintenance. Above all, companies also want to better monitor market demand in order to adjust production. The goal is to find every opportunity to reduce costs in the long run. ”
Machine learning benefits companies in this scenario by driving automation, process improvement, and demand forecasting. We can help you modernize your maintenance practices, detect leaks, streamline data management and documentation, and optimize your inventory and supply chain.
Puranik continues: “Machine learning is a rapidly growing area in the oil and gas industry that has the potential to revolutionize how companies explore and produce oil and gas. and used to help interpret seismic data and optimize the performance of operational equipment.This technology is also very useful in predicting potential equipment failures, thereby preventing nasty accidents. , increasing operational safety.”
The Organization for Economic Co-operation and Development (OECD) estimates that AI could add up to $16 trillion to global GDP by 2030. This equates to over 10% of his gross world product.
Puranik concludes: “Oil and gas companies have deployed machine learning algorithms to track performance across various assets such as drilling rigs, pipelines, LNG facilities and refineries. Additionally, new use cases for AI in carbon sequestration are emerging among industry players: Researchers such as ExxonMobil and Equinor are using machine learning tools to analyze seismic data. to narrow down potential storage sites for captured carbon dioxide.Machine learning has great potential in the energy sector, and new applications for automation and optimization will continue to be discovered. prize.”
Source: Global Data
