Machine Learning Set to Transform Oil and Gas Industry, Says GlobalData
Machine learning has the potential to revolutionize the oil and gas industry by improving efficiency, increasing production, and reducing costs, according to data and analytics company GlobalData. The technology is rapidly growing within the sector, with several practical applications highlighted by GlobalData.
One of the major applications of machine learning is the analysis of seismic data, well logs, and other geologic data to identify potential oil and gas reservoirs. Machine learning algorithms can also analyze production data to identify patterns that enhance well performance.
Machine learning is commonly used to automate repetitive tasks and assist in seismic data interpretation and equipment performance optimization. It also plays a vital role in predicting equipment failures, preventing accidents, and ensuring operational safety.
Oil and gas companies are leveraging machine learning algorithms to track performance across diverse assets, such as drilling rigs, pipelines, LNG facilities, and refineries. The technology is also aiding in inventory management and supply chain optimization.
Emerging use cases for machine learning in the industry include carbon sequestration, where researchers are employing machine learning tools to study seismic data and identify potential sites for carbon dioxide storage.
GlobalData projects significant growth in the artificial intelligence market, with AI hardware, platforms, consulting, support services, and specialized applications expected to be worth a combined $140 billion by 2026.
Digitalization, driven by the adoption of technologies like AI, big data, cloud computing, and robotics, is already improving productivity and profitability in the oil and gas industry. It streamlines operations, cuts costs, and enhances decision-making processes.
Despite previous challenges, such as skilled labor shortages and data security concerns, more oil and gas companies are upgrading their assets with digital technologies due to their transformative potential and ability to meet future energy demands.
The adoption of digital technologies focuses on minimizing equipment downtime and enhancing operational performance to increase company revenues. Streamlined processes and improved operational visibility through digital workflows are the key goals.
In conclusion, machine learning and digital technologies are set to transform the oil and gas industry by optimizing operations, tapping into new hydrocarbon deposits, boosting efficiency, and reducing emissions.