Powering Tomorrow: The Role of AI in Reshaping the Oil Industry

Hey there, folks!

Welcome to the first edition of AI Brew Lab, your go-to place for everything buzzing in the world of Artificial Intelligence. According to a published PwC report, the impact of artificial intelligence on the Middle East is expected to be 320 billion US dollars by 2030. According to another report by Future Insight, the share of artificial intelligence in the oil and gas sector is expected to increase to $13 billion by 2034. With this in mind, in the first series of my blog where I plan to write about the use of artificial intelligence in sectors, I decided to discuss how oil companies use artificial intelligence. Let's dive in.

AI in Oil: How Smart Tech is Tapping into New Possibilities?

Artificial intelligence is now present in every aspect of our lives and in many sectors, such as the oil industry. According to Mordor Intelligence, AI technology is rapidly transforming the oil and gas industry. The use of AI in the oil and gas sector is expected to reach $5.70 billion by 2029. According to the report, AI technologies increase operational efficiency, reduce costs and improve occupational safety. The prominent applications of AI technology are seen in areas such as machine learning, data analytics and predictive maintenance. The report also emphasizes that large companies such as ExxonMobil have gained competitive advantage by integrating AI solutions.

We can outline the areas, where artificial intelligence can be used in oil and gas companies as follows:

Demand forecasting: The first example of the adoption of artificial intelligence in the oil and gas sector is demand forecasting. Using artificial intelligence technology, past records and market patterns can be analyzed in the oil and gas sector. AI aids in adjusting production according to demand, minimizing overproduction, reducing waste, and increasing profitability. By using AI in demand forecasting, companies can also optimize logistics and supply chain planning and increase overall operational efficiency.

Safety: The oil industry encompasses many risks, such as heavy equipment, high pressure, high temperature operations and aggressive chemicals. Artificial intelligence and deep learning-based systems can identify any violations of security protocols. If we give an example of how and in what way these technologies can be effective; it examines the video streams in camera recordings with the pattern recognition method. In this way, it can determine whether employees are dressed in accordance with safety rules. Another instance is the ability to provide advance warnings about equipment status using predictive analytics, thus contributing to proactive measures for the environment and safety.

Predictive Maintenance: AI technology is effective in predictive maintenance by analyzing real-time data from sensors, machines and previous records. In this way, operating costs are reduced proactively. Downtime is minimized and equipment life is extended. When AI is used in predictive maintenance, unplanned downtime is prevented and maintenance costs are reduced by up to 40%. Digital twins, an innovative technology, can be used to support predictive maintenance. The digital copy of the resource in the field is called a digital twin.

Large companies like Shell and Equinor use digital twins of existing or planned oil and gas facilities to understand the performance and maintenance of the facilities in a risk-free environment. For oil companies, digital twins can also be effective in clean energy projects such as reducing carbon emissions. This technology has the potential to help predict wind and solar energy production as well as provide remote monitoring. Another area where digital twin technology is useful is supply chain and inventory management. Using this technology, products can be tracked in real time. This optimizes logistics costs.

Exploration: AI can be used to predict drilling locations using geological data to increase exploration and productivity in oil companies. Machine learning technology is also added to identify seismic data trends and anomalies, which can increase the accuracy of exploration. In this way, it can optimize production and improve well output and extraction. If we give an example of the use of this technology in the drilling phase; ExxonMobil uses an AI-controlled system that can determine drilling parameters. This system reduces repetitive tasks by drilling operators. In this way, it increases operational efficiency.

Data Analysis: Data analysis is important in decision-making and strategic planning in oil companies. Artificial intelligence technology produces effective solutions for processing large amounts of data, identifying trends, condensing and visualizing data, and for data scientists to make precise predictions. The contribution of artificial intelligence to oil companies in improving data processes and obtaining real-time insights is extremely large.

Artificial intelligence has the potential to create significant opportunities in ensuring efficiency, safety and sustainability in the oil industry. It is important for oil companies to create the necessary collaboration and infrastructure to benefit from AI technologies.