In this article we delved into application of AI in Oil and Gas and how it can help the petroleum sector to promote safety culture, reduce repair and maintenance costs, increase production, enhance asset performance through various uses in exploration, transportation, and refining activities.
Artificial intelligence (AI) is transforming the oil and gas industry by enhancing efficiency, reducing costs, and improving decision-making.
The ways AI in oil and gas transforms the industry across all stages
Here are some of the key applications of AI in the oil and gas industry Upstream AI applications in oil and gas: exploration and production:
Upstream Applications:
Predicting reservoir’s exact location, quality, and size
- Seismic images interpretation: Geoscientists can remove noise, improve resolution, detect subtle features, or even generate additional data samples with AI if imagery quality is poor or incomplete.
- Understanding reservoirs better: Generative AI is used to analyze geological maps, production data, and well logs to create geo-models of hydrocarbon reservoirs. Such models help engineers control fluid movement and predict the long-term performance of a well.
- Informed oil well placement: A powerful duo — IoT devices and edge computing — enables the local processing of real-time sensor data, including pressure, temperature, and flow rates, to be used for geological models without relying on external computing systems. As stated by Deloitte, the time required to create geo-models for oil well placement can be reduced from months to hours, thanks to Edge AI.
Increasing extraction rates with automated drilling
- Drilling optimization: With hefty costs involved, no wonder oil and gas companies seek to hammer drilling operations home on the first try. A helping hand here is drilling optimization; another application of AI in the oil and gas industry.
- Machine learning: Predictive intelligence allows engineers to convert cross-sourced historical and real-time data into actionable insights for drilling preparation. Armed with advanced ML algorithms, geo steering teams analyze terabytes of historical data to configure optimal parameters, such as weight on bit, drilling speed, angle, etc. Meanwhile, neural networks leverage real-time drilling data to predict the likelihood of stuck pipe events, enabling proactive measures. Machine learning for oil and gas reduces the risk of drill-bit failures and optimizes extraction rates.
Proactively identifying and preventing equipment failures
Oil and gas producers put significant effort into overseeing E&P equipment and scheduling maintenance activities. While manual monitoring is error-prone, time-consuming, and lacks the ability to leverage historical data, automated fault detection helps identify patterns, predict potential failures, and optimize operations.
Drones and robots with tiny sensors and cameras scan each equipment component with laser precision. AI performs 24/7 meticulous real-time inspection without human intervention, minimizing operational risks and expenses.
Midstream Application:
Midstream use cases of AI in the oil and gas industry, storage and transportation
Inspecting storage facilities
Optical gas imaging (OGI) cameras installed on robots or unmanned drones capture large amounts of data. Generative AI sums it all up in natural language, saving hours that used to be spent on the manual review of the footage.
Planning the safest and fastest routes for logistics vessels
Logistics specialists analyze vast amounts of data related to weather, route hazards, port congestions, vessel conditions, and other operational factors. Advanced analytics help them reveal insights to plan the most cost-effective tank routes.
Not only do AI optimization algorithms ensure on-time delivery, but they also identify risks and adjust the route on the go without increasing the planned transit time.
- Pipeline Monitoring: AI-powered sensors detect anomalies and predict potential failures.
- Predictive Maintenance: Machine learning algorithms optimize maintenance schedules, reducing downtime.
- Supply Chain Optimization: AI-driven models optimize logistics and transportation.
- Inventory Management: AI-powered systems optimize inventory levels and reduce waste.
Downstream Applications
By embracing AI, downstream firms achieve refinery & distribution cost reduction and reach regulatory compliance faster in several ways.
Honing the refinery process
The application of AI in downstream oil and gas companies involves real-time monitoring systems that optimize refineries. Those systems monitor operations and collect data during distillation, catalytic cracking, and hydrogenation. They also screen data from energy meters and equipment sensors.
All this contributes to boosting petrochemical throughput, minimizing energy consumption, and identifying potential safety hazards.
Meeting quality standards faster
AI aids oil and gas refinery companies to meet key quality standards, including ISO, API, ASTM, and others.
Straight from the production lines, ML algorithms and predictive AI models analyze the produced diesel, lubricants, jet fuel, natural gas, liquefied petroleum gas, oil petrochemicals, etc., against the standards.
By predicting deviations in product quality before they occur, production specialists can make corrections to minimize waste and ensure production reliability and environmental sustainability.
Reinforcing product research and development
Generative AI in the oil and gas industry allows petrochemical engineers to accelerate the materials development process and bring down R&D costs by:
designing new chemical compounds with diverse compositions, simulating how these virtual assets behave under different conditions before actual physical production;
establishing the most efficient experimental procedures for probing or optimizing materials;
developing high-entropy alloys (HEAs) with excellent physical, chemical, and mechanical properties.
- Refinery Optimization: AI-driven models optimize refining processes, improving efficiency and yield.
- Predictive Maintenance: Machine learning algorithms optimize maintenance schedules, reducing downtime.
- Supply Chain Optimization: AI-driven models optimize logistics and transportation.
- Customer Service: AI-powered chatbots and virtual assistants enhance customer experience.