Artificial Intelligence
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our solutions
Artificial Intelligence (AI) can be used in various aspects of the oil, gas, chemicals, and power industries. Here are some specific applications
oil & gas
1. Emissions Reduction: AI can help the oil and gas industry reduce greenhouse gas emissions. Digital tools using advanced analytics, AI, and machine learning can help companies identify and measure their emissions as accurately as possible, determine the optimal means for abating them, execute the abatement, and then provide a full and accurate accounting of their decarbonization efforts.
2. Exploration and Drilling: AI algorithms can provide accurate and precise intelligence to guide drills on water and land3. This can help companies involved in the oil and gas industry to drill and mine raw hydrocarbons and other products required to produce fuel.
3. Operational Efficiency: AI can help oil and gas companies increase operational efficiency. Companies can decrease scope 1 and scope 2 emissions through operational and energy efficiency initiatives.
4. Monitoring and Protection: With billions of OT (Operational Technology) and IT data points captured from physical assets each day, oil and gas companies are now turning to built-for-purpose AI tools to provide visibility and monitoring across their industrial operating environments. This not only makes technologies and operations more efficient but also provides protection against cyberattacks.
5. Upstream, Midstream, Downstream Operations: AI has applications in optimizing upstream operations (including exploration, drilling, reservoir, and production), midstream operations (including transportation using pipelines, ships, and road vehicles), and downstream operations (including production of refinery products like fuels, lubricants, and plastics).
chemicals
1. Predictive Analytics: AI can be used to predict future trends and behaviors, allowing chemical companies to make proactive, knowledge-driven decisions.
2. Quality Control and Assurance: AI can improve the quality of products by identifying and correcting errors in real-time.
3. Automation of Production-Related Processes: AI can automate various production-related processes, increasing efficiency and reducing costs.
4. Customer Interaction and Operations: AI has the potential to disrupt every aspect of a chemical company’s business model, from customer interaction and operations, to sales and R&D.
5. Product and Service Offerings: AI can open the door to new product and service offerings.
6. Improve Targeting: AI-based customer-specific forecasting and demand sensing support highly targeted, proactive sales.
7. Boost Sales: Data-driven R&D reduces price leakage through superior product quality and faster innovation cycles.
8. Deepen Insights: A much richer understanding of customers helps identify new applications/markets for products.
9. Raise Throughput: Advanced analytics offer greater right-first-time production, optimized change-overs and smart maintenance to reduce unplanned downtime.
10. Lower Churn: 24/7 AI-powered customer service reduces churn rates, with higher levels of data-driven service quality.
11. Resolve Customer Problems: Virtual technical sales agents support customers, resolving queries, answering questions and informing them of new offerings.
power
1. Asset Optimization: AI can enhance many activities, from asset optimization to customer segmentation2. AI provides an opportunity to significantly increase performance through the use of data collected from IoT devices.
2. Predictive Maintenance: Power companies are using AI-based solutions to predict maintenance operations.
3. Pricing Optimization: Distribution companies are using machine learning-driven trading platforms to optimize pricing.
4. Grid Resilience: Suppliers are promoting context-aware computing systems to lower prices and ensure grid resilience.
5. Energy Transition Acceleration: AI can speed up the energy transition, saving time and money3. Most of the energy sector’s transition efforts have focused on hardware: new low-carbon infrastructure that will replace legacy carbon-intensive systems. However, next-generation digital technologies, in particular AI, can be adopted more quickly at larger scales than new hardware solutions, and can become an essential enabler for the energy transition.
6. Monitoring and Protection: AI is optimizing and securing energy assets and IT networks for increased monitoring and visibility. This not only makes technologies and operations more efficient but also provides protection against cyberattacks.Leading adopters of artificial intelligence in the power sector include Duke Energy, E.ON, Enel, Électricité de France (EDF), Iberdrola, Exelon, Schneider Electric, Dubai Energy & Water Authority (DEWA), National Grid, and Southern Company.
benefits
AI has the potential to transform these industries by improving efficiency, enhancing safety, optimizing operations, accelerating energy transition, and much more.
oil & gas
1. Understanding Reservoirs: AI can help operators acquire a better understanding of their reservoirs and therefore make better exploration and production decisions.
2. Drilling Optimization: AI can assist in oil well designing and drilling, optimizing well placement and spacing to increase production.
3. Risk Measurement: Subsurface risks can be measured through AI.
4. Production Forecasts: AI can provide optimal flow rates, pressure, and other variables for maximum lifetime well production.
5. Maintenance Planning: AI can play a role in the planning of maintenance shutdowns at refineries.
6. Safety Measures: AI can be a great asset in anticipating health and safety risk throughout the value chain.
chemicals
1. Process Optimization: AI can optimize chemical processes by analyzing historical and real-time data.
2. Supply Chain Management: AI can optimize the supply chain and use resources to increase returns.
power
1. Asset Optimization: AI enhances many activities, from asset optimization to customer segmentation3.
2. Predictive Maintenance: Power companies are using AI-based solutions to predict maintenance operations.
3. Pricing Optimization: Distribution companies are using machine learning-driven trading platforms to optimize pricing.
4. Grid Resilience: Suppliers are promoting context-aware computing systems to lower prices and ensure grid resilience.
5. Energy Transition Acceleration: AI can speed up the energy transition, saving time and money.