Artificial Intelligence

Leading oil and gas companies are harnessing the power of AI to future-proof operations, accelerate outcomes, and make smarter decisions at every stage of the supply chain. 

Upstream

Upstream operations can use AI to Improve oil exploration, production, and labor efficiencies while reducing operational risk. Examples include:

• Oil Exploration utilizing data analytics to identify oil reserves and drilling targets
• Drilling optimization by Improving the rate of penetration to drill faster and more effectively
• Optimizing production by streamlining oil extraction methods to maximize field asset values
• Avoiding production failures and downtime by predicting equipment maintenance needs
• Optimizing land-based rig locations to improve site development, rig operations, resource management, and labor costs
• Integrating environmental, social, and governance objectives into field operations to build models for continuously tracking and reducing carbon emissions 

Midstream

Midstream operations can use AI to ensure secure and efficient oil processing and transportation. Examples include:

• Optimizing the supply chain by managing inventory levels more efficiently to reduce overall costs and accelerate delivery
• Forecasting demand by predicting future oil production needs to optimize delivery resource planning
• Preventing security and fraud losses through proactive risk management

Downstream

Downstream operations can use AI to optimize fuel price, the customer experience, and non-fuel upsell opportunities. Examples include:

• Determining optimal fuel prices based on seasonal, environmental, and event-based trends
• Identifying ideal retail sites based on regional demographics to yield more revenue 
• Developing data-driven trading strategies that increase top-line growth and optimize pump price
• Increase customer lifetime value with personalized recommendations of non-fuel products like drinks and snacks

Leaders in the construction and engineering industries are harnessing the power of AI to reduce building costs, reduce expensive errors, reduce worksite injuries, and make building operations more efficient. The potential applications for AI in construction and engineering are vast. Some examples include:

• Planning the routing of electrical and plumbing systems in modern buildings
• The development of safety systems for worksites
• Tracking the real-time interactions of workers, machinery, and objects on construction sites
• Sending alerts for potential safety issues, construction errors, and productivity issues

Traditional document management using manual workflows can result in lost productivity, lack of organization, and poor customer service. Companies can integrate AI into their document management system to speed up the process. Some areas where AI is changing the businesses manage documents include:

• Automating classification and processing
• Data extraction
• Clustering documents
• Bringing order to unstructured data
• Supporting content and document development
• Securing documents and data 

Managing the complexities and data of payroll management can be a challenge for most businesses. Artificial intelligence can efficiently handle payroll data and remove the chances of human error. AI can optimize mundane tasks like calculating salary and recording work hours. Some of the ways payroll management can become better with AI include:

• Faster and accurate classification of employees for payroll calculation and tax band assignment
• Improved interactivity, connectivity, and query resolution through AI-powered chatbots
• Comprehensive compliance with the dynamic regulatory landscape 
• Quicker and precise analysis of data for devising innovative payroll strategies
• Better decision-making due to data-based insights and forecasts
• Faster induction/relieving of employees into payroll system on entry/exit