The study presents a conceptual model that uses generative AI to automate project scheduling for complex oil and gas capital projects. The model uses historical project schedules and expert-built process maps to generate a full-scale schedule including dependencies, resources, and duration. The study highlights the limitations of traditional scheduling methods based solely on planner ability and discusses the potential benefits of using AI, including improved accuracy and efficiency.
The conceptual model aims to address project schedule issues and the model begins by collecting data from past projects to create a historical database, which is used to train a generative AI algorithm to perfect the process maps. Process maps serve as a visual representation of the project schedule, detailing the steps and dependencies involved in a project, and are used to find potential issues or bottlenecks in the schedule and recommend solutions based on historical data. Text summarization and cataloging techniques are used to extract key information and categorize them based on project type, driver, size, stage, etc.
After examining the available literature and conducting a market analysis, it was found that the potential solutions for oil and gas scheduling requirements were not enough. The quality of the project schedule can be affected by several factors, leading to associated integrity issues, project delays, cost overruns, and other negative consequences. Addressing these challenges upfront requires a robust and reliable method that incorporates historical data, process maps, and AI-driven analysis to create accurate and transparent project schedules. Observations revealed that the model's ability to learn from historical project schedules and expert knowledge was crucial to its success. The use of expert-built process maps supplied a comprehensive and accurate framework for generating project schedules, improving the accuracy and efficiency of the generative AI algorithm. The proposed model offers a streamlined approach to project scheduling that can help reduce the potential for human error and improve project outcomes. The use of generative AI for project scheduling has the potential to revolutionize the oil and gas industry by supplying a more efficient and accurate method for managing complex projects. Further research and development of this approach can lead to continued improvements in accuracy and efficiency, ultimately leading to better project outcomes.