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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.
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.
Effective well design constitutes a collaborative effort involving well engineers and diverse subject-matter experts, encompassing the entire well construction process. Traditionally, this process has relied heavily on manual data consolidation and iterative revisions at various project maturity stages. This paper elucidates the transformative impact of digitalization initiatives within well planning stages, achieved through a digital decision gate system. This system ultimately facilitated the automation of generating the Drilling Program and regulatory-required Notice of Operations (NOOP). The process of digitizing operations began with a thorough assessment that explored the evolving digital landscape in well planning. This assessment aimed to digitize the existing manual workflows, identify data analytics requirements, foster collaboration among previously isolated teams using different software systems, and facilitate data exchange. The ultimate effort was to construct a holistic digital project framework encompassing the scoping, planning, design, operation, and eventual close-out stages. A user-friendly cloud-based solution was implemented to facilitate this transition, granting stakeholders access to a web-based platform. This platform streamlined planning through digitized standardization and task automation in well engineering. Furthermore, comprehensive data management within well engineering and its open environment contributed to orchestrating multiple planning workflows and expediting the delivery of well programs. The Malaysia case study demonstrated the successful application of digital strategies in well design. The digital platform fostered an open environment that enhanced design optimization and encouraged the exchange of expertise. It seamlessly incorporated calculations and results from the company's standardized well engineering suite, preserving workflow continuity without interruptions. Additionally, this platform provided service partners with the capability to document their specialized knowledge and innovations within a unified framework. The platform's project management capabilities ensured transparent progress monitoring, aligning stakeholders, and maintaining accountability. Rigorous digital technical assurance validated and ensured well design data's accuracy, compliance, and completeness with the company's predefined standards and criteria. The automated process eliminated the need for manual intervention when updating the drilling program, maintaining data integrity and consistency throughout the entire drilling planning process. As a result, the platform effectively streamlined the well design for 14 wells over a year. It amplified the synergy across more than 12 multidisciplinary teams, ultimately yielding a 40% (70 days) reduction in design cycle time and a 60% (28 days) decrease in time required for manual report generation. The paper offers key insights for operators embarking on the digitalization journey in front-end well engineering. This study showcases the revolutionary impact of digital technologies, paving the way for future digital advancements. This effort represents a notable addition to the existing literature, emphasizing the significance of collaboration, automated well-planning workflows, data-driven decision-making, and technology adoption in attaining optimal well-construction performance.
Improving well cycle time efficiency involves automating the benchmarking and replication of top-performer activities across a fleet, enhancing safety, reducing learning curves, and enabling real-time well performance monitoring for consistent operational excellence beyond on-bottom activities. The process involves consuming data from any digital well program, automatically converting it into a Driller's Roadmap for the digital execution of well-defined sequence of activities to eliminate guesswork and delays, to help with knowledge sharing minimizing the learning curve. Efficiently disseminating and implementing best practices, lessons learned and operational procedures by activity backed by data driven insights. Sending timely notifications and alerts to drillers and preparing crews to transition seamlessly into the next operation. The rig execution engine includes optimizing various drilling activities through specific recipe parameters, which will enable automation within a rig operating system all while providing visibility for all stakeholders. As the industry progresses towards automating well design, a key innovation is the integration of these designs with a versatile rig execution engine, accessible at the edge and the cloud. An implementation conducted across 5 rigs working for a major operator in the Permian Basin demonstrated significant improvements in well cycle efficiency, highlighting the potential of this approach to transform well operations in the industry. The integration of a versatile rig execution engine with cloud-edge technology and enhanced crew dynamics resulted in observable well cycle time efficiencies, emphasizing the value of this innovative approach through flat time optimization. This integration between a rig plan and the rig execution engine increases procedural adherence through execution using a rig operating system that automates tasks according to the well program. Additionally, improves driller training and crew retention by providing timely, relevant information and resources, including multi-lingual support, thereby fostering greater inclusion in operations.
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