The Future is Here. Utilizing Physics-Informed AI Models to Optimize Highly Intricate Water Injection Systems Within a Mature Oil Field

Abstract This paper discusses the development of an integrated model-based solution for a mature oilfield. The solution integrates state-of-the-art hydraulic models with cutting-edge Physics-Informed AI, to create a solution for the optimization of the water injection and disposal Network, which is impacted daily by the dynamics of fluid composition and the changing operational parameters in wells and surface flowlines across the field. The solution is capable of thoroughly assessing production and injection systems for improved forecasting and operational excellence The primary goal in developing this solution is to enhance asset management and improve decision-making by considering the interactions and synergies between injection wells, surface flowlines, and facilities constraints. The solution was developed using calibrated well and flowline models built within the automatic Flow Assurance Smart System (FASS), a digital solution that utilizes high-frequency data, machine learning insights and automatic updates of the hydraulic predictive models. This digital solution allows for the continuous evaluation of injection/disposal components including injection wells and hundreds of kilometers of surface pipelines. The implementation of this solution led to notable enhancements in the overall field injection and disposal systems, driving significant improvements in operational efficiency. The solution enables a comprehensive evaluation of various operational scenarios by integrating the number of injector wells with network model and aligning these with the broader field strategy. This integrated approach supports the identification of immediate optimization opportunities and provides insights into the long-term dynamic impact on field development. The system's advanced capabilities play a crucial role in identifying bottlenecks within the field's injection and disposal processes. By optimizing water injection rates and ensuring the efficient disposal of produced water, the system enhances the reservoir's pressure maintenance and overall productivity. This level of optimization is essential for achieving the aggressive oil production targets set for the asset. Moreover, the ability to evaluate and adjust to multiple scenarios allows for a more resilient and adaptive field management strategy, ensuring sustained performance and reliability in meeting the asset's goals

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Published
2024-11-04
DOI
https://doi.org/10.2118/222071-ms
Primary Topic
Oil and Gas Production Techniques
Type
article
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article

The Future is Here. Utilizing Physics-Informed AI Models to Optimize Highly Intricate Water Injection Systems Within a Mature Oil Field

Tiago Pinto, Gayatri Kartoatmodjo, Ricardo Vallejo, Gary J. Jacobsohn et al.
Oil and Gas Production Techniques
article

The Future is Here. Utilizing Physics-Informed AI Models to Optimize Highly Intricate Water Injection Systems Within a Mature Oil Field

Tiago Pinto, Gayatri Kartoatmodjo, Ricardo Vallejo, Gary J. Jacobsohn, J. Medina, Belén Del Manzo, Luis Marchán, M. Cattani, Fabián Florez, Willem Sepúlveda, L. Aguilar, Juan Guaman, J. Somale, Eva Gimenez, Francesca Franco, L. Johnson, R. Correa, N. Ontaneda
article en

Abstract

Abstract This paper discusses the development of an integrated model-based solution for a mature oilfield. The solution integrates state-of-the-art hydraulic models with cutting-edge Physics-Informed AI, to create a solution for the optimization of the water injection and disposal Network, which is impacted daily by the dynamics of fluid composition and the changing operational parameters in wells and surface flowlines across the field. The solution is capable of thoroughly assessing production and injection systems for improved forecasting and operational excellence The primary goal in developing this solution is to enhance asset management and improve decision-making by considering the interactions and synergies between injection wells, surface flowlines, and facilities constraints. The solution was developed using calibrated well and flowline models built within the automatic Flow Assurance Smart System (FASS), a digital solution that utilizes high-frequency data, machine learning insights and automatic updates of the hydraulic predictive models. This digital solution allows for the continuous evaluation of injection/disposal components including injection wells and hundreds of kilometers of surface pipelines. The implementation of this solution led to notable enhancements in the overall field injection and disposal systems, driving significant improvements in operational efficiency. The solution enables a comprehensive evaluation of various operational scenarios by integrating the number of injector wells with network model and aligning these with the broader field strategy. This integrated approach supports the identification of immediate optimization opportunities and provides insights into the long-term dynamic impact on field development. The system's advanced capabilities play a crucial role in identifying bottlenecks within the field's injection and disposal processes. By optimizing water injection rates and ensuring the efficient disposal of produced water, the system enhances the reservoir's pressure maintenance and overall productivity. This level of optimization is essential for achieving the aggressive oil production targets set for the asset. Moreover, the ability to evaluate and adjust to multiple scenarios allows for a more resilient and adaptive field management strategy, ensuring sustained performance and reliability in meeting the asset's goals

National Polytechnic School (EC), Universidad Internacional del Ecuador (EC), Gemini Computers (United States) (US)
Openalex Percentile: Top 74%
Oil and Gas Production Techniques
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