A Novel Framework for Streamlining Well & Surface Integrated Network Model Management in the Oil & Gas Industry

This paper presents a novel framework for an advanced solution to centralize and automate the management and maintenance of well and surface integrated network models (W&SINM). Effective management of intricate W&SINM is crucial to ensure optimal performance, strategic planning, and data-driven decisions in the Oil and Gas industry. It also allows seamless collaboration for multi-disciplinary teams by providing holistic understanding of both surface and sub-surface networks to leverage simulation scenarios, forecasts and predictive analysis. The solution provides a centralized data repository that integrates with organizational repositories, databases, and modelling software to automate the update of intricate network models. The development methodology capitalizes on analytics of field sensors and surveillance activities, along with advanced modelling technologies to replicate the networks from reservoir to central processing facilities. Integration with existing well model repository, allows automated update of the uploaded network models with latest calibrated well data collected from field sensors. The platform also provides version control and metadata management with customized fine-tuned access control to ensure security. The development of a centralized network model management repository leads to streamlining the process of modelling update, verification and sharing/collaboration mechanism. The platform also improves integrity and reliability of the models by establishing a transparent and auditable record of all model adjustments and modifications. Additionally, it improves accessibility and collaboration across stakeholders, enhancing communication and facilitating informed decision-making, all while maintaining robust access controls to protect sensitive information. Overall, this platform enables organizations to optimize their modeling processes, reducing errors and increasing productivity, which ultimately leads to better-informed decision-making, improved operational efficiency, and enhanced overall performance.