An Innovative and Practical Integrated Asset Model: Optimisation from Pore to Process with Automatic Model Update
Authors: Biniwale Shripad , Ali Samad , Khan Osama Hasan , Zhou Wentao , Efimova Ksenia , Takahashi Masahiro 
Published: March 2021

Abstract
One of the major challenges the oil and gas industry faces is the enablement of fast and seamless multi-disciplinary integration across the reservoir, production, network and facilities. Integrated Asset Management (IAM) is a key concept for making critical decisions about assets development and to maximise asset value. Although the IAM concept has been used in the past, it's tough to implement due to its inherent complexities. This paper introduces the latest technical innovations and processes that make IAM approach practical and reliable for its implementation. This innovative solution offers flexibility to rapidly adjust the model through "automatic and fast model updates" and provides a fit-for-purpose integrated model. The solution improves the speed and accuracy of decisions, and assists in field development planning workflows, modelling operational challenges and addressing debottlenecking options while considering all the domain constraints and development scenarios.
Integrated asset modelling methodology used in this paper, is flexible in capturing domain science at different fidelity levels, incorporating fit for purpose physics, ranging from analytical models to highly complex reservoir simulation models with high-resolution grids that capture geological complexity. Operational logic design and decision cycle implementation for production forecasting are leveraged from an implicitly coupled scheduler, referred to as "Field management". Surface network integration in the integrated asset model is flexible and dependent on the level of fidelity needed – with complete control of all entities inside the surface network model provided by the "Field management" system. Optimisation capabilities provided with the "Field management" system allows for "automatic updates" to entities in the production system to optimise the recovery. "Model updates" pertinent to production data updates is driven by the "rapid model update" technique.
Flexible coupling techniques and niche optimisation capabilities offered by the "Field management" scheduler between the different domain models enable optimising the asset's production with implicit operational constraints. The superior performance offered through state-of-the-art solvers, fit for purpose fidelity and parallel scalability offers a practical advantage to this integrated asset management approach. "Rapid model update" workflow allows for seamless and fast integration of production data updates within the integrated asset model, thereby keeping the model in an "evergreen" state, reflective of the subsurface dynamics and operational changes.
This paper provides the most practical solution for Integrated asset modelling implementation that provides flexibility to balance between performance and fidelity by leveraging the latest technological advancements and workflows. It is the first solution that offers an optimisation technique capable of "rapid and automatic model updates" and python extensibility to achieve realistic field planning forecasts.