Integrated Mature Field Management System from Rapid Production Update to History Matching

Abstract Effective asset-level decision-making relies on a sound understanding of the complex sub-components of the hydrocarbon production system, their interactions, along with an overarching evaluation of the asset's economic performance under different operational strategies. This is especially true for the LNG upstream production system, from the reservoir to the LNG export facility, due to the complex constraints imposed by the gas processing and liquefaction plant. The evolution of the production characteristics over the asset lifetime poses a challenge to the continued and efficient operation of the LNG facility. To ensure a competitive landed LNG cost for the customer, the economics of the production system must be optimized, particularly the liquefaction costs which form the bulk of the operating expenditure of the LNG supply chain. Forecasting and optimizing the production of natural gas liquids helps improve the asset economics. The risks due to demand uncertainty must also be assessed when comparing development alternatives. This paper describes the application of a comprehensive field management framework that can create an integrated virtual asset by coupling reservoir, wells, network, facilities, and economics models and provides an advisory system for efficient asset management. In continuation of previously published work (Khan, Ali, Elfeel, Biniwale, & Dandekar, 2020), this paper focuses on the integration of a steady-state process simulation model that provides high-fidelity thermo-physical property prediction to represent the gas treatment and LNG plant operation. This is accomplished through the Python-enabled extensibility and generic capability of the field management system. This is demonstrated on a complex LNG asset that is fed by sour gas of varying compositions from multiple reservoirs. An asset wide economics model is also incorporated in the integrated model to assess the economic performance and viability of competing strategies. The impact of changes to the wells and production network system on LNG plant operation is analyzed along with the long-term evolution of the inlet stream specifications. The end-to-end integration enables component tracking throughout the flowing system over time which is useful for contractual and environmental compliance. Integrated economics captures costs at all levels and enables the comparison of development alternatives. Flexible integration of the dedicated domain models reveals interactions that can be otherwise overlooked. The ability of the integrated field management system to allow the modeling of the sub-systems at the ‘right’ level of fidelity makes the solution versatile and adaptable. In addition, the integration of economics enables the maximization of total asset value by improving decision making.

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An Innovative and Practical Integrated Asset Model: Optimisation from Pore to Process with Automatic Model Update

10.2523/iptc-21295-ms ◽

2021 ◽

Author(s):

Shripad Biniwale ◽

Samad Ali ◽

Osama Hasan Khan ◽

Wentao Zhou ◽

Ksenia Efimova ◽

...

Keyword(s):

Asset Management ◽

Management System ◽

Superior Performance ◽

Production Data ◽

Complete Control ◽

Practical Advantage ◽

Model Update ◽

Field Management ◽

Surface Network ◽

Fit For Purpose

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.

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Integrated Assisted History Matching and Forecast Optimisation Under Uncertainty for More Robust Mature Field Redevelopment Project

10.2118/205204-ms ◽

2021 ◽

Author(s):

Simon Berry ◽

Zahid Khan ◽

Diego Corbo ◽

Tom Marsh ◽

Alexandra Kidd ◽

...

Keyword(s):

History Matching ◽

Traditional Approach ◽

Large Data ◽

Integrated Approach ◽

Data Sets ◽

Efficient Manner ◽

Fully Integrated ◽

Well Location ◽

Assisted History Matching ◽

Mature Field

Abstract Redevelopment of a mature field enables reassessment of the current field understanding to maximise its economic return. However, the redevelopment process is associated with several challenges: 1) analysis of large data sets is a time-consuming process, 2) extrapolation of the existing data on new areas is associated with significant uncertainties, 3) screening multiple potential scenarios can be tedious. Traditional workflows have not combatted these challenges in an efficient manner. In this work, we suggest an integrated approach to combine static and dynamic uncertainties to streamline evaluating of multiple possible scenarios is adopted, while quantifying the associated uncertainties to improve reservoir history matching and forecasting. The creation of a fully integrated automated workflow which includes geological and fluid models is used to perform Assisted History Matching (AHM) that allows the screening of different parameter combinations whilst also calibrating to the historical data. An ensemble of history matched models is then selected using dimensionality reduction and clustering techniques. The selected ensemble is used for reservoir predictions and represents a spread of possible solutions accounting for uncertainty. Finally, well location optimisation under uncertainty is performed to find the optimal well location for multiple equiprobable scenarios simultaneously. The suggested workflow was applied to the Northern Area Claymore (NAC) field. NAC is a structurally complex, Lower Cretaceous stacked turbidite, composed of three reservoirs, which have produced ~170 MMbbls of oil since 1978 from an estimated STOIIP of ~500 MMstb. The integrated workflow helps to streamline the redevelopment project by allowing geoscientists and engineers to work together, account for multiple scenarios and quantify the associated uncertainties. Working with static and dynamic variables simultaneously helps to get a better insight into how different properties and property combinations can help to achieve a history match. Using powerful hardware, cloud-computing and fully parallel software allow to evaluate a range of possible solutions and work with an ensemble of equally probable matched models. As an ultimate outcome of the redevelopment project, several prediction profiles have been produced in a time-efficient manner, aiming to improve field recovery and accounting for the associated uncertainty. The current project shows the value of the integrated approach applied to a real case to overcome the shortcomings of the traditional approach. The collaboration of experts with different backgrounds in a common project permits the assessment of multiple hypotheses in an efficient manner and helps to get a deeper understanding of the reservoir. Finally, the project provides evidence that working with an ensemble of models allows to evaluate a range of possible solutions and account for potential risks, providing more robust predictions for future field redevelopment.

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