The West Qurna-2 field is one of the biggest undeveloped oil fields in Iraq. The development of the field is handled by the Iraqi Basra Oil Company and a consortium between LUKOIL and the National Iraqi North Oil Company. The production of the field started in 2014. This paper describes how the integrated asset model (IAM) and automated workflows have been used to support the field's development from first oil to full field development. Along the field's development stages, the IAM system has assisted LUKOIL in its operations. Its availability for first oil allowed refining the assumptions made about the productivity of the wells and reservoir characteristics for issuing realistic production profiles, hence playing a crucial role in achieving the production's targets. Subsequently, the wells have been gradually converted to artificial lift (electrical submersible pumps) and the IAM system has allowed for extension of the natural flowing mode of the wells, optimization of workover operations, short term optimization of the ESP operations and extension of their lifetime. Today, the IAM system allows for mitigating the water handling constraints through tailor-made field-wide optimization algorithms. The latter optimizes not only the field operations through integrated asset planning but also the water production and injection system continuously. Overall, the IAM system has proven to be instrumental for the development and optimisation of West Qurna II. It has allowed to de-risk the initial production, optimize the workovers in the field, enhance the ESP performance and lifetime, optimize the pressure maintenance systems, etc. It has also provided a platform to empower multi-disciplinary teams. The saving figures presented in this paper alone amount to around 7 million dollars in 2015-2016 and it is not unreasonable to ultimately expect total savings of tens of million dollars. Such savings make the implementation of IAM systems very attractive for similar green field operations. LUKOIL has made critical choices for ensuring the success of the initiative, such as allowing for customization and flexibility of its IAM system, putting the necessary efforts in developing and maintaining accurate hydraulic models and starting simple and keeping focusing on the continuous and organic development and improvement of its IAM system. This paper demonstrates how IAM systems can be practically used for optimization activities in a green field, and the associated direct benefits. It also details how to maintain such systems continuously up to date for a field in development phase, where the layout is continuously changing. A series of best practices are identified for ensuring successful embedment of these advanced approaches within the day-to-day operations.

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Objectives/Scope The Rumaila Operating Organisation (ROO) is a consortium made up of BP, China National Petroleum Corporation (CNPC) and the Basra Oil Company (BOC) formed to manage the rehabilitation and expansion of the Rumaila super giant oil field, considered the third largest in the world. The Digital Oilfield (DOF) plays an important role in the rehabilitation process. This paper describes the major challenges, solutions and benefits over 5 years of implementation. Methods, Procedures, Process The development of the DOF solution involved several components: the installation and connection of sensors; a data management platform for both real-time and non-real time data; the development of engineering models and workflows; an Exception-Based Surveillance engine (EBS) and a user interface integrating all this data. This paper details how an EBS process is handled for a field of this magnitude, the use of state-of-the-art algorithms for identification of well flow conditions, deployment of advanced analytics for surveillance and optimization of natural flow and ESP wells. This paper also details how usability testing and advanced graphic design practices were used to guarantee maximum adoption of the new toolkit. Results, Observations, Conclusions The Rumaila case is ideal for evaluating the added value of digitalization of oilfields since the project developed from a zero base to a fully digital system in a matter of a few years. The main success stories include: the extension of the lifetime of the ESPs; reduction in well downtime; significant time savings for repetitive tasks; improved reservoir management accuracy; the ability to more readily meet production targets; facility management and optimization; and improvement in oil quality. The data and visualization usage figures which are continuously monitored show a year-on-year growing user base. This demonstrated that maintaining focus on continuous development and evolution, and providing top class support locally and from specialist vendors, increases user adoption. As a result, the program has gained support at all levels in the organization, making DOF an integral part of the field operation providing high efficiency and standards of excellence. Novel/Additive Information Due to the initial lack of any digitalization in the field, this project can be considered a blueprint for modernization of fields in challenging environments, where very little digital infrastructure is initially available. This project has proven that DOF implementation can be very successful despite many localized challenges, and that a continuous focus on system evolution guarantees a growing user base year after year.

The digital oilfield technology is typically associated with high level of field automation and instrumentation, as well as advanced petroleum engineering modelling. This paper discusses the application of digital oilfield to large brown fields based on real, but anonymous cases, where the level of instrumentation is low, production models might not be available, and the local expertise might be limited. In such situation, the principles of digital oilfield need to be adapted. This paper presents a staged implementation methodology, where the benefits and costs can be evaluated at every step of the project, allowing to build a system with the right amount of functionality and complexity. The first step focuses on improving data quality, even if the data is captured manually, through automated quality checks and raising awareness during the data capture process. The second step focuses on automating routine tasks, such as reporting, leading to efficiency improvement, but also increased accuracy and traceability of the reported figures. The third step focuses on developing a production monitoring platform, allowing to perform exception-based surveillance, particularly important for large fields, as well as providing a single point of access for different disciplines, hence acting as a collaborative environment. At last, the model-based more complex workflows are discussed, such as virtual metering, production optimization and short-term production forecasting. The main conclusion of this paper is that the Digital Oilfield can deliver value for brown fields, even if they are close to their life end. The relatively low cost of these solutions, and the immediate benefits they can provide makes it meaningful, even in a short-term perspective. A staged implementation lowers both the project risks and the required initial investment, while easing the adoption process by the users. The main differences with application to green fields is an increased focus on data quality improvement, and a lower focus on models and complex engineering workflows. The surveillance platform should also focus more intensively on exception based surveillance, allowing to pre-process large amounts of data, rather than providing extremely fine detail.

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