First Complete Digital Drilling Package Deployment for Risks Reduction and Performance Optimization: Africa Offshore Case History

P. Ferrara, Luigi Mutidieri, Gianluca Magni, D. Farina, Luca Dal Forno, Giorgio Ricci Maccarini, Francesco Battaglia, G. Ricci
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Abstract

In an era of reduced profit margin and high market uncertainty, more than ever it is important to meet operational excellence as a key factor for business sustainability. This is common to most technical applications, but it is particularly true for the drilling operations, where considerable investments and associated risks are involved. During last four years, as part of its digital transformation process, Eni has equipped itself with several digital tools for the diagnosis and the monitoring of drilling and completion operations. Goals and reached benefits can be summarized in risk reduction, operational efficiency and performance optimization. Based on a wide case history started in 2019, a Digital Drilling Package was developed for operations support, from the design to the construction phase. Three main tools are now available to be applied to the most complex wells, either stand-alone or in parallel, covering drilling operations non-productive time (NPT) prediction, performance advanced analytics and real time simulations. This last simulation tool was deployed for the first time in late 2020 on some wells and is now being included in the engineering and operation workflows. Attacking operational NPT and invisible lost time with the aim to increase safety and to reach the technical limit is not only a matter of processing tools. It requires a deep integration with headquarter (HQ), geographical units and field locations, with the definition of a strong data management infrastructure. This paper describes Eni's experience both on-site and in office, showing how the portability and integration of big data systems, suitable data lake architectures and human factor synergies can create effectiveness at all levels. An Africa Offshore field case history is reported to show how predictive and data analytics modelling and tools interact. In addition, the way in which these tools have been managed to support optimum decision-making processes is highlighted. Next development steps will target an even higher level of integration of all available digital tools to have a single diagnostic approach based on univocal dashboards and in-house data server infrastructures.
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第一个完整的数字钻井包部署,以降低风险和优化性能:非洲海上案例历史
在利润率下降和市场高度不确定性的时代,将卓越运营作为企业可持续发展的关键因素比以往任何时候都更重要。这在大多数技术应用中都很常见,但在钻井作业中尤其如此,因为钻井作业涉及大量投资和相关风险。在过去的四年中,作为其数字化转型过程的一部分,埃尼公司为自己配备了几种数字化工具,用于钻井和完井作业的诊断和监控。目标和达到的效益可以概括为降低风险、提高运营效率和优化性能。基于2019年开始的广泛案例历史,从设计到施工阶段,开发了一个用于操作支持的数字钻井包。目前有三种主要工具可用于最复杂的井,包括钻井作业非生产时间(NPT)预测、性能高级分析和实时模拟。最后一个模拟工具于2020年底首次在一些井中部署,现在已被纳入工程和操作工作流程。为了提高安全性和达到技术极限,打击作业NPT和无形的时间损失不仅仅是加工工具的问题。它需要与总部(HQ),地理单位和现场位置进行深度集成,并定义强大的数据管理基础设施。本文描述了埃尼在现场和办公室的经验,展示了大数据系统的可移植性和集成,合适的数据湖架构和人为因素协同作用如何在各个层面创造效率。报告了非洲海上油田的案例历史,以展示预测和数据分析建模与工具如何相互作用。此外,还强调了管理这些工具以支持最佳决策过程的方式。下一步的开发目标是将所有可用的数字工具集成到更高的水平,以实现基于单一仪表板和内部数据服务器基础设施的单一诊断方法。
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