Production Optimiser Pilot for the Large Artificially-Lifted and Mature Samotlor Oil Field

Valery Sorokin, Alexey Semenovich Gudoshnikov, Denis Vyacheslavovich Nyunyaykin, Andrey Anatolyevich Kochenkov, P. Sethuraman, Sabina Barysheva, D. S. Lipanin, A.A. Mokrev, Sergey Aleksandrovich Vukolov, A.A. Ardalin
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引用次数: 1

Abstract

This paper describes a production optimiser Pilot, developed by Rosneft/Samotlorneftegaz, with support from bp and deployed in JSC Samotlorneftegaz - a vast, mature, water-flooded, high water-cut and artificially-lifted oil field. Objectives include creating a digital twin for a sub-system of 600 wells and ~180 km of pipeline network, applying discrete, continuous and constrained optimisation techniques to maximise production, developing sustainable deployment workflows, implementing optimiser recommendations in the field and tracking incremental value realisation. This proof-of-concept Pilot and field trial approach was adopted to understand the optimisation technology capability and work-flow sustainability, prior to a field-wide roll-out. The periodic optimisation activity workflows include the creation of a "Digital Twin", a validated surface infrastructure model that is fully calibrated to mimic field performance, followed by performing optimisation that includes all the relevant constraints. Optimisation was trialled using two different classes of algorithms – based on sequential-modular and equation-oriented techniques. This strategy minimises optimisation failure risks and highlights potential performance issues for such large-scale systems. Optimiser recommendations were consolidated, field-implemented and values tracked. The optimiser Pilot development was undertaken during the fourth quarter of 2019. The delivered minimum viable product and workflows were used for field trials during 2019-20 and continuously improved based on the learnings. Specialists from both bp and Rosneft, along with three consulting organisations (1 in Russia and 2 in the UK) collaborated and worked as one-team to deliver the Pilot. Optimiser recommendations for maximising production include continuous and discrete decisions such as ESP frequency changes, high water-cut well shut-ins and prioritised ESP lists for installing variable speed drives. Field production increase of 1% was achieved in 2020 and tracked. Enduring capabilities were built, and sustainable work-flows developed. Field-wide optimisation for Samotlorneftegaz is non-trivial due to the sheer size, with over 9,000 active wells and due to continuously transient operations arising from frequent well-work, well shut-in's, new well delivery, pipeline modifications and cyclic mode of operations in some wells. This Pilot has provided assurance for the optimisation technical feasibility and workflow sustainability. A second Pilot of similar complexity but with different pressure-flow system response is planned. The combined results will help to decide about the full-field roll-out for this vast field, which is anticipated to deliver around 1% of additional production. This Pilot has demonstrated the applicability of discrete and continuous variable constrained optimisation techniques to large-scale production networks, with very high well-count. Furthermore, the developed workflows for configuring and calibrating the digital twin have several unique components including automation of hydraulic network model generation from static data, well model build automation and fit-for-purpose automated well model calibration. Overall, the results of this approach demonstrate a viable and sustainable methodology to optimise large-scale oil production systems.
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Samotlor大型人工举升成熟油田产量优化试验
本文介绍了由Rosneft/Samotlorneftegaz在bp的支持下开发的生产优化器Pilot,并应用于JSC Samotlorneftegaz——一个巨大、成熟、水淹、高含水、人工举升的油田。目标包括为600口井的子系统和180公里的管道网络创建数字孪生,应用离散、连续和约束优化技术来最大化产量,制定可持续的部署工作流程,在现场实施优化建议,并跟踪增量价值实现。在全面推广之前,采用这种概念验证和现场试验方法来了解优化技术的能力和工作流程的可持续性。定期优化活动的工作流程包括创建“数字孪生”,这是一个经过验证的地面基础设施模型,经过完全校准以模拟现场性能,然后执行包括所有相关约束的优化。优化试验使用了两种不同类型的算法——基于顺序模块化和面向方程的技术。这种策略最大限度地降低了优化失败的风险,并突出了这种大规模系统的潜在性能问题。优化器的建议得到了整合、现场实现和值跟踪。优化器的试点开发于2019年第四季度进行。交付的最小可行产品和工作流程在2019- 2020年期间用于现场试验,并根据学习结果不断改进。来自bp和Rosneft的专家与三家咨询机构(1家在俄罗斯,2家在英国)合作,组成一个团队,交付了Pilot项目。为了实现产量最大化,优化器的建议包括连续和离散决策,如改变ESP频率、高含水关井以及优先安装变速驱动器的ESP列表。油田产量在2020年实现了1%的增长,并进行了跟踪。建立了持久的能力,并开发了可持续的工作流程。由于Samotlorneftegaz的规模庞大,有超过9000口活动井,并且由于频繁的作业、关井、新井交付、管道改造和一些井的循环作业模式,导致持续的瞬态作业,因此对Samotlorneftegaz进行全油田优化并非易事。该试点为优化技术可行性和工作流程可持续性提供了保证。计划进行第二个具有相似复杂性但具有不同压力-流量系统响应的试验。综合结果将有助于决定该大油田的全油田推广,预计该油田将提供约1%的额外产量。该试验证明了离散和连续变量约束优化技术在大规模生产网络中的适用性,具有非常高的井数。此外,开发的用于配置和校准数字孪生的工作流程具有几个独特的组件,包括从静态数据生成水力网络模型的自动化、井模型构建自动化和适合用途的自动化井模型校准。总的来说,该方法的结果证明了一种可行且可持续的方法,可以优化大规模的石油生产系统。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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