数字油田;NPDC经验

H. Ijomanta, Lukman Lawal, Onyeka Ike, Raymond Olugbade, Fanen Gbuku, Charles Akenobo
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引用次数: 0

摘要

本文介绍了oml111油田数字化油田(DOF)实时管理系统的实施概况。Oredo油田主要是一个逆行凝析油田,有几个相对较小的油藏。现场作业理念涉及最大限度地提高凝析油产量和满足每日合同天然气量的双重目标,这需要对井进行控制和布线,以满足双重目标。集成资产模型(IAM)(或集成生产系统模型)的目标是为满足油田目标提供数学基础。IAM与模型管理和版本控制工具、工作流编排和自动化引擎、强大的数据管理模块、先进的可视化和协作环境以及分析库和引擎相结合,创建了Oredo数字油田(DOF)。数字油田是油田在计算机上的实时数字表示,它复制了油田的行为。这个虚拟油田为工程师提供了所需的所有信息,使他们能够在不同能力和工程技能的多学科组织中,通过模型、工作流程和智能过滤的数据,做出快速、合理的油田管理决策。DOF的创建涉及4个主要步骤;数据收集被认为是此类工程项目中最关键的,因为它有助于设定模型可以实现的限制并降低期望。工程模型的评审、更新和对标;主要涉及工程模型的审查和更新,实时数据历史部署等。系统预配置和部署;制定了自由度系统架构和工程流程设置。部署后的审查和更新;目前,这项工作正在进行中,包括作业后的评估、DOF挑战的更新和解决、运营商的能力开发以及优化系统以提高性能。Oredo油田的DOF系统使油田管理任务的集成、自动化和简化成为可能,并大大缩短了决策周转时间。作业和现场管理决策现在可以在几分钟内做出,而不是几周或几个月。从提高作业安全性到提高作业效率和节约成本、实时生产监控、优化生产、早期问题发现、提高安全性、组织/跨学科协作、数据集中化和效率,这些收益和好处贯穿整个生产价值链。DOF系统在规划、执行和后期评估阶段都遇到了一些特殊的挑战,包括选择合适的数据收集和采集系统、部件互换性和设备与现有现场设备的集成、由于带宽、信号强度等原因导致的高数据延迟、井口传感器和变送器在作业期间(如钢丝绳和WHM活动)的损坏、电池寿命短、维护等。以及更换频率等。这些挑战影响了项目进度和成本,但也为公司提供了很好的经验教训,并改善了DOF的学习曲线。Oredo数字油田代表了石油和天然气行业的未来,与工业4.0的特性相结合,使用数字技术来提高效率,降低运营成本,并应用石油和天然气行业生存所需的最佳监控实践。5G技术的出现及其对数据传输、延迟和带宽的影响,有可能降低自动数据传输的成本,提高数据收集的性能,进一步提高DOF系统的效率。数字集成技术、计算能力、云计算和传感技术的改进将进一步加强DOF的未来。工程团队、IT和仪器工程师之间需要协同工作来全面管理系统,以避免可能由接口管理问题引起的故障。应始终监测电池寿命状态,以确保连续的实际现场数据流。为了进一步最大化DOF系统的效益,需要将传统的石油技术技能与数据分析技能结合起来,形成一套新的能力。NPDC需要培训和鼓励员工进入这些数据分析技能库,以开发所需的知识智能,从而最大限度地提高Oredo数字油田的效益,并将这些知识转移到NPDC的其他资产中。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Digital Oil Field; The NPDC Experience
This paper presents an overview of the implementation of a Digital Oilfield (DOF) system for the real-time management of the Oredo field in OML 111. The Oredo field is predominantly a retrograde condensate field with a few relatively small oil reservoirs. The field operating philosophy involves the dual objective of maximizing condensate production and meeting the daily contractual gas quantities which requires wells to be controlled and routed such that the dual objectives are met. An Integrated Asset Model (IAM) (or an Integrated Production System Model) was built with the objective of providing a mathematical basis for meeting the field's objective. The IAM, combined with a Model Management and version control tool, a workflow orchestration and automation engine, A robust data-management module, an advanced visualization and collaboration environment and an analytics library and engine created the Oredo Digital Oil Field (DOF). The Digital Oilfield is a real-time digital representation of a field on a computer which replicates the behavior of the field. This virtual field gives the engineer all the information required to make quick, sound and rational field management decisions with models, workflows, and intelligently filtered data within a multi-disciplinary organization of diverse capabilities and engineering skill sets. The creation of the DOF involved 4 major steps; DATA GATHERING considered as the most critical in such engineering projects as it helps to set the limits of what the model can achieve and cut expectations. ENGINEERING MODEL REVIEW, UPDATE AND BENCHMARKING; Majorly involved engineering models review and update, real-time data historian deployment etc. SYSTEM PRECONFIGURATION AND DEPLOYMENT; Developed the DOF system architecture and the engineering workflow setup. POST DEPLOYMENT REVIEW AND UPDATE; Currently ongoing till date, this involves after action reviews, updates and resolution of challenges of the DOF, capability development by the operator and optimizing the system for improved performance. The DOF system in the Oredo field has made it possible to integrate, automate and streamline the execution of field management tasks and has significantly reduced the decision-making turnaround time. Operational and field management decisions can now be made within minutes rather than weeks or months. The gains and benefits cuts across the entire production value chain from improved operational safety to operational efficiency and cost savings, real-time production surveillance, optimized production, early problem detection, improved Safety, Organizational/Cross-discipline collaboration, data Centralization and Efficiency. The DOF system did not come without its peculiar challenges observed both at the planning, execution and post evaluation stages which includes selection of an appropriate Data Gathering & acquisition system, Parts interchangeability and device integration with existing field devices, high data latency due to bandwidth, signal strength etc., damage of sensors and transmitters on wellheads during operations such as slickline & WHM activities, short battery life, maintenance, and replacement frequency etc. The challenges impacted on the project schedule and cost but created great lessons learnt and improved the DOF learning curve for the company. The Oredo Digital Oil Field represents a future of the oil and gas industry in tandem with the industry 4.0 attributes of using digital technology to drive efficiency, reduce operating expenses and apply surveillance best practices which is required for the survival of the Oil and Gas industry. The advent of the 5G technology with its attendant influence on data transmission, latency and bandwidth has the potential to drive down the cost of automated data transmission and improve the performance of data gathering further increasing the efficiency of the DOF system. Improvements in digital integration technologies, computing power, cloud computing and sensing technologies will further strengthen the future of the DOF. There is need for synergy between the engineering team, IT, and instrumentation engineers to fully manage the system to avoid failures that may arise from interface management issues. Battery life status should always be monitored to ensure continuous streaming of real field data. New set of competencies which revolves around a marriage of traditional Petro-technical skills with data analytic skills is required to further maximize benefit from the DOF system. NPDC needs to groom and encourage staff to venture into these data analytic skill pools to develop knowledge-intelligence required to maximize benefit for the Oredo Digital Oil Field and transfer this knowledge to other NPDC Asset.
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