数字解决方案套件:大数据、人工智能和数字桶

Roberto Fuenmayor
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引用次数: 0

摘要

数字化转型的概念基于两个原则:数据驱动(利用数据源的每一点)和以用户为中心。目标不仅是整合来自多个系统的数据,而且是应用分析方法提取来自多个来源聚合的见解,然后将其呈现给用户(现场经理,生产和监控工程师,区域经理和国家),标准是简单,具体,新颖,最重要的是,清晰。其理念是通过提供一站式生产数字平台,释放整个上游社区的数据,为生产操作人员提供数据,该平台可以挖掘非结构化数据,并将其转换为结构化数据,作为工程模型的输入,从而提供数据分析并生成见解。主要有三个关键目标:使用基于云的技术只有一个真实来源;结合人工智能模型来填补生产和操作参数(如压力和温度)的数据空白;为上游社区整合多种解决方案,以帮助上游运营的慢、中、快速循环。新的“工作方式”可以帮助多个学科,如地下团队、设施和运营、HSSE和业务规划,将业务流程管理和技术工作流程相结合,以产生影响运营商损益表的见解和创造价值。“新的工作方式”解决了诸如生产优化、减少计划外延迟、避免成本和提高工艺周期效率等价值支柱。大数据和人工智能算法的使用是了解油井和油田生产情况的关键,也是自动化工程模型处理数据的关键,从而能够更好地做出决策,包括时间尺度的跨度,如快速、中速或慢速循环操作。
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Digital Solutions Suite: Big Data, Artificial Intelligence, and Digital Barrel
The concept of digital transformation is based on two principles: data driven—exploiting every bit of data source—and user focused. The objective is not only to consolidate data from multiple systems, but to apply an analytics approach to extract insights that are the product of the aggregation of multiple sources then present it to the user (field manager, production and surveillance engineer, region manager, and country) with criteria's of simplicity, specificity, novelty—and most importantly, clarity. The idea is to liberate the data across the whole upstream community and intended for production operations people by providing a one-stop production digital platform that taps into unstructured data and is transformed into structured to be used as input to engineering models and as a result provide data analytics and generate insights. There is three main key objectives: To have only one source of truth using cloud-based technology To incorporate artificial intelligence models to fill the data gaps of production and operations parameters such as pressure and temperature To incorporate multiple solutions for the upstream community that helps during the slow, medium, and fast loops of upstream operations. The new "way of working" helps multiple disciplines such as subsurface team, facilities, and operations, HSSE and business planning, combining business process management and technical workflows to generates insights and create value that impact the profit and losses (P&L) sheet of the operators. The "new ways of working" tackle values pillars such as production optimization, reduced unplanned deferment, cost avoidance, and improved process cycle efficiency. The use of big data and artificial intelligence algorithms are key to understand the production of the wells and fields, as well as anchoring on processing the data with automated engineering models, thus enabling better decision making including the span of time scale such as fast, medium, or slow loop actions.
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