大型石油生产商利用先进的分析平台
技术
- 分析与建模 - 机器学习
- 分析与建模 - 预测分析
- 基础设施即服务 (IaaS) - 其他
适用行业
- 石油和天然气
适用功能
- 流程制造
用例
- 预测性维护
- 根因分析与诊断
客户
未公开
关于客户
美国最大的石油和天然气生产商之一拥有上游投资组合，由分布在北美、南美和中东 10 个国家/地区的 22,000 多口油井组成。
挑战
客户投资组合中大约有 17,000 口井采用了梁泵人工举升技术。虽然与其他人工举升技术相比，游梁泵技术相对便宜，但游梁泵经常出现故障，故障率从每年 66% 到 95% 不等。意外故障会导致数周的生产损失、紧急维护费用和昂贵的设备更换。
解决方案
作为 C3 IoT 分析软件套件的一部分，C3 Predictive Maintenance 采用基于机器学习的算法来增强故障预测和诊断能力。该应用程序通过持续监控所有仪器信号、跟踪复杂的故障模式以及检测与大量资产即将发生的设备故障相关的操作异常来增强传统系统。在此部署中，C3 IoT 集成了来自现场设备的日常传感器读数和来自维护工作订单的非结构化数据。这种全面的数据集成和分析使服务团队能够提前数周全面了解新出现的设备维护需求，并提供详细的支持数据和诊断工具来支持维护决策。硬件组件 - 日常传感器
收集的数据
Asset Status Tracking, Fault Detection, Operation Performance, Overall Equipment Effectiveness, Per-Unit Maintenance Costs
运营影响
数量效益
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