石油和天然气的认知分析

石油和天然气公司在从数据中学习以了解资产的不同运行状态和故障模式时遇到问题，并利用这种学习在故障发生之前提供足够的警告，以便运营商可以计划纠正措施，从而优化其运营和维护预算。

SparkPredict 已部署在上游资产（例如钻柱和电动潜水泵）以及下游资产（例如炼油厂的泵）上。对于没有标记故障的资产，SparkPredict 会自动分析事件并识别异常（未知的操作状态、故障条件等）。 SparkPredict 使用已识别的异常来识别偏差模式并在观察到明显偏离正常时发出警报。 SparkPredict 利用尖端的认知机器学习技术来额外预测资产故障。我们算法的认知或基于推理的性质意味着 SparkPredict 可以部署到任何位置的任何资产，并且洞察力将适应该特定资产的独特特征。此外，SparkPredict 与已安装的资产监控系统集成或与数据历史学家（如 OSI PI）一起使用，以利用预先存在的和/或实时流数据来改进故障预测。

• [Process Optimization - Remote Diagnostics]
  SparkPredict detects both Stuck Pipe and Wash Out conditions with high accuracy.
• [Efficiency Improvement - Operation]
  SparkPredict provides an in-context advisory system technicians can use to quickly find documents and other digital resources to address issues, automatically provide meaningful remediation steps, and seamlessly communicate and share data with team member
• [Efficiency Improvement - Maintenance]
  For Electrical Submersible Pumps (ESPs), SparkPredict predicts failures days in advance.

• The average lead times for detection of these failures is: Stuck Pipe: 1.2 hours, Wash Out: 2.3 hours.

• SparkCognition targeted two failure modes, accounting for 85% of all ESP failures, and were able to provide the following median forewarning: Electrical Short: 5.5 days, Mechanical Breakdown: 6 days.