Oxy 和 Chevron 等公司试图在数据分析和机器学习之间找到最佳结合点，并通过人工干预来优化生产并预测故障。
人工智能和机器学习 (ML) 被用作油田优化碳氢化合物产量和预测故障的工具。但该行业仍有一些问题需要解决。
行业专家在 8 月 20 日举行的专业工程师协会 (SPE) 人工举升会议和展览会上表示，工程师仍然需要通过反复试验进行调整，找到技术和人为干预之间的最佳平衡点。
先进的数据分析为设计人工举升系统提供了必要的信息。但西方石油公司的工程师考特尼·理查森在会议上表示，复杂的数据也表明该行业远未完全放手。
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Oxy and Chevron are among companies trying to find the sweet spot between data analytics and machine learning with human intervention to optimize production and predict failures before they happen.
AI and machine learning (ML) are used as a tool in the oilfield to optimize hydrocarbon output and predict failures before they happen. But the industry still has some kinks to work out.
Engineers still have to make adjustments through trial and error to find the sweet spot between technology and human intervention, industry experts said at the Society of Professional Engineers’ (SPE) Artificial Lift Conference and Exhibition on Aug 20.
Advanced data analytics provide the necessary information to design artificial lift systems. But the complex data also shows the industry is far from being completely hands-off, Courtney Richardson, an engineer at Occidental Petroleum, said at the conference.
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