Seeq’s Focus on Time-Series Data Draws in Chevron, Shell, and Pioneer

Abstract

Time-series data historians have been a staple of industrial computing for almost 30 years. Yet today, this flavor of database technology has become more important than ever and is considered to be a linchpin of the Industrial Internet-of-Things (IIOT). The predictive analytics market has seized upon this trend with the idea that intelligent computing programs can drive cash-generating insights out of these databases with little or no human supervision. Seeq Corporation is taking a different approach to this concept by enabling humans to do what they already know how to do—only much faster. So far, more than 100 companies have bought into the 5-year-old firm’s software technology, including a growing number of the oil and gas industry’s most familiar names. “Our focus is rooted in a really practical, natural intuition about how to work with data,” said Brian Parsonnet, the chief technology officer at Seeq. “We look not just at the analytics, but at what we would say is the ‘full arc’ of everything that goes into it.” The oil field’s interest in the company was highlighted in July when it raised $23 million from a group of investors that included the venture arms of Siemens and Chevron. The funding round also saw participation from Denver-based Altira Group, a venture capital fund that is closely aligned with several of the largest US shale producers: Apache, Devon Energy, and EQT. Pioneer Natural Resources, which also has strong ties to Altira, and international major Shell are two of the most notable producers who have announced that they are licensing the company’s software. The Full Arc The universal problem that the Seattle, Washington-based company says it has cured deals with the data that go into historians, which in the oil and gas context would commonly be temperature or pressure readings taken on a constant basis. The time-series (i.e., time-stamped) data are the vital signs of modern oilfield equipment, wellheads, and production facilities. Amid the widening embrace of the IIOT, these historian databases have become the most rapidly expanding piece of the storage market, according to at least one analysis. One thing to know about all this machine data is that they pile up quickly in the historians. That means for even the most routine root-cause analysis, it can it take a person weeks to access, connect, clean, model, give the data context (e.g., weather events or problems in ancillary systems), and then publish all these insights in a shareable report—this is the full arc Parsonnet refers to. But when all this comes together the right way, the real story of how a complex system works together can finally be told. Seeq’s role is to shorten the time it takes to tell that story by making all those time-consuming steps easier and faster to complete. The role of discovering the big IIOT insights is then driven by a process or production engineer whose job it is to monitor and diagnose large production systems.