Chevron Corporation is an American multinational company operating in the oil and gas industry. Chevron has been making huge losses associated with downtime in its numerous oil fields and refineries across the world. The company announced plans to launch predictive maintenance as a way of preventing breakdowns before they occur. An article published on the Wall Street Journal presents a detailed description of the findings of a pilot study by Chevron ( Castellanos, 2018). The statistical findings quantify the effectiveness of combining data collection through sensors and analysis through cloud-based predictive analysis to show when maintenance problems might occur. In doing so, Chevron can conduct maintenance on time prevent breakdowns.
The article is associated with cloud-based predictive analysis that Microsoft Corporation offers. In brief, the article presents the outcome of a six-month pilot study on the effectiveness of the predictive analysis. As part of the study, four wireless sensors were put strategically placed on the heat exchangers meant for oil processing in oil refineries. The researchers analyzed the data using Microsoft’s cloud-based predictive analytics applications and determine when the heat exchangers will require cleaning or maintenance services. In contrast, Chevron Corp. has been using sensors for measuring the temperature and the flow of oil, which only shows the past and present with no predictive capacity.
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According to Chevron’s executives, the firm has been spending millions of dollars annually to repair its equipment across the world. Further, the company has been making losses associated with downtime during the maintenance and repair time. Therefore, predicting maintenance time can significantly reduce the losses by preventing such breakdowns. For example, conducting maintenance when needed is more economical than conducting maintenance regularly even when the machines are in good condition. Also, conducting maintenance in a timely manner avoids repairs that are more costly than maintenance.
Oil companies such as Exxon Mobil can take advantage of Microsoft’s cloud-based predictive analysis to avoid costly repairs and reduce downtime. The predictive analysis will not only increase the efficiency of the maintenance department but also reduce the losses associated with multiple repairs and downtime. In addition, the predictive nature of the findings provides the much-needed platform for preventing environmental disasters associated with accidental oil spills and leakages of oil pipelines. For example, a study by Jernelöv (2010) revealed how Exxon Mobil’s oil spill in 1989 killed over 1,000 sea otters and other marine wildlife in Alaska. Based on the findings from the pilot study conducted, it is clear that Exxon Mobil can reduce the environmental risk risks by showing when such disasters might occur. The statistical analysis will have a significant financial impact on the company while at the same time assist the company to achieve its responsibility of protecting the environment. Also, it is important to note how such accidents can have a negative impact on the company’s revenue in the case of fines or lawsuits. Hence, Exxon Mobil needs to adopt a similar statistical application in the future.
In summary, the article provides crucial insights regarding the impact of cloud-based predictive analysis in the oil and gas industry. It shows the strategy of combining data collection through sensors, relaying of data through the Internet of Things, and analysis through cloud computing to predict maintenance problems. The fact that Microsoft Corporation offers the services makes it possible for other competitors such as Exxon Mobil in the oil and gas industry to adopt the analytical application.
References
Castellanos, S. (2018, September 05). Chevron Launching Predictive Maintenance to Oil Fields, Refineries. Retrieved October 1, 2018, from: https://blogs.wsj.com/cio/2018/09/05/chevron-launching-predictive-maintenance-to-oil-fields-refineries/
Jernelöv, A. (2010). The threats from oil spills: Now, then, and in the future. Ambio: Royal Swedish Academy of Science, 39(5), 353-66
