Getting energy to market is a challenge for all energy companies ranging from oil producers to electricity generators.\u00a0 Transporting and processing the natural gas resources that are produced by exploration and production companies is one of the main focus areas of the midstream oil and gas industry.\u00a0 While historically oil and gas companies have been criticized for being slow to adapt to technology, the recent prolonged low-price commodity market has caused the industry to turn to digital solutions in order to improve operational efficiency [1].<\/p>\n
DCP Midstream, with over 50,000 miles of natural gas gathering pipeline and over 60 gas processing plants, is a major player in the midstream segment of the oil and gas industry and is utilizing digital technology in the day to day operations of doing business [2].\u00a0 In this year\u2019s first quarter earnings call, the DCP Midstream Chairman, President & CEO expressed reluctance to let innovation pass by the industry and explained the company\u2019s plans to utilize data to help corporate decision making and asset optimization [3].<\/p>\n
DCP Midstream has already taken monumental steps towards aggregating and interpreting company data. The first step for any organization to start implementing machine learning is the thoughtful collection of data.\u00a0 Oil and gas operational data is often available but has historically been localized to rural plants.\u00a0 To collect data into a central repository, DCP Midstream entered into an enterprise agreement with OSIsoft to utilize their PI System to aggregate data from companywide assets [2][4]. Aggregated data is then analyzed to translate operating conditions into financial performance and made readily available to personnel at all levels of the organization.\u00a0 In addition, DCP Midstream installed an Integrated Collaboration Center (ICC) as a central location where data could be viewed, collectively interpreted, and informed decisions could be made with input from stakeholders across the organization. [2][4]<\/p>\n
With the infrastructure in place for centralized data collection and interpretation, DCP Midstream started to utilize the data to look closely at specific equipment performance and maintenance.\u00a0 The use of machine learning in equipment maintenance programs takes information from sensors embedded within machinery and looks for patterns in the data that indicate the machine needs attention before a failure event occurs [5]. Predictive analytics using machine learning allows for a precise maintenance program that predicts when failures might occur and provides sufficient time for preventative action [5].\u00a0 For DCP Midstream, compressors are an integral piece of equipment used to transport and process natural gas.\u00a0 DCP Midstream\u2019s utilization of equipment data collection and specialized analytics has improved compressor reliability and saved costs on equipment parts [2][4].<\/p>\n
In addition to the maintenance application specifically mentioned by DCP Midstream, it would be interesting to incorporate existing and forecasted weather data into the predictive data analytics.\u00a0 This would allow for plant operating parameters to be optimized considering upcoming external weather conditions. Extreme heat and freezing cold can significantly impact operating parameters of natural processing plants. \u00a0Utilizing forecasted weather data could allow for predictive operating conditions to be determined and implemented in advance instead of making reactive changes.\u00a0 Integrating predictive weather data into operator schedule assignments could also help to ensure operations staff are not deployed in advance of an impending snowstorm thereby avoiding the danger of adverse road conditions.<\/p>\n
Predictive data analytics and machine learning could also be developed to help guide DCP Midstream\u2019s overarching strategy and improve customer relations.\u00a0 Machine learning algorithms could be developed to anticipate geographic locations and well performance of customer drilling to provide advance guidance on where infrastructure will be needed, and the capacity required.\u00a0 This information could be used to anticipate bottlenecks in pipeline gathering systems and predict upcoming capital expenditure necessary to keep up with production.\u00a0 These types of predictive analytics could serve as a great competitive advantage for DCP Midstream to capture customer production and exceed customer expectations of midstream infrastructure speed to market.<\/p>\n
Utilizing digital technology has already improved DCP Midstream\u2019s business and is likely to further guide operations in the future.\u00a0 As machine learning is further deployed, how will DCP Midstream empower operations staff with data without decreasing headcount?\u00a0 How will the organization implement a robust feedback loop from operations staff to ensure that data is accurate and not skewed by instrumentation and sensors in need of repair?<\/p>\n
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[1] Paul Hart, \u201cWhat\u2019s Next?,\u201d Midstream Business<\/em>, Volume 8, Issue 1, (Jan\/Feb 2018): 26-34, ABI\/INFORM via ProQuest, accessed November 2018.<\/p>\n [2] Tauna Rignall, \u201cDCP Midstream \u2013 Enabling Business Transformation with the PI System: The DCP 2.0 Journey\u201d PowerPoint Presentation, 2018, https:\/\/cdn.osisoft.com\/osi\/presentations\/2018-uc-emea-barcelona\/UC18EU-D2OG04-DCPMidstream-Rignall-DCPMidstream-Journey-Begins-PISystem-DCP-20-Digital.pdf?_ga=2.111696653.827490330.1542037537-1233485663.1542037537<\/a>, accessed November 2018.<\/p>\n [3] Wouter T. van Kempen, Chairman President & CEO of DCP Midstream LLC, remarks made at \u2018DCP \u2013 Q1 2018 DCP Midstream LP Earnings Call,\u2019 May 8, 2018 3:00PM GMT.\u00a0 From edited transcript provided by Thomson Reuters Streetevents, http:\/\/ir.dcpmidstream.com\/phoenix.zhtml?c=194107&p=irol-calendar<\/a>, accessed November 2018.<\/p>\n [4] Tauna Rignall, \u201cDCP Midstream \u2013 Enabling Business Transformation with the PI System: The DCP 2.0 Journey\u201d presentation given at OSIsoft PI World Conference, Barcelona, Spain, 2018, https:\/\/www.osisoft.com\/Presentations\/DCP-Midstream—Enabling-Business-Transformation-with-the-PI-System–The-DCP-2-0-Journey\/<\/a>, accessed November 2018.<\/p>\n