Published by Nicholas Woodroof,
In fraccing, members of the oilfield services equipment (OFSE) ecosystem can include operators, service providers, equipment manufacturers, transportation and suppliers of pumps, blenders, hydration units, water, chemicals, proppants, and storage tanks. Everyone committed to the success of fraccing and bringing it to market plays a valuable role.
As the process stands today, each member of the value chain is capable in its current condition, but the overall ecosystem is disjointed. This is because data across fraccing operations is stuck in silos. Consider the different types of data that may be involved in fraccing operations: planning data (fraccing schedules), equipment data (failures and sensors), supply chain data (material inventory, truck locations, and field tickets), and operational data (pressures and volumes). There is a massive amount of data being generated and collected, but rarely – or ever – are the different sources of data connected to one another.
Figure 1. Oilfields hold a wealth of data, most of which is locked or unavailable.
As global oil demand is set to drop this year for the first time since the financial crisis of 2008 – 2009 (a ripple effect caused by the coronavirus outbreak), according to the International Energy Agency’s (IEA) March report, it is more imperative than ever that oil and gas companies liberate fraccing data from their silos in order to survive today’s volatile, low-cost oil environment.1 Data liberation and integration can transform the fraccing industry, driving greater operational efficiency, reducing energy use and costs, and contributing to new, more productive business models that benefit all members of the larger ecosystem.
Liberate, then contextualise
At its core, liberating and unlocking data simply means allowing users to view and export information. Access to open data means manufacturers and suppliers involved in the fraccing process can see exactly how products and services are performing onsite.
However, openness is not enough. Once the data is open, it must be contextualised so that it has meaning. It does not matter how much data operators, manufacturers, and suppliers have at their disposal if there is no meaning attached to it. Contextualisation for complete data management means combining a powerful blend of machine learning, rules engines, and subject-matter expertise to convert data into actionable knowledge.
Contextualised data and actionable knowledge create opportunities for better decision-making across teams and organisations. Here are four examples:
- Manufacturers and operators can greatly reduce risk by testing hypotheses on a digital twin of an asset. A digital twin is simply a virtual replica of industrial reality, created by combining different data sets about a physical product, process, or system. Once testing on the digital twin has concluded, the manufacturer or operator can then apply those lessons to the physical asset.
- Original equipment manufacturers (OEMs) and suppliers can use contextualised, high-quality data to deliver benefits such as longer service life, better performance, and greater efficiency. Using pumps as an example, contextualisation is a necessary part of optimising pump performance, since it depends on upstream and downstream conditions, as well as fluid properties. Adding information about weather conditions, such as ambient temperature, can give an OEM insight into how the quality of the lubricant – one of the main causes of frac pump failure – changes over time. In addition, geographical information can help operators optimise how it allocates maintenance resources such as personnel and spare parts. In other words, each additional data source adds another dimension of performance monitoring and performance optimisation.
- For field personnel and completion engineers, liberating and contextualising data in real time means the ability to analyse and monitor operational performance in a single, unified place – removing the need to access multiple systems to view documents, sensor data, and work orders. Beyond that, access to contextualised data can then be shared among all stakeholders, from electricians to external consultants. Data quality control is now unified within the platform, making it easy to troubleshoot problematic quality controls or gauges. Most, if not all, of listed maintenance issues can be anticipated and mitigated with access to intelligent maintenance planner applications powered by contextualised data.
- Finally, data sharing enables OEMs and operators to explore new business models. The traditional industrial infrastructure has limited supplier relationships to a transactional format. Data is now disrupting that relationship. In 2018, for example, Aker BP, an independent European upstream oil and gas company, launched a predictive maintenance pilot with pump supplier Framo. Aker BP granted Framo access to its industrial data, which the company used to inform its product development. Integrating with Aker BP’s enterprise resource planning (ERP) system, Framo could set work orders, enabling a feedback loop with design and engineering. This resulted in 30% reduced maintenance, 70% reduced shutdowns, and 40% increased pump availability. Earlier in 2020, Aker BP and Framo signed the first long-term, service-based predictive maintenance contract, where compensation is directly linked to facility uptime. This is an important step in the modernisation of the Norwegian shelf.
In the time of COVID-19, Framo engineers also have the ability to monitor the condition of seawater lift pumps from their home confinement and still maintain control – without having to visit the platform in the North Sea as a result of digital collaboration. Live data, machine learning and algorithms are being used at the Aker BP-operated platforms and this ensures control over all parameters regardless of location, whether it be at home, at Framo’s facilities or on the platform. The ongoing world situation emphasises the need to rethink and explore the possibilities for remote monitoring.
Figure 2. Maintenance and field operations paradigms are being transformed.
The working relationship between OEMs and operators referenced in this case is similar to what fraccing operators encounter when working with their service companies. Frac jobs are completed in stages; a typical job can include between 10 to 50 stages. Between each stage, the wireline goes in and perforates the well. This process usually lasts approximately 90 minutes, and during this time, the frac crew takes a break, the engineer makes calculations on how much chemical and proppant is left onsite, and the pumps are not running. In an industry where service-based contracts are the norm, the operator would be able to charge the pumps for only the amount of time they are actually running. The same sensors would be able to show flow rates and ensure correct pressures, as well as predict when seals and valves on the pumps need to be changed out rather than waiting on pump failure to do so.
Building a better ecosystem
Sharing data means redefining relationships. Building long-term trust and loyalty will maximise the value partners get from one another. By sharing industrial data, oil and gas companies can transform their business relationships, turning suppliers and service providers into strategic partners.
Going forward, companies in the OFSE ecosystem should ask themselves whether they are well-positioned to face a volatile and uncertain future. To test whether they are pursuing the right strategies, they should ask themselves a few critical questions:
- Is the operating model in place resilient to changes in the industry?
- Are the right collaboration models with customers and other suppliers in place?
- Is it possible to address the need to reduce the cost for customers, such as through standardisation for example?
- Are digital/data and analytics being used?
Organisations that can answer in the affirmative to most or all of the above are likely to be on track when it comes to strategy setting and establishing strong, agile positions as they move into what will likely be a challenging short-term. Those who are unable to answer or would respond in the negative should make some important considerations; namely where and how they may best change investment strategies, develop collaboration models and make smart investments in digital-, data- and analytics platforms in order to set themselves on the right path.
Sustained investment in new technologies enables companies to capture new growth and quickly adapt to market fluctuations. Liberated, contextualised data will allow everyone in the ecosystem to contribute and thrive.
- International Energy Agency, ‘Oil Market Report – March 2020’, www.iea.org/reports/oil-market-report-march-2020.
Read the article online at: https://www.oilfieldtechnology.com/special-reports/05082020/joined-up-data/
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