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Technology changing how assets are managed

Apr 25, 2019
6 min read
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Boston Consulting Group’s Bjorn Ewers, partner & managing director, Jean-Christophe Bernardini, associate director and Aida Mbaye, consultant talk about how technology is changing the energy landscape


How is technology changing the oil and gas landscape?

Over the past two years, oil and gas (O&G) companies have accelerated their transformation by leveraging technologies to be more resilient and competitive stimulated by resurgent oil prices.

The O&G value chain and industry has undergone a new technological transformation as evident by new partnerships, digital initiatives and centers and the introduction of new capabilities like chief digital officer and data scientists.

This evolution is changing the way that O&G companies operate. The industry is perceiving technology not only as a lever to gain competitive advantage but as a requirement to navigate the transition.


What trends do you see with regard to asset management in the oil and gas industry?

In this new context, asset management becomes central to be able to maximise the value of each asset and get the extra barrel at a minimum cost to be able to face the uncertainty in demand and price.

To reach these objectives, we are witnessing the deployment of a variety of technological solutions:

  • Investment in technology and digital in particular is set to grow 2 to 3 times faster and impact the overall oil and gas spend for the next 5 years.
  • Leading companies are investing in 3 key streams:

            o          Internal capabilities

            o          Partnerships with leading technology providers

            o          Investment via corporate ventures


What are some potential key differentiators to look for in an asset management solution?

Implementing an asset management solution requires careful consideration of the strategic vision of each company, its specific technical and operations context and the economic implications. O&G companies should evaluate multiple issues along the business application layer, the asset data layer and the connected items:

  • Business application layer: What applications should be prioritised? What data is needed? What level of investment is required?
  • Asset data layer: Which data architecture model will work? How to integrate existing structures? Whether to buy from single or multiple vendors?
  • Connected items: What requisites should be prioritised? What digital level can be used for brownfield? What roadmap will be suitable for retrofitting?

It is essential to invest in solutions that address operators’ concerns and solve their operational issues before focusing on innovation. The focus should be on flexible solutions that fit asset portfolios and help develop fit-for-purpose functionalities.

On the other side, it is essential to position the machine learning strategy at the center of decision making to better leverage data and models and find optimal operational and economic solutions in terms of performance and field development. 


How will asset management drive value for an oil and gas company in the future?

Asset management is a key solution to help companies make faster and better decisions by offering an integrated view of different value drivers of your asset, with regards to production recovery and development and integrity management.

A fit-for-purpose asset management solution will help operators to better define potential solutions and:

  • Reduce the constraints / bottlenecks (subsurface, surface, export, flaring) that impact the maximum production potential of each asset
  • Maximise the parameters of the operating envelope of each asset
  • Improve the performance of the safety and business critical equipment by defining a fit for purpose maintenance strategy and reduce impact of reactive maintenance both in term of production and OPEX
  • Define optimal solutions in term of field development that will increase the recovery of each reservoir and reduce companies’ CAPEX


What are the main challenges for oil and gas companies implementing new asset management systems?

New asset management systems today have the potential to generate value which is accepted by the industry but we have noticed several specific implementation challenges, along with the ones we foresee in any digital solution implementation process.

  • Risk aversion: Asset management systems are critical to operations, in an industry that is risk averse and slow to adopt new technologies. This makes leadership endorsement and approval beyond funding a requirement for success.
  • Data quality: More often than not companies are not satisfied with the available data and there is a tendency to fix data issues before starting the implementation process. This translates into years of data governance work before new systems are implemented. Companies need to be aware of data imperfections and build a proper data strategy and governance in parallel to a technology roll out.
  • Talent limitations: Oil and gas companies are set on who they need to hire to produce oil but in order to disrupt they need to enhance the diversity of their workforce in relation to background. They need to hire non-oil and gas people to bring new perspectives to the table.
  • Capex optimisation: While investments are slowly recovering, there is a general cautiousness in the industry and mismanagement of investment can deprive key projects from optimal resources. Operators need to use the momentum, allocate resources to the right project and deprioritise the ones not meeting expectations.


What approaches are companies making in terms of machine learning?

Operators have options to implement machine learning, some opt to procure from established service providers like GE (Predix) while others focus on building in house capabilities. Both methods face challenges mainly related to scalability.  Machine learning is not “plug and play” per se – a system successfully implemented in one asset is not necessarily transferable to another asset. In fact, more than 50 per cent of the component will be asset specific - not reusable due to the specific operational context of each asset.


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