Thought leadership by Accenture
The industry’s best-kept secret may be that O&G players are perfectly positioned to take advantage of AI and machine learning (ML). The potential to unlock trapped value is significant across the exploration and production (E&P) value chain for three key reasons:
- The industry collects incredible amounts of sub-surface and surface data every minute but uses only a small fraction of that information to drive integrated E&P decisions.
- O&G players were among the first to use supercomputers, mainly to tackle highly complex subsurface problems. Today, they are starting to generate tremendous value by connecting surface and commercial considerations with subsurface reservoir variables.
- The industry has many disparate and disjointed databases that capture information concerning rock-properties, well costs, supply chain, operations, maintenance, finance, and human resources (HR). When connected, these granular data can enable prompt and reliable decision making.
Since the 2014 downturn, the O&G industry has struggled to lower supply costs and reduce the long cycle times (latency) that have characterized E&P projects. AI/ML has the potential to resolve both these problems.
Our analysis shows the industry can reduce the cost of supply 30 to 50 percent and cut cycle times by multiple years when it employs advanced analytics in E&P decision making. Figure 1 shows the key capabilities analytics can enable.
Figure 1: Untapped value potential from analytics
A successful analytics transformation
O&G companies can unleash the full potential of advanced analytics by solving complexity problems and scaling solutions within the company.
Organizations that generate value from analytics do so primarily because all elements of the transformation constantly ask two questions:
- Can data help my team make better decisions and improve profitability?
- What’s holding us back from getting the most from our data and associated analytics?
Weaving an analytics mindset into the organizational DNA involves fundamental behavioral changes that address the factors illustrated in Figure 2.
Figure 2: Factors that influence the behavioral change required to adopt an analytics mindset
5 key areas for O&G leaders to focus on
As with any transformational change, upskilling the workforce for advanced analytics will require deliberate planning and effort from all ranks of organizations, including leadership support and sponsorship.
How can O&G leaders approach analytics upskilling effectively?
Understand how the work will change
Identify the required skills, and the changes to leadership and culture required to adopt the eventual analytics solutions.
Define the analytics competencies required
Decide which competencies the company needs to achieve its vision and which roles it should upskill.
Use the broader ecosystem to train staff
Utilize the wider ecosystem of learning providers to deliver cost-effective training while keeping pace with the speed at which technologies and tools change.
Create an environment for analytics to flourish
Role model the priority that analytics has within the organization on a continuous basis.
Ensure the analytics upskilling strategy works
Acknowledge that specific pivots could take place when beginning the program to enable easier adjustments when underway.
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