As oil prices fell from their highs of well over $100 per barrel, to less than $35 by early 2016, Energy and Resource companies were forced into a dramatic cost-cutting mode – streamlining focus and creating new efficiencies within their operations.
Now, even with the resurgence in price these industrial firms are realising the benefits of preserving operational efficiencies, as they look to scale more smartly – and more digitally – into the future. While the resources super-cycle of the early-2000s may be over, there are certainly still many untapped opportunities for Oil & Gas players.
The very nature of energy is undergoing radical transformation. The way we power our world a decade from now may look quite different from today; as the shift towards renewables gathers pace, emerging markets continue to industrialise, and as electric vehicles grab increasing market share.
However, by applying Artificial Intelligence (AI) and Cognitive Learning tools, industrial firms will find new market opportunities and unlock further productivity gains.
So, just how can AI help Oil & Gas companies plan for the future?
Predicting market trends and shifts – by analysing vast swathes of data – from macroeconomic trends, to weather patterns, to consumer spending patterns – Energy companies can predict future demand and dynamically resource their operations to capture the most profitable opportunities.
Exploration and mining – By analysing seismic vibrations, reservoir pressure differentials, strata permeability and other geospatial data, AI can effectively guide decisions about where to drill. For instance, Intel’s acquisition of Nervana Systems (a start-up applying AI to enhance operational efficiency in oil exploration) in 2016 caught the attention of many industry players, and became an important symbol for the future of exploration.
Dynamic operations – Embedded sensors enable better visibility across the value-chain – alerting managers when industrial equipment is liable to overheat, rerouting truck schedules to another depot based on traffic conditions, and the like. Many Oil & Gas players are not yet gathering the richness and depth of data that may be possible, as sensor manufacturers jostle with technology firms, and the industry players themselves, in commercialising the data that they’re generating.
Storage and transportation – The composition of Oil or Gas resources can be accurately monitored (in terms of temperature, moisture levels, transport time duration etc.). With the right cognitive tools, you’re able to better understand the effect of certain external conditions on the composition and profitability of the Resources.
Development of Platforms- Oil & Gas players have a golden opportunity to create their own digital platforms or ecosystems, enabling others to create apps and connect to their operations via APIs. With AI underpinning the development of these platforms, industrial firms are able to reach new markets and deepen their expertise, by pulling in the specialised services of partners, vendors, and others within the value chain.
Attracting talent- Due to their geographically-dispersed, engineering-intensive nature, the Oil & Gas industry has a heavy requirement for skilled management and technical staff. As the younger (millennial) workforce takes a fresh view on how they can add value to their company, they need the infrastructure and tools to turn their ambitions to reality. If the average millennial employee is used to AI-powered social media algorithms and digital navigation systems, then the corporate tools must have a similar level of sophistication.
Supply Chain efficiency– It’s important to note that Oil & Gas companies operate within complex supply chains – so rule-based anomaly detection can be used to detect things like fraud, duplicate payments, untapped volume discounts and other areas of direct cost benefit in the supply chain areas
Smarter Trading Decisions- In many areas, trading decisions are made by analysing a large number of factors. AI can be harnessed to extract relevant information from literally hundreds of sources – presenting data to analysts, to ensure optimal pricing.
Pipeline monitoring- The likes of encroachments, rusting, and leakage, can be monitored with drone and satellite pictures, robotics, AI and machine vision technology. In this way, it’s even possible to conduct maintenance and inspection without shutting down facilities.
Back office function compliance- Using Robotics-based automation, business processes at the back-end can help to ensure that compliance can be automated – reducing the overall costs and cycle time.
Generating value from your data- By training AI systems to go through digital information on oil wells and understand its context, the metadata can be extracted. This can be used by decision-makers so that intelligent, ‘context specific’ queries can be answered from these unstructured data sources. Over time, the models can become more comprehensive – deriving increasingly valuable insights.
Building the modelling context
Infusing AI into one’s operations is but one aspect of a coordinated digital transformation strategy, but for those forward-looking players that take advantage of AI, cognitive computing and deep learning, the benefits will be improved asset health and more efficient operations.
Building the data models that enable AI takes considerable time (masses of historical data records need to be gathered to create the ‘context’ in which today’s data can be analysed and operations can be optimised).
For this reason, it’s important to start one’s AI journey sooner rather than later.
In the future, the ability to blend the power of artificial intelligence, with human intuition and creativity, will be essential in navigating an uncertain future that’s characterised by fewer sources of growth, slimmer margins, and increasing complexity.