By Alex Robart and Uday Turaga
Over the past few years, oil and gas majors have announced a number of partnerships around digital with a wide range of high-tech players. Two events, however, underscore the progress these partnerships are making. First, Shell and Microsoft announced a strategic alliance last week where the energy major will supply renewable power to help Microsoft meet its commitment of going all renewable by 2025. In addition, Microsoft will continue its three-year-old partnership to develop artificial intelligence (AI) and other digital tools for Shell. This partnership has, so far, yielded close to 50 AI-powered applications that are helping Shell reduce greenhouse gas emissions, improve energy efficiency, and enhance worker safety.
Second, BP presented its energy transition strategy recently – see our detailed post on this here – where one of the presentations focused heavily on digital. BP is aspiring to use digital investments to achieve $1 billion of cost reductions and $1 billion of EBITDA increase by 2023 and 2025, respectively. Further, BP is planning its energy transition pivot by heavily exploiting the company’s customer touchpoints and retail footprint. Such aspirations will need a complex, sophisticated, and multi-layered digital ecosystem that the company has painted in some detail. Finally, like Shell, BP seeks to build its low-carbon energy business by serving cities and corporate customer such as Microsoft.
These two events collectively paint the growing reliance and symbiosis that one should expect between energy and digital over the next decade. While these future scenarios are vivid and exciting, progress on digital so far has been mixed. Even so, we have seen a lot of progress especially driven by COVID-19. Drawing from the authors’ investment, research, and consulting experiences, we look at how digital is doing in oil and gas.
- Cloud data management underpins digital innovation and is best achieved with a phased, prioritized approach as opposed to an all-in strategy that can bog progress down.
Oil and gas companies still transmit most operational data via SCADA (supervisory control and data acquisition) and often store that data in one or multiple legacy on-premise systems. On-premise systems make it harder to move data around an organization and to take advantage of the innovation ecosystem that is exploding around cloud computing, particularly artificial intelligence (AI).
Moving technical data into a cloud data architecture — for instance, moving data from a Pi historian into Microsoft Azure or Amazon Web Services (AWS) — represents a foundational initiative that underpins digital innovation. However, too many oil and gas operators have taken on more than they can chew, kicking off ambitious and broadly scoped cloud migrations. Such “big bang” initiatives neither prioritize systems/data that can deliver the biggest operational and financial impact nor consider use cases in parallel, and seriously hobble digital initiatives.
The most successful operators have been pursuing phased cloud migrations based on use cases and prioritization for impact. Demonstrating operational and financial value step-by-step is critical to help the organization sustain momentum for what will be a complex journey.
- Automation’s success with back-office operations is convincing operators to pursue its use in operations in a serious, material fashion.
Given the recent turmoil in commodity prices, doing more with less is one of the few surviving priorities. Oftentimes this means fewer employees but operators should also see this freeing up staff time to focus on higher-value activities that deliver better outcomes.
Over the last few years, a new wave of process automation tools have emerged called robotic process automation (or RPA). These software tools have become important to streamline enterprise operations and reduce costs in rules-driven back-office processes. Companies configure the software (or “robot”) to capture and interpret applications for processing a transaction, extracting data, triggering responses, and communicating with other enterprise systems. RPA scenarios range from something as simple as generating an automatic response to an email to deploying thousands of bots each programmed to automate jobs in an ERP system. The tools are typically low-cost and easy to implement requiring no customer software or complex systems integrations.
In the operational context, automation is increasingly seen as an effective tool to optimize headcount in the field reducing costs without compromising and often improving safety. The diversity, flexibility, and costs of automation options — both hardware and software — continue to fall as novel sensors and technologies supporting the Industrial Internet of Things (IIoT) have continued to mature. As upstream oil and gas operators are re-evaluating production economics across assets/fields and processes, many are shutting-in marginal wells, which rarely have remote automation and/or remote visibility. Operators are realizing that such decision making can be better informed and yield optimal performance outcomes if wells have remote automation and data visibility.
- COVID-19 has clearly highlighted the efficacy and value proposition of remote operations prompting a step change in its adoption and new investments.
The COVID-19 pandemic has forced the global workforce to work from homes at an unprecedented scale exposing just how many enterprise systems and processes were not designed to support remote operations. This extends from everyday IT firewalls and videoconferencing tools to more complex remote operations centers populated with dozens of screens from on-premise systems many of which are extremely challenging to access remotely.
This abrupt shift has also exposed the inflexibility of many enterprise information (IT) and operational technology (OT) systems forcing companies to make immediate investments to support a more flexible, remote workforce. Legacy on-premise systems exacerbate the challenge, while OT systems are more complex to access remotely by design given security concerns. Cloud architectures are more flexible and make remote access easier to achieve.
While the current remote nature of the workforce will not be permanent, parts of it will be as the economy settles on a “new normal” that will be more remote than it was in the pre-COVID world. Companies that are not planning and investing now to support a more remote long-term operating model will risk losing talent, particularly younger talent, that will come to expect more flexible work arrangements.
The past six months have also demolished many oil and gas operational dogmas. Remote technical services especially for refineries and petrochemical plants have often been delivered by expert and consultant teams that travel from site to site. Although some operators and several vendors have tried offering these services remotely, operational teams at plant sites have resisted them historically. That resistance has melted away following COVID-19, and many operators will likely accelerate adoption of these offerings far more quickly in the next few years.
- Best-in-class oil and gas operators are finding that digital can help with improved environmental, social, and governance (ESG) compliance and outcomes.
Steadily increasing pressure from investors and consumers means that, despite the challenging times, ESG initiatives are not going away. ESG has gone from a nice-to-have feature to a must-have pre-requisite that is influencing investments and will ultimately decide winners and losers in the energy market going forward.
Emissions is one of the most prominent areas of focus, with methane at the top of the list. The emergence of drones and IIoT technologies provide a new suite of low-cost tools to monitor and understand emissions. These technologies have matured significantly in recent years and are ready for scaling across a wide range of operational contexts. In fact, environmental activists are using these tools far more effectively than a number of oil and gas operators.
The first step to solving any problem is understanding it so many emissions monitoring initiatives have continued through the crisis. We expect more operators to expand existing monitoring programs and launch new ones using the range of new low-cost digital technologies now available. As ESG targets intensify over time, operators will have to rely on a wider range of digital offerings to track assets, measure and gather data, and analyze it to make real-time changes to ensure desired performance outcomes.
- Advanced analytics and artificial intelligence for asset and process optimization represent the efficient frontier but the quick wins will focus on using them for predictive maintenance.
Analytics has been a buzzword in recent years with many operators struggling to understand what it means or how it can deliver value. At its core, analytics is about bringing better data to bear to understand operations and then apply a range of analytical techniques to improve performance. These techniques range from simple data visualization through statistical analyses to the range of more advanced statistical techniques that can all be broadly categorized as artificial intelligence including machine learning, deep learning, reinforcement learning, etc. In the operational context, these tools tend to be applied to address two broad sets of use cases: predictive maintenance and process / asset performance optimization.
Many operators are building on top of existing OT data infrastructure such as Pi data historian and using more advanced data visualization/analysis tools to better understand their real-time operational data and implement improved processes with new alerts/alarms or even predictions. Predictive maintenance is fundamentally about understanding the operating condition of in-service equipment to understand wear/tear and potential failures. More advanced operators have implemented AI-powered “anomaly detection” to augment traditional condition-based monitoring systems, which can serve as a powerful new set of data signals to ensure that equipment does not fail, resulting in non-productive time (NPT).
Optimization is all about improving system or process performance, while reducing energy inputs. Improved analytics and AI can be a powerful tool to achieve these outcomes, often while reducing staff manual data reviews and diagnoses that tend to be less scientific or systematic than many expect. There are a range of vendors that have proven solutions across industry use cases to help accelerate efforts to implement both predictive maintenance and optimization programs although oil and gas operator adoption has been mixed.
ADI Analytics’ consulting and research teams are working on a broad range of digital issues across oil & gas, power, industrials, and chemicals. Recent work has (1) explored how digital tools can enhance ESG outcomes, (2) benchmarked various digital product and service offerings in upstream, (3) evaluated digital initiatives by process automation vendors, (4) supported digital transformations in oil and gas majors, and (5) led due diligence across a number of digital start-ups. Contact us to learn more, and see how ADI Analytics can support your needs in digital.
– Alex Robart is Managing Director, Unconventional Capital.
