As we are getting used to the new normal of USD 50/bbl oil, buzzwords such as efficiencies, automation, best practices, and partnering have become standard in our discussions. This is all well and good but how real are these new processes? Like the cyclical nature of our industry, will these processes also become cyclical, soon to be relegated to small incremental change, rather than the needed step change as the price of oil recovers, or as our experienced people ride off into the sunset? What are the real opportunities for disruptive step change that will help us to catch up and leapfrog other industries that have given rise to companies such as Amazon, Google, and Apple, and have driven the renewal of the auto, airline, and transportation industries?
Today, companies are patting themselves on the back for handling the “great crew change,” and rightly so. But is this really cause for celebration? Should we be celebrating simply for doing our job? Would the great crew change be manageable if activity and the price of oil had not crashed? What about the individuals who have lost their jobs in the downturn and have not been able to recover? Can experience really be fully transferred by training classes? Does the young engineer walking onto an offshore drilling rig immediately notice the small things that “just don’t seem right” that an “old hand” might? How can we leverage communications technology to make a sustainable step change in efficiency and lower costs without compromising quality, safety, and the environment?
Three Critical Areas
Firstly, we need to consider key performance indicators (KPIs). A famous quote from Lou Gerstner, IBM’s CEO from 1993 to 2002, “People don’t do what you expect but what you inspect,” is often quoted as, “Inspect what you expect.” In other words, we need to measure performance. However, the metrics used in tracking KPIs must be directly related to the expected outcomes, the component data easy to manage, and the outputs statistically meaningful.
KPIs can be divided into two main groups. Lag indicators are typically measures of present or past performance, easy to measure but difficult to relate to future or improved performance. Lead indicators are harder to measure as they typically relate to processes or activities to improve performance, but are equally important as lag indicators. Lag indicators enable valid data analytics, benchmarking, and target metrics or “par” values for performance. Data analytics provide “calibration points” to track continuous improvement and ideas on lead indicators by identifying processes that consistently give good results.
An example of KPIs being used to drive and improve operational efficiency in drilling was presented by Nabors at an industry conference in May. Downtime is a standard lag indicator and minimizing downtime is of course absolutely necessary in any economy. However, this metric does not in itself indicate how efficiency can be improved. The KPI that downtime addresses is nonproductive (NPT) time. Digging a little deeper, the idea of invisible lost time (ILT) makes NPT more sharply focused and clearly identifies the opportunity cost of less efficient drilling as lost time and a problem to be fixed. The reduction in ILT then translates directly into improved efficiency, quantifying the value of joint teams with the client to dissect the operations into discrete steps with the appropriate KPIs. Pragmatically, to make this work, Nabors automatically collects all drilling data into a central database for analysis.
Lag KPIs in production might be the current ones related to production rates. These are great KPIs and, as with downtime in drilling, have stood the test over decades. It is time perhaps that operators more fully quantify the value in working with service providers by (a) using focused lead indicators, and (b) sharing production data with their service partner.
The Possibilities of Automation
Secondly, “automation” needs to be clearly defined beyond the buzzword if as an industry we are to move to the next level. Standards for data accuracy are critical, whether in automated closed-loop systems downhole, rig floor operations, a combination of the two, or in database management. Real-time and remote access to data, electronic data recorders at the wellsite, the cloud, and phenomenal data transmission rates open up endless possibilities, including intelligent rigs and processes integrating the talents of engineers, geoscientists, information technology (IT), business, and the collective field experience of our industry. NOV, Nabors, and other companies have made major investments in automating drilling processes. Commercial products and services are readily available today, but there is still a long road ahead as we challenge every stage of the existing workflows.
During Gerstner’s tenure, IBM’s market capitalization rose from USD 29 billion to USD 168 billion as the company refocused on its unique competitive advantage—its ability to provide integrated, broad-based IT solutions for its customers.
Why are we so far behind in the use of IT? We have had to be focused on operations due to the hazardous nature of the job; secondly, competition has been based on proprietary processes and services; and thirdly, as an engineer-driven industry, we have tended to be resistant to change.
The oil and gas industry needs to remain focused on safe operations but perhaps we should be researching completely new rig designs, not just improvements to current designs. For example, the auto industry is not only researching new battery designs to extend the range of electric vehicles, but also a completely new motor design and driverless cars. We have had our fair share of innovations over the years, but given lower oil prices, the growth of renewables, and fewer people wanting the nomadic life of working on remote oil rigs, are we innovating fast enough? Are we using optimal transmission speeds for data from downhole to surface to remote offices to allow reliable automation based on accurate data and appropriate human intervention? Is cybersecurity part of our culture and best in class? Are we still attracting the best young minds and are we taking advantage of the wealth of experience in our young, over-65 professionals? Should we encourage and facilitate freelancers to develop new apps with open systems, akin to app development for Windows or Mac?
Thirdly, how can we best leverage personnel and open systems with IT? The great crew change primarily replaces the retirees with the younger folks by training. The need to truly think outside the box and continuously bring new technology to the industry requires the best and brightest in each field, including software developers who have no interest or aptitude to work in the field or young engineers more interested in robotics than getting their hands dirty or working long hours.
We might still want to attract and harness some of this talent if the experienced folks could remain integral parts of the teams. The new multidisciplinary team might include the IT talent, the young genius, and the experienced guru, along with the traditional engineers and geoscientists. The ability to leverage the experience and expertise of operations experts who are at the stage where they do not want to retire but want a more flexible work schedule could be a game changer. Many of the young, over-65s would be happy to be part of remote teams sharing their experience on projects while mentoring younger engineers who want a more balanced work and social life.
