Tag Archives: ML

Jobs Are Changing In Oil and Gas. Here Is How and Why…

If we fast forward 10 years, what type of jobs will be in the landscape of upstream oil and gas ? Would your current job description read the same for a new entrant? Will it still exist?

There is no denying that technological changes are upon us.  When it comes to digital data technology, there are two waves of change in plain sight, one behind the other.  The first wave is in a bundle of technology enhancing and enabling one another. This bundle includes faster connectivity, IOT/ IIOT, Cloud, AI (Artificial Intelligence) and Robots.  This change is already strongly underway.  The second wave is following closely behind the first and will probably have the same impact if not larger. This is in the distributed ledger and crypto technology, specifically Blockchain Technology.

The Why

The industry went through/is undergoing a crew change, with baby boomers retiring and younger employees stepping in. This crew change is coinciding with lower commodity prices, opening the door and arms to any technology could impact the bottom line.  These new technologies are proving themselves plenty, resulting in ever greater shifts in daily tasks, if not entire job descriptions.

Major technological change, like the above, always calls for adaptation or job abolition.  New skills will arise, some will be obsolete, yet others will morph. Reskilling or upskilling is no longer optional.

Back to the question, will your current job description read the same in 10 years? It’s a question worth pondering and answering.

The What and How

From our recent experience (and research), here are some of the many jobs that may morph into something different in the same way the map drafters (draftsman) morphed to Geotechs in the previous digital revolution from paper to digital:

  • Shale Reservoir Engineering: This job has increasingly focused on reserves estimation and economics. Granular estimates require customized production analyses. Today this task is time consuming and takes the bulk of Reservoir Engineer’s time. As capabilities in AI modeling advance, I believe “Supervised” or “Reinforcement” Machine Learning (ML) could automate this work, altering the job description to focus on detailed management and recovery efficiency of the reservoir.
  • Surveillance and Maintenance Engineering: today, for many digitally maturing organizations, automated surveillance and managing by exception has become the norm. However, predictive and prescriptive maintenance enabled by ML and Augmented Reality (AR) will become the new norm.  Furthermore, with blockchain or otherwise, vendors can be integrated in the ecosystem to ascertain equipment optimal performance for their customers (as additional services).  A combination of IIoT, ML, AR and expanded ecosystem alters inhouse surveillance and maintenance jobs, yet opens up new opportunities for service companies.
  • Lease Brokers and Land Administrators: with increased connectivity to source systems and with verifiable trusted data on immutable blockchain ledger, much of the due-diligence and verification work could be reduced if not fully automated in the future. These jobs could morph to something new and exciting.

Summary: Stay Relevent

IoT, IIOT, Cloud, AR, VR, Bots, ML-AI, and Blockchain are all here to stay. But as powerful as the digital world in general and the machines in particular, they are not without flaws or challenges. Human mind will always be more flexible and can deal with more and forseeable scenarios. nonetheless, some jobs will become obsolete, others will morph, and new ones will arise.          

Knowing what could change, how and when, will help to prepare a strategic road map to stay ahead and relevant, for both the individual and for the company.

What jobs do you think will be different in 10 years? What new and exciting jobs will become available? Alternatively, let me know your job and I will share my thoughts on how it would morph (info@certisinc.com).

Here are some curated references:

  • SPE-194746-MS: The End of Petroleum Engineering as We Know It. From the SPE library
  • What to Do When Industry Disruption Threatens Your Career. MIT Sloan Management Review, Spring 2019

Why it’s time for Petroleum Geologists and Engineers to move away from generic data scientists

The industry has finally warmed up to Artificial Intelligence (AI) technologies. This is good news. AI technologies are a great opportunity for operators to increase their efficiency and boost their competitive advantage and safe operations. And, in fact, many have already done just that.

But not every company will deploy AI at the same pace or with the same capabilities. Skills to build meaningful and impactful AI models are still scarce. Also, the people who understand the earth’s and hydrocarbon’s physics are not always the same ones who know how to use “Data Science (DS) and AI” tools. For that reason, some Exploration & Production (E&P)  companies opt for a centralized DS team that has skills in statistics and building AI models. In this centralized model, engineers’ “exploratory” and “what-if” analyses go to a central team to build a predictive model for their hypothesis.

This process is not ideal. It takes longer to reach to an acceptable and final model even when a request is prioritized (long cycle time).  How can you speed it up? While a central DS team may seem to be the only option at the moment, I would argue that eventually those who understand the physics should be the ones building and confirming their own AI and Machine Learning (ML) models. I argue, some of brightest engineering and geoscience ideas are yet to come out to industry, once they learn the tools.

Why Change?

For some petro-proefessionals, learning a new coding language (python for example) is not fun. After all if they wanted to be coders they would be in a different place. We have been there before. The situation is analogous with the old DOS commands days, which were the domain of specialists and enthusiasts. But as soon as software (like Microsoft Office) came out with Graphical User Interface (GUI) – where we clicked and typed a natural language – then the engineers, geologists and the whole world came onboard. Well, AI and ML tools are making that turn and do not require any coding. 

It was no longer a stretch to get petroleum engineers to use Excel spreadsheets, then and it is an expectation now. We should have the same expection for them using advanced analytics, AI and ML tools. 

 

Some suggestions

I leave you with some tips to reach that goal (if you agree with the above premises);

  • Look for advanced analytics tools with graphical user interfaces (as opposed to those that require you learn a new language like python). I like this software’s interface Data Robot, www.datarobot.com
  • Make sure your software can easily connect to data sources – it’s not enough to have a software with good GUI.
  • Automate the flow of information and facilitate access to information – this is key to success, especially for data warehouses/ lakes or hubs.
  • Make sure your users are trained in advanced analytics principles and on the software.  At the very least, engineers should be able to build some basic predictive models.
  • Consider cloud data warehouses and tools to get the power to run large datasets such as logs and seismic. We are following Snowflake https://www.snowflake.com  and MEME SQL https://www.memsql.com/

Certis consults and delivers data services. We are systems agnostic. We focus on helping oil and gas clients set up their backbone data and processes. Find out more about our services