Category Archives: Uncategorized

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

If we fast forward 10 years, what type of jobs will be still be relevant in upstream oil and gas ? Would your current job description read the same for a new applicant? 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 and machines are , they are not without flaws or challenges. The 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 you to prepare a strategic road map to stay ahead and relevant.

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

Harnessing The Newly Found Trust

In our previous blog, we presented blockchain as a welcome addition to the E&P landscape and added our voice to those extolling the value in baking data integrity from the ground up when re-engineering processes with it. In this article, we look at how a business can be reshaped, simply by having all business partners trusting the same data.

Trusted data is key to streamlining wasteful processes and reshaping how we do business together. The Economist in 2015 correctly identified blockchain as ‘the trust machine’. As the technology underpinning Bitcoin, it allowed organizations or persons with no particular trust in one another to collaborate without having to go through a neutral central authority. This capacity allows complex transactions to be completed more quickly, and at the same time, for companies to align on common interests and share risks and returns on new opportunities.

One company that is harnessing the shift in trust paradigm and advancing the ball on performance-based contracts is Data Gumbo (DG). This team’s solution elegantly aligns operators, rig owners and service providers on Key Performance Indicators (KPI).

Last week I attended the Oil and Gas Smart Contract conference. A demo from DG showed the use of their Smart Contract feature using drilling KPIs. The contract is prepared, negotiated and signed, all on their platform. Once signed, the contract is locked in an immutable distributed ledger (blockchain). This means all parties possess the same exact copy on the same network with no permissions to update it unless all agree.  This alone is an improvement from the abilities of most current systems.

But the blockchain takes us further, it allows us to codify contracts and automatically trigger workflows, in what they call “Smart Contracts”. Once codified (and the data connections are put in place with the rig and ERPs), data from the rig can feed the codified contract directly, which in turn could trigger the release of payments. Notice, within this example, no invoices needed, no data reconciliation is needed, and there is no chasing after Accounts Payable or Accounts Receivable. That is needle-moving change for organizations that handle hundreds of thousands of payments per year.

With blockchain, this could be taken further, to “crypto assets”, but we are not sure if the industry is ready yet. A discussion for another blog, meanwhile check out Ziyen Energy, they are staying ahead of the curve. (https://www.ziyen.com/ziyencoin_future_currency_of_oil_industry/)

What is your interest in blockchain? Interested to know more? Contact us for a free consultation.  info@certisinc.com

Considering Blockchain for E&P? Don’t Forget to Bake-in Data Integrity

A Welcomed Addition to the E&P Digital Landscape, Blockchain

Over the decades, energy companies have adopted many technologies and tools to enhance and advance their competitive advantage and business margins. Information Technology (IT) has played a significant role in those enhancements. Most recent examples in the news coming from AI, IoT and Cloud computing. Nonetheless, oil and gas is still struggling with few persistent data issues:

  1. security and data leakage
  2. siloed systems and/or a spaghetti of connections from ETLs, and
  3. reliability of data quality.

As a consequence, workflows and processes are still filled with examples of inefficiency and rework. But an emerging technology, Blockchain[1] may have the answer we’ve been waiting for.

Blockchain is a distributed ledger and crypto technology which brings with it the promise of connectivity, security and data reliability that we have not been able to achieve with past technology, and which was desperately needed in this very specialized industry.

Data Integrity Baked In Business Processes

Because it is possible to enter wrong data in blockchain applications, thinking about how to capture and disseminate good data quality using blockchain applications is important to ensure quality analysis and sound, informed decision making.

Blockchains can live alongside the current data architecture as a data governance layer and passively monitor quality. However, baking data integrity into business applications from the ground up is the best way forward.

Along with distributed ledger and crypto signatures, blockchain also offers a smart contract feature which allows us to re-engineer and re-imagine processes and how we all work together. Thinking through the last-mile problem to build data integrity into the application from the ground up will be one of the key success factors that will make or break your ultimate solution. New jobs will arise to ensure that correct information is being captured, as specialists that can verify information validity will be a must. These specialists will be similar to insurance adjustors that testify a claim is valid prior to insurance payment.

The further good news is blockchain data will only need to be verified once. No more having to verify the same data over and over again!!! Who has time for that?!

Recommended Resources

[1] A plethora of material describing blockchain are everywhere. Make sure you are reading  curated articles.  One good reference is from IBM (not endorsing their technology, Certis is vendor agnostic): Blockchain for dummies


For additional information, architecting, implementing and training on successful Blockchain projects please contact us at info@certisinc.com

How To Turbocharge Oil & Gas Analyses With Machine Learning and The Right EIM Foundation

It is generally accepted that good analysis of oil and gas data results in actionable insights, which in turn leads to better profits and growth. With today’s advancements in technology and processing power, more data and better analysis are easily achievable but will require the right EIM (Enterprise Information Management)  foundation to make “all” data available and “analyses-ready”.

The evidence of those analytics are clear and ubiquitous. In an article in JPT (Journal of Petroleum Technology) by Stephen Rassenfoss, “Four Answers To the Question: What Can I Learn From Analytics?”, Devon Energy concludes it is possible to increase production by 25% by drilling the lateral toe-up in Cana-Woodford Shale. Range Resources, responding to a different question and with Machine Learning (ML) analysis, concluded more production in the Marcellus is associated with wells fracked with as much sand volume as the reservoir can handle.

All Data All The Time = More Studies More Return

Looking closer at the article, both studies were based on a relatively small data set; Devon Energy and Range Resources only used 300 and 156 wells respectively.  Both companies stated that a larger data set would help their respective studies. So, why some studies rely on a small population of wells when there are thousands more that could have been included to reach a deeper understanding.

While the answer depends on the study itself, we find two key data”preparation” problems that may contribute to the answer a) data findability/ availability b) data readiness for analyses. In some E&P companies, data preparation can consume over 50% of total study’s time. This is where I believe EIM can make a difference by taking a proactive role.

 Three Strategic EIM Initiatives to Turbocharge Your Organization’s Analytics

Information preparation for exploratory analytics like the above, require Oil and Gas companies to embrace a new paradigm in EIM. The traditional “data management” has its applications but can be rigid and limiting because it requires predefined schemas.

We share our favorite three EIM strategic initiatives to deliver  more, trustworthy and analyses-ready information:

  • Strategic and Selective Information Governance Program – A strong data governance model ensures data can be trusted, correlated and integrated, this is a foundational step and will take standardizing, and mastering key entities and attributes.   Tip: key enabling technology is Master Data Management (MDM)
  •  Multi-Stream Data Correlation – Together with the MDM, “Big Data” technology and processes enable the inclusion and further correlation of data from a variety of streams, without the prejudice of predefined data schema.
  • Collaborative Process and Partnership – From years of lessons learned, we’ve noticed that none of the above will move the needle much at all if implemented in isolation. A collaborative process with the sole purpose of fostering a close partnership between IM engineers/ architects, data scientists, and the business, is what differentiates success from failure. As the organization finds new “nuggets of insights,” the EIM team’s role is to put the necessary structure in place to capture the required data systematically and then infiltrate it into the organization’s DNA.

New analytics are positively changing how we produce and manage oil and gas fields. Companies that invest in getting their EIM foundation right will lead the race among its competition.

Disclosure:

For help on defining and implementing EIM strategy please contact us.
With Petroleum Engineers, Geoscientist, Data Scientists and Enterprise Information Architects on the Certis team, we help companies design and implement EIM solutions that support their business goals. for more information on our services please email us at info@certisinc.com.

The Rise of the Oil & Gas Analytical Citizen (and company)

 

Then

Fifteen years ago, while at the Society of Petroleum Engineers (SPE.org) conference, I was introduced to artificial intelligence (AI) tools specific for Oil and Gas use.  I was very excited to learn more and build models to optimize production and understand its key influencers for example.  I was certain data-driven insights were what this industry needed. What engineer wouldn’t want to use this?

To my surprise though, only a handful of engineers were ready to embrace the technology, and most said their organizations simply weren’t ready for it.

Now

Fast forward to 2017. Data-Driven and AI analytics are reasonably commonplace among engineers. Tools are found in nearly every company – not just the major companies, but also in the independent players and ambitious smaller companies. How did this happen?

This is what happened: Time, technology and people changed.

A widespread of technology is usually a result of ease-of-use, reliability, and usefulness. One needs only look as far as Apple’s iPhone. Apple created an amazingly intuitive, reliable and useful phone, with an ever-growing market of applications.
With each new iteration, more and more people wanted an iPhone. Today not only is every citizen using a smartphone but they are entirely comfortable asking digital strangers named Siri, Alexa or Cortana for directions or to plan their daily activities.

Advancements in smartphones (and subsequent widespread adoption) raised the technological comfort level of the everyday user. Consequently, today’s oil and gas citizens easily embrace new technology and will take the time to experiment with different ideas and tools.

These same consumers are not afraid of change – they expect it now.

Statistical and AI based analytical tools were (and are) perfectly placed to succeed in Oil and Gas. Increased adoption was inevitable. But they are still not at the level I expected 15 years ago. Why?

What needs to happen in Oil & Gas next?

The problem is that while the market is ripe, oil and gas infrastructure and culture must catch up as well. More integrated and better quality data must seamlessly flow to analytical tools so an average company-citizen (and not IT) can easily explore any data, trust it and generate meaningful calculations or reports, faster, efficiently and more insightful than ever.

That vision translates to three actions:

  • Prepare a data strategy, architecture, and governance that enable an analytical company.  Few advancements in the MDM and Data Lake areas that will put you on a good pathway.
  • More intuitive and easier to use analytical tools must infiltrate the organization, the way outlook or excel does. Take advantage of smart searches, NLP (Natural Language Processing), and machine learning to name a few.
  • Create and encourage a culture that expects and enforces data-driven decisions across the entire company, for this you will need a clear vision and commitment from the leaders.

Until then, AI and Data Driven analyses remain in the hands of the chosen few ‘nerds’ – thanks to The Bing Bang Theory, being a ‘nerd’ is totally cool.

For greater clarity on your position, contact Esta Henderson – esta@certisinc.com – tel: +1.281.674.3224 to schedule a complimentary strategy appraisal with Fatima Alsubhi, our CEO.

Crossing The Border From a Mere Change to Cultural Expectation for QUALITY DATA

Culture sets certain expectations of behavior, and once accepted, there is no deviation. Even if you are removed from the cultural origin, these behaviors are ingrained and follow long after. 
I recently experienced this first hand, when a dear friend of mine was diagnosed with cancer. Of course, I was very distraught. When, a few weeks later, he was admitted to hospital for surgery, I visited him and his wonderful wife. This was natural to me, visiting a sick or injured friend at home or in the hospital, is not only a kind gesture, but is an expected social obligation ingrained in me since childhood.  What seemed to my friends as a thoughtful gesture was something I could not imagine not doing, or imagine friends of my culture not doing for me.
 
It made me wonder what makes a behavior “culturally” accepted and ingrained? When did visiting a sick friend become more than a thoughtful gesture, and cross the barrier into social obligation? How did this transition occur?  
 
These musings extended to Oil and Gas corporate culture. What behaviors were so ingrained at work that they had become second nature? Did they serve a purpose, such as to improve data quality? If not, what would it take to weave in these behaviors, and make them the expected social norm, and a clear moral obligation or expected practice within an organization? In an ideal world, these cultural obligations would lead to employees and employers alike feeling that it is “on them” to report and correct data quality issues, no matter at what point in the process they were discovered.  
 
 I thought it might be a good idea to ask my readers these questions. Are such behaviors ingrained in your workplace deep enough to be considered cultural? How would you weave them in, if not? If they are a part of your corporate culture, can you point to any policies and practices that may have led to this?  

Reminded Again, Narrow Focus Leads To Failure Every Time. Why do Some Data Projects Never Make It?

toronto
In 1993, an incident occurred in the Toronto Dominion Bank Tower that caught national attention, enough so that it made the infamous “Darwin Awards”. A lawyer, in an attempt to demonstrate the safety and strength of the building’s windows to visiting law students, crashed through a pane of glass with his shoulder and fell 24 floors to his death. Maybe the Glass did not break but it pulled off the wall. 
 
The lawyer made a classic mistake, he had focused on one specific thing to the exclusion of the big picture. If he had taken a look at his hypothesis from a wider angle, he might have considered the numerous other factors  that may have contributed to his doomed demonstration – the bond between the glass and the frame,  the yielding effect of material after repeated tests, or simply the view of the courtyard below (the high risk should it fail) might have been enough to make him reconsider his “leap of logic”. He focused on a specific item and ignored the other factors. 
 
Such a narrowed focus is equally risky to an information management project, or any project really. Although we are getting better we often focus on one thing: technology implementation and ignore other aspects.
From my experience, many factors contribute to the success or failure of information management in Oil & Gas projects. People, technology, processes, legacy data, Integration, a company’s culture, operational model, infrastructure, time constraints, or external influences such as vendors and partners, just to name a few. Each has a degree of influence on the project, but rarely will they cause the demise of the project – unless they are ignored! The key to success in any project is the consideration of all aspects, and an assessment of the risks they impose, prior to spending millions.
As an example, let’s look at survey data. How would you manage that data?
Often, companies focus on two elements:
  • Finding the technology to host the data
  • Migration of the data to the new solution
Success is declared at the end of these two steps, but two years down the road, the business has not embraced the solution, or worse yet, they continue to see incomplete surveys, a problem the new technology was supposed to solve. Failure, in this case, is less abrupt than an appointment with the Toronto Dominion Courtyard, but it is failure nonetheless.
 
More often than not, projects like the one above fail to take into consideration the other aspects that will keep data quality intact.
Even more often, these projects fail to consider external factors such as data acquisition vendors. These external vendors have their own processes and formats. If your project ignores our increasingly integrated world, and cannot cooperate with the processes, technology, and data formats of key external vendors and business partners, your project will yield very limited results and will not be sustainable. 

To achieve sustainable success in data management projects or any projects for that matter, it is necessary to consider the context surrounding the project, not just the specifics. Without this context, like the unfortunate lawyer, your project too can look forward to a rather significant fall.