Author Archives: Certis Inc.

About Certis Inc.

Certis IS Inc. Provides Information Management Services to Oil and Gas Companies. This blog contains the views and opinions of the founder and CEO of Certis IS Inc.

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



Fifteen years ago, while at the Society of Petroleum Engineers ( 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.


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 – – tel: +1.281.674.3224 to schedule a complimentary strategy appraisal with Fatima Alsubhi, our CEO.

Managing Data For The Sake Of Managing Data Or Are You Making a Difference?

A client and now dear friend of mine told me once “We are not managing data for the sake of data management, we are doing it to support the business.” We connected immediately, and I took this as a sign that she would achieve great things for her company.

Supporting the business is the only reason to justify an IM group in an E&P company. But how does an Information Management connect (and prove the value of) enterprise initiatives that may take years to complete, to business operations that fluctuate with commodity prices?

Let’s look at the typical experience of many companies in the past few years:

When oil prices hovered for a lengthy period at approximately $100 a barrel, most businesses prioritized exploration and production to find new plays as fast as possible. Drill faster, complete faster, produce sooner, and find more. In this “growth” mode data came in, fast and furious. Companies threw in serious money to gather and analyze every data.

However, when oil prices hit $26 a barrel, “survival” mode kicked in. Most companies renegotiated their contracts and loans while trying to maintain base oil or gas production (revenue) at the least cost possible. Meeting or exceeding production targets became existential, not just good for business. Here in this mode, some data gathering slowed significantly, while the focus on producing wells and its facilities heightened.

Two entirely different sets of processes, completely different sets of priorities, could force totally different data management projects. In ‘growth’ mode, the focus was on the speed of processing directional surveys, logs, perforation, costs, and frac data. In ‘survival mode,’ the focus changed to Wells’ and facilities’ performance and integrity.


All technical data is critical to an oil and gas company and should be available, boom or bust. It is also, entirely understandable that, in a world of limited resources, projects with the highest impact to the business are prioritized first. Shifting IM priorities with the change in commodity prices or change of business focus is not simple.

However, a good EIM strategy will support the business in any mode, growth, survival, or any other mode, with ease. The good news is, it is entirely possible to have such an EIM strategy, simply by focusing efforts towards organizational goals through growth and lean times alike. Also, today’s advancements in technology allow for increased agility in organizational response. But you got to have a strategy.

Once a strategy is defined and embraced, every information management project, for both structured and unstructured information, must advance the ball towards the goal, or just be killed. This is not as easy as it sounds, of course. It requires expertise and the dedicated effort. Prioritizing efforts, identifying weaknesses, choosing the right technology, all can help your organization grow faster in growth mode, as well as to swim, rather than tread water in survival mode.

Has your organization defined a strategy yet? Are they working to support the business, or are they just managing data for the sake of data management?

For greater clarity on your position, call or email us to schedule a complimentary strategy appraisal with one of our consultants.

To Build Fit Enterprise Solutions, Be Physical …

The British and the Americans speak the same language. But, say “I have a flat” to a British, and it means something completely different than said to an American. The former would congratulate you, and the latter would feel sorry for you. Flat in the UK means an apartment. Flat in Houston means a flat tire. The same 4 words, arranged in the exact same way, in what is ostensibly the same language, and yet either speaker would confuse their audience, if the audiences were transposed.

It is the same thing in business – if you cross different corporate cultures or even inter-organizational boundaries, industry terminology might sound the same but mean very different things. Sometimes we think we are communicating, but we are not.

Why is this a problem? Because it is not possible to build an enterprise data management solution to serve all departments without addressing variations in expectations for the same word. Especially if the term in question is one that defines your organization’s values and activities.

“Sometimes we think we are communicating, but we are not”

In the corporate world of Energy E&P, the word “completion” means different things to the different departments. If you mention a “Completion” to a Landman, he will assume you are referring to the subsurface horizon for his leases (it is more complex than this, but for the sake of this argument we need not dive into details). If a “Completion” is referenced to a Production Engineer, she immediately thinks of the intersection of a wellbore and a reservoir horizon. To a Completion Engineer, the same term means the process of completing a well after the well has reached final depth.

As organizations’ data management practice become more matured, they start to make their way towards the right of the EIM MM (Enterprise Information Management Maturity Model). Centralized solutions such as Master Data Management (MDM) are important and are designed to serve ALL departments to break as many silos as possible.

Naturally, to create a centralized solution that addresses needs across the enterprise, you must first reach consensus on how to build that solution. The solution must ensure that the data is NOT LOST, NOT OVERWRITTEN and is FULLY CAPTURED and useful to EVERYONE. What is the best way to reach consensus without the risk of losing data?

Get Physical

To answer the above question, many agree that information systems need to be built based on the physical reality to gather granular data …

By basing your data on the physical world and capture granular data as practically possible, you not only make it possible to capture all related information but also possible to report it in any combination of grouping and queries. See the example in figure 1.

Focus on Enterprise Needs and Departmental needs will follow…

I have seen systems that ignore wellbore data yet store only completions per well. At other clients, I have seen systems that take short cuts by storing wells, wellbore and wellbore completion data in one line (this necessitates overwriting old completion data with new everytime there is a change), these are “fit-for-purpose” systems.  These are not enterprise level solutions, but rather serve departmental needs.

Too often systems are designed for the need of one group/department/purpose rather than for the need of the company as a whole. However, if the needs of the whole are defined and understood, both company and groups will have what they need and then some.

Let’s look at an example to clarify this position:

Figure 1 Multi lateral well

Figure 1 Multi lateral well

In Figure 1 above, how would you store the data for the well in your organization or your department? Would you define the data captured as one well, three bores, and three completions? Or maybe two completions? One?
Depending on your department or organizational definitions, any of the above definitions could be fit-for-purpose correct. Accounting systems might keep track of ONLY one completion if it made Payroll and Tax sense. While Land may only keep track of 2 completions if the bores are in two zones. An engineer would track three completions and will be specific to one completion per wellbore. The regulatory department may want you to report something entirely different.
How do we decide the number of completions so that the information is captured accurately, yet remains useful to a Landman, Accountant, Engineer, and Geoscientist? Build based on the physical reality and stay granular.
In Figure 1, physically speaking, we see one well with three paths (3 wellbores). Each bore has its own configuration that open to the reservoir (completions). In total, this well has three different ‘Completions’,  one ‘Completion’ for each of the horizontal bores.
Accounting can query how many different cost centers the well has, and depending on the production (and other complex rules) the answer could be three but it could be 1.  Depending on the lease agreement, Landman could get a result of one or 3 completions. An engineer can also easily query and graph this data to find the three pathways, and determine each completion job per wellbore.
While it could be argued that data needs to be presented differently to each department, the underlying source data must reflect the physical truth. After all, we cannot control what people call things and certainly cannot change the lingo.

Juicy Data Aligned


Around the corner from my house is a local shop selling an excellent assortment of fresh vegetable and fruit juices. Having tried their product, I was hooked, and thought it would be a good addition to my diet on a daily basis. But I knew with my schedule that unless I made a financial commitment, and paid ahead of time, I would simply forget to return on a regular basis.  For this reason, I broached the subject of a subscription with the vendor. If the juice was already paid for, and all I had to do was drop in and pick it up, I’d save time, and have incentive to stop by (or waste money).

However, the owner of the shop did not have a subscription model, and had no set process for handling one. But as any great business person does when dealing with a potential long term loyal customer, the owner accommodated my proposition, and simply wrote the subscription terms on a piece of paper (my name, total number of juices owed and date of first purchase), and communicated the arrangement with her staff. This piece of paper, was tacked to the wall behind the counter. I could now walk in at any time, and ask for my juice. Yess!

Of course, this wasn’t a perfect system, but it aligned with business needs (more repeat business), and worked without fail, until, of course, it eventually failed. On my second to last visit, the clerk behind the counter could not find the paper. Whether or not I got the juice owed to me that day is irrelevant to the topic at hand…the business response, however, is not.

When I went in today, they had a bigger piece of paper, with a fluorescent tag on it and large fonts. More importantly, they had also added another data point, labeled ‘REMAINING DRINKS’. This simple addition to their data and slight change to the process made it easier and faster for the business to serve a client. Previously, the salesperson would have to count the number of drinks I had had to date, add the current order, then deduct from the total subscription. But now, at a glance a salesperson can tell if I have remaining drinks or not, and as you can imagine deducting the 2 juices I picked up today from the twelve remaining is far simpler. Not to mention the data and process adjustment, helped them avoid liability, and improved their margins (more time to serve other customers). To me, this is a perfect example of aligning data solutions to business needs.

There are several parallels in the above analogy to our business, the oil and gas industry, albeit with a great deal more complexity. The data needs of our petro professionals, land, geoscience and engineering have been proven to translate directly into financial gains, but are we doing enough listening to what the real needs of the business are? Reference our blog on Better Capital Allocation With A Rear-View Mirror – Look Back for an example on what it takes to align data to corporate needs.

There is real value to harvest inside an individual organization when data strategies are elevated to higher standards. Like the juice shop, oil and gas can reap benefits from improved data handling in terms of response time, reduction in overhead, and client (stakeholder) satisfaction, but on a far larger scale.  If the juice shop had not adapted their methodology in response to their failure of process (even if it wasn’t hugely likely to reoccur) the customer perception might be that they didn’t care to provide better service. Instead, they might just get unofficial advertising from readers asking where I get my juice. I’d suggest that the oil and gas industry could benefit from similar data-handling improvements. Most companies today align their data management strategies to departmental and functional needs.  Unless the data is also aligned to the corporate goals many companies will continue to leave money on the table.

We have been handicapped by high margins, will this happen again or will we learn?

About 15 to 20 years ago, we started to discuss and plan the implementation of databases in Oil and Gas, in hopes of  reaping the benefits of all its promises. And we did plan and deploy those databases.  It is now no longer conceivable to draw geological maps by hand or to store production volumes in books. Also, in the last ten years, we have moved beyond simple storage of digital content and have started looking into managing data quality more aggressively. Here too, we have made inroads. But have we done enough?

Have you ever wondered why companies are still cleaning their data over and over again? Or why we are still putting up building blocks such as standards for master well lists and hierarchies? It seems to me that the industry as a whole is not able to break through the foundational stages of enterprise information management.  Because they can’t break through, they are unable to achieve a sustainable, robust foundation that allows their systems to  keep pace with business growth or business assets diversification.

Perversely, I believe this is because the oil and gas industry has been handicapped by high margins. When a company is making money despite itself, throwing additional bodies and resources to solve a pressing issue seems like the fastest and most effective solution in that moment. Because the industry is structured in such a way that opportunities have to be seized in the moment, there is often little time to wait for the right solution to be implemented.

Throwing money at a problem is not always the wrong thing to do. However, if it becomes your go-to solution, you are asking for trouble.

I would argue that highly leveraged companies have put themselves at high risk of bankruptcy because they do not invest sufficiently in efficiency and agility through optimized processes and quality information flow. For example, coming up with the most effective completion for your reservoir requires access to quality and granular technical data. This data does not just happen, it takes a great deal of wiring and plumbing work to obtain your organization’s data and processes, luckily if done right, it is a one-time investment with minimal operational upkeep.

According to Bloomberg, CNN and Oil & Gas 360 reports, during this ongoing downturn, at least 60 companies have entered chapter 11 in the USA alone. Ultra, Swift, Sabine, Quicksilver, American Energy are just a few of these highly leveraged but otherwise technically excellent companies.

Without the required behind the scenes investment, engineers and geoscientist will  find a way to get the data they need to make decisions. They will, and often do, work hard to bring data from many siloed systems. For each engineer to still have to massage data is throwing money at the problem. If the correct platform is implemented in your company, this information would flow like clockwork to everyone that needs it with little to no manual work.


We all know it is never the wrong time to make a profit. Consequently, it is never the wrong time to invest in the right foundation. During a downturn, lower demand creates an abundance of the only resource unavailable during an upturn – time. This time, spent wisely, could bring huge dividends during the next upswing in prices. Conversely, during a period of high prices, it is the other resources we cannot afford to waste. During a boom, we cannot ignore building sustainable longterm data and process solutions the RIGHT way.

It is never the wrong time to make a profit. Consequently, it is never the wrong time to invest in the right foundation.

Of course, there is no single “right way” that will work for everyone. The right way for your organization is entirely subjective, the only rule being that it must align with your company’s operations models and goals. By contrast, the only truly wrong way is to do nothing, or invest nothing at all.

If your organization has survived more than ten years, then it has seen more than one downturn, along with prosperous times. If you’ve been bitten before, it’s time to be twice shy. Don’t let the false security of high margins handicap you from attaining sustainable and long-term information management solutions.

Here are some key pointers that you probably already know:

      Track and automate repeatable tasks – many of your organization’s manual and repeatable tasks have become easier to track and automate with the help of BPMS solutions. Gain transparency into your processes, automate them, and make them leaner whenever possible.  

   Avoid Duplication of Effort – Siloed systems and departmental communication issues result in significant duplicated efforts or reworks of the same data.  Implementing strong data QA process upstream can resolve this. The farther upstream, the better. For example, geoscientists are forced to rework their maps when they discover inaccuracy in the elevation or directional survey data. These are simple low hanging fruits that should be easy to remove by implementing controls at the source, and at each stop along the way.

  Take an Enterprise View –  Most E&P companies fall under the enterprise category. Even if they are a smaller player, they often employ more people than the average small to medium business  (especially during a boom) and deal with a large number of vendors, suppliers, and clients. Your organization should deploy enterprise solutions that match your company’s enterprise operations model. Most E&P companies fall in the lower right quadrant in the below MIT matrix.


Non-Disruptive, Non-Invasive Data Governance for Oil & Gas

Trading post

Establishing data governance is not a new activity. It is, at its heart, an extension of man’s desire to define the world, and to communicate these discoveries in a more efficient manner. A good data standard can be linked to the use of Latin as a lingua franca by merchants in medieval Europe. Few English merchants could speak Dutch, but most were taught Latin (and vice versa). Latin provided a set of definitions and rules understood by all, promoted by rote memorization of grammar and a large number of books, policed by data stewards in the form of tutors who rapped children’s knuckles when they got it wrong.  (ok, maybe this is a stretch a bit, but I like the story :-))

Not a Blank Slate

In Oil and Gas, I see data governance programs in many forms, from centralized formats to a completely distributed approach, and everything in between. These implementations come with varying degrees of success.

So when I came across Robert Seiner’s book “Non-Invasive Data Governance” I asked myself could this work for oil and gas? In my judgment, a distributed, organic, and non-invasive approach could be an option to deploy a data governance program in a faster, more uniform and comprehensive manner, which in turn would yield better success.

Non-invasive data governance is built around identifying already in place, de facto standards, and processes to capture and manipulate data. If there isn’t one standard, then “converging” and “formalizing” to one standard that suits is put in place. In the new world, data stewards will be recognized “formally” and will maintain “universal” standards for work they have been doing all along…

To me, this approach has far-reaching implications to raise the bar on data quality standards. This approach weaves the quality standards in the DNA and the culture of an organization.

Business Specific Pidgin

Let’s continue the historical analogy a little. Trade was still conducted without the advantage of a lingua franca, albeit with greater difficulty. Typically, this was accomplished by the evolution of pidgin languages. The first encounters, however, were most likely exercises in frustration, as both parties attempted to learn one another’s needs, defined goods and services, and the perceived value of these. In speaking a pidgin language with another merchant, if either party used a differing definition, or even presented his offer in an unfamiliar sentence structure, the business venture could go south very quickly. Similarly, within a single oil and gas organization. For each data group, there needs to be one standard for all.

The oil and gas industry would not be where it is today without some established data standards and data processes already in place. Data governance will never be a blank slate. The problem is that while standards exist that are recognized across the industry, there are many terms that differ from one team to another and are not quite formalized or fully recognized.

The non-invasive DG approach is to formalize what is currently not formal and monitor it for continuous improvement over time. For example, wellbore survey data can be captured in different ways, none of which are wrong, just different. One team would store latitude, longitude, geodetic system, Easting, Northing, and distance. Another team might use Negative and Positive to indicate directions instead of Easting and Northing. These are very subtle differences, however, when flowing data from one system to another (and data flow we do a lot) a level of accuracy is lost in the translation.

Let me know your thoughts…


Technical Documents Architecture: Separate for Sustainable Efficiency

If like many oil and gas companies, your technical documents are scattered, or buried in folders and nested folders, you have an opportunity to increase the efficiency of your petro-professionals by organizing their technical documents, speed up how they locate these documents, or better yet, do both. If you do it right, you can architect a solution that is sustainable.

Organizing electronic files for an oil and gas company is not as complex as it first may seem. It is very similar to the way you organize files and documents on your PC. The fundamental question you must ask yourself is “ How do I sustainably tag or organize my files so that I can find what I need in 5 seconds or less?”. This question guides my efforts for all designs and solutions that I help my client’s engineers and geoscientists build. The topic is big and long, but here I would like to share my thoughts on architecture.

This topic is big with many angles, for this blog I would like to share my thoughts on architecting the technical documents healthy environment.

This Valentine’s month: Separate for Sustainability

Many oil and gas companies do not distinguish between the active work area and a long-term final-version area for their electronic files. What we find instead is one environment with ALL technical files in one place. This repository often contains both active and long-gone projects, including files for divested wells. Attempts to organize this chaotic mess happen every other year. This kind of architecture requires organizing these files every few years, with a hefty price tag!


In my opinion, for an oil and gas company to have a sustainable documents management practice, there should be at least 4 working areas within your environment (see diagram below).

TDRM architecture

An Amicable Separation

Area #1 Team work area

By establishing a day-to-day work area that can by definition be an organic mess coordinated by those who know exactly what everything is, your team can collaborate in whatever method works best for them. This area is usually cluttered with analyses files, multiple versions of a document, manuals, research material, cost proposals from vendors, and more.

This flexibility is key to a productive, happy work environment. It only becomes a problem when others are exposed to a ‘mess’ that is not of their own making. For this reason, each team should have their own defined work area.

(Yes, I hear some of you say today’s technology are designed curtail this mess and no need to have a separate work environment. I think that may be possible if the technology is used by a homogenous skilled staff.  Oil and gas staff are of all ages and at different levels of software savviness)

Area #2 Final versions area

Separating your final-versions area from the working area has two immediate benefits

1) Efficiently and effectively declare and distribute the final version to the enterprise

2) Allows the removal of inactive files from work area (declutter).

Your final versions area should provide access to (and the easy identification of) the latest version of a report in a timely manner and without delays. Unfortunately,  this area is often not formalized (it is not separated from area #1), causing delays for other teams who need access to a given file – they need to notify your team, have a member of your team identify the correct file, and then possibly to send them the file.

Often, distribution of final versions is a complex dance of requests and delivery between multiple teams or individuals. By separating the archival/final versions area, and providing access to authorized resources, this jitterbug contest can become a synchronized line dance. If all parties that need a file can identify the right file on their own, and retrieve it themselves, significant delays can be avoided.

Furthermore, by separating the final-version area from a work area, you have a chance to sustain the serenity and completeness of technical well files and specifically well files and records (most important assets you can have). Allowing any company to easily open a data room when and if needed.

Areas # 3 and 4: External Collaboration and Access

When considering work areas and final versions, it is important to consider accessibility, external as well as internal. Providing data to JV partners bases on their WI and JOV data requirements and collaborating with vendors during well directional design or completion treatment is essential to keeping the technical documents preserved and not lost in a web of email attachments.

To me, this architecture is non-invasive or intrusive to engineers and geoscientists workflow.

Summary: Separate, but don’t go far

Separating final-version area from work area can have an immediate and strong benefit to productivity, balancing team flexibility with the requirements of other teams in the organization. While the day-to-day work area should be organic and flexible, it is important that the Archival/Final Version repository is defined. This is because it is not serving just one team, but the needs of an organization as a whole. This separation of working area and final versions/archival area provides a sustainable solution that meets the 5-second accessibility requirement outlined above.

Having outlined the benefits of this simple change in a complex working environment, we’d love to hear from the community. Do you have a better approach? Questions regarding implementation? Have you implemented something like this, and if so, what was your experience? Whatever the input, we would love to hear from you.