Why Connecting Silos With Better IM Architecture Is Important

If you work in an oil and gas company, then you are familiar with the functional divides. We are all familiar with the jokes about geologists vs. engineers. We laugh and even create our own. But jokes aside, oil and gas companies operate in silos and with reason.

But while organizational silos may be necessary to excel and maintain standards of excellence, collaboration and connection across the silos are crucial for survival.

For an energy company to produce hydrocarbons from an asset, it needs all the departments to work together (geoscience, engineering, finance, land, supply chain …etc.). This requires sharing of detailed information and collaborating beyond meeting rooms and email attachments. But the reality in many oil and gas companies today is different, functional silos extend to information silos.

Connected Silos Are Good. Isolated Silos Are Bad

In an attempt to connect silos, “Asset Teams” or “Matrix” organizations are formed and incentive plans are carefully crafted to share goals between functions. These are great strides, but no matter the organizational structure, or the incentive provided, miscommunications, delays, and poor information hand-over are still common place. Until we solve the problem of seamless information sharing, the gap between functional departments will persist; because we are human and we rationalize our decisions differently.  This is where technology and automation (if architected correctly) can play a role in closing the gap between the silos.

Asset team members and supporting business staff have an obligation to share information not only through meetings and email attachments but through organizing and indexing asset files throughout the life of the asset. Fit-for-Purpose IM architecture has a stratigic role to play in closing the gap between the functional silos.  

Connecting Functional Silos With IM Takes Vision & Organizational Commitment 

Advancements in IM (Information Management) and BPMS (Business Process Management Systems) can easily close a big part of the remaining gap. But many companies have not been successful in doing so despite significant investments in data and process projects. There can be many reasons for this, I share with you two of the most common pitfalls I come across:

  • Silo IM projects or systems –  Architecting and working on IM projects within one function without regard to impact on other departments. I have seen millions of dollars spent to solve isolated geoscience data needs, without accounting for impact on engineering and land departments. Or spent on Exploration IM projects without regard to Appraisal and Development phases of the asset. Quite often, organizations do not take the time to look at the end-to-end processes and its impact on company’s goals. As a result, millions of dollars are spent on IM projects without bringing the silos any closer.  Connecting silos through an IM architecture requires a global vision.
  • Lack of commitment to enterprise standards – If each department defines and collects information according to their own needs without regard of the company’s needs, it is up to other departments to translate and reformat. This often means rework and repetitive verification whenever information reaches a new departmental ‘checkpoint’.

Each of the above pitfalls can be mitigated by recognizing the information dependencies and commonalities between departments then architecting global solutions based on accepted standards and strong technology. It takes a solid vision and commitment.

For a free consultation on how to connect silos effectively please schedule your appointment with Certis consultants by sending an email to Info@certisinc.com or call us.

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.

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.

TECHNICAL DATA

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.

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.

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

juice

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.