Category Archives: Information Management

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

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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

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

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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

The Way To Maximize Value from M&A Assets

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In North America, the only constant when it comes to Oil and Gas companies is change.  With mergers and acquisitions (M&A), hydrocarbon assets constantly change hands. The value of acquired assets will then either be maintained, increased. decreased or maximized depending on how it is managed under the new owners. It is generally agreed the value can only be maximized when the asset’s geological models, reservoirs’ dynamics, and wells’ behavior are fully understood to their minute details. The new owner takes over the asset but is not guaranteed the people with the knowledge.

Building a clear understanding of the new asset becomes an urgent matter for the new owner.  This understanding is typically hidden under the mountain of data and files that change hands together with the asset. How and when the data is migrated to the new organization, therefore, can build up or bring down the value.

Typically, when an asset changes hands, the field staff remains, but the geologists and geoscientists that strategized the assets’ management may not follow the asset. This can mean that a great deal of knowledge is potentially lost in transition. This makes the data and documents that are delivered, after the transaction is complete, that much more important to understanding the details of the acquisition. Obtaining as much of this data as possible is crucial.  As a geologist who has been through multiple mergers put it:

“Knowledge like drilling through a fault is only known to the asset team operating the asset. This information is not publicly available. During the transition, getting any maps or reports from the geologists will help the acquiring company develop the right models and strategies to increase value. We want all the data we can get our hands on.”

Another key consideration is software licenses and versions, which may or may not transfer.  We find that the risk of losing the information permanently due to software incompatibility, licensing, or structure issues is very real. Migrating the technical data during the transitioning period will help protect the new owner from data loss.

Per Harvey Orth, a geophysicist and former CIO who has been through three mergers and acquisitions:

“In many cases, companies made an agreement with the software vendor to maintain a read-only copy of all the data; just in case they needed to extract some data they had not loaded into their production systems (for the new owner) or need the data for legal or tax reasons later (for the seller). In fact, keeping a read-only copy can be easily negotiated within a purchase agreement if you are divesting an asset. When acquiring, then everything and anything you can get your hands on can be essential to getting the most value from the field and should be migrated. ”

Tips to Protect the Value of New Assets 

Experts like us can help ensure that data is migrated quickly and efficiently and that the right data is obtained from the acquisition target. However, if inclined to manage the data transfer yourself, we share the following tips:

Make it Manageable, Prioritize it Right:

While all of the data and information is important, time is of the essence. Most companies will prioritize migrating “accounting” data, and rightly so, but to maximize value, technical data must also be at the top of the priority list. The following should top your priority list: production volumes and pressure data, land and lease data, well construction & intervention data (drilling, completions, and intervention history), Reservoir characterization (logs, paraphysics, core …etc.)

Do Due Diligence with a Master List

Getting your hands on all the data starts with a master list of all the assets,  including such things as active wells and their statuses. This list is the first-stop shop for every department that needs to build its knowledge and processes to manage the new assets. It is also the checklist against which to assess received information. If you have invested in a MDM (Master Data Management) system, then adding the new assets to the database should be one of your first steps.

Know What is Good Quality and What Is Not.

One of the biggest obstacles that companies face is the realization that their own data is not standardized and clean.  So now they are faced with the prospect of adding bad to bad.

Much can be said about investing in data quality standards and governance practice. It makes folding in any new assets easier, faster and cost effective. If you don’t have strong data standards yet, see if you can inherit them from the selling company,  or alternatively get help from IM experts to create these standards and merge legacy data with the new acquisitions.

Make it Findable: Tag Your Electronic files

Documents like geological maps, logs, lab reports, management presentations, and other files contain a wealth of information. Finding the right file can take considerable time, especially if the organizer was not you. Take advantage of Artificial Intelligence and “tag” the files based on their content. This will create a layer of metadata and make finding the right file based on “petroleum natural language” easier.

For additional information or a free consultation on migrating M&A data please contact us at info@certisinc.com

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

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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.

Why Connecting Silos With Better IM Architecture Is Important

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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’.

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 a Certis consultant. Email us at info@certisinc.com or call us on 281-377-5523.

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

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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.

To Build Fit Enterprise Solutions, Be Physical …

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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

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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.

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

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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.

WHAT COULD HAVE BEEN DONE?

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 long–term 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.

mitopmodel

Coming Current with E&P Data Management Efforts

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During the PNEC 2015 conference last week, we managed to entice some of the attendees, passing by our booth, to take part in a short survey. As an incentive we offered a chance to win a prize and made the survey brief, we could’t make it too long and risk getting little or no intelligence.

I’m not sure if any of you will find the results to be a revelation or offer anything new that you already did not know anecdotally. But if nothing else, they may substantiate “feelings” with some numbers.

You will be pleased to know that more than 60% of the replies are from operators or NOC.  In this week’s blog I share the results  and offer my thoughts on the first survey question.

graph

In the above question and graph “Which data projects are of high priority in your mind?”, it appears the industry continues to pursue data integration projects and the majority of the participants (73%) consider them to be the highest priority. Followed closely on the priority list were “data quality” projects (data governance and legacy data cleaning), 65% consider these a priority.

Thoughts…

Integration will always be at the top of the priority list in the E&P world until we truly connect the surface measurements with the subsurface data in real time. Also, given that data integration cannot be achieved without pristine data, it is no surprise that data quality follows integration as a close second.

Because many “data cleaning” projects are driven by the need to integrate, data quality efforts are still focused on incoming data and mostly on “identification” data, such is the case in MDM projects.

Nonetheless, how a well was configured 20 years earlier and what failures (or not) were encountered during those 20 years are telling facts to engineers. Therefore, the quality of “legacy” technical data is just as important as of new incoming data.

Reaching deeper than identification and header data to ensure technical information is complete and accurate is not only important for decision making, but as my friend at a major company would say: it is important firstly for safety reasons, then for removing waste (lean principle) and then for decisions.  Of course chipping away slowly at the large mountain of data is a grueling task and can be demotivating if there are only limited results.

To get them done right with impactful E&P business results, these projects should be tackled with a clear vision and a holistic approach. As an industry we need to think about  legacy data preparation strategically, do them once and be done with it.

Legacy data cleanup projects are temporary (with a start and an end date), experience tells me they are best accomplished by outsourcing them to professional data cleaning firms that fully understand E&P data.

This blog is getting too long, I’d better cover the results of the rest of the survey in the next one.

Please share your thoughts and correct me where you feel I got it wrong….