Category Archives: Integration

The Way To Maximize Value from M&A Assets

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

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

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.

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.

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 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.

mitopmodel

Coming Current with E&P Data Management Efforts

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….

 

Part 3 of 3: Are we progressing? Oil & Gas Data Management Journey the 2000s

The 1990’s shopping spree for applications produced a spaghetti of links between databases and applications while also chipping away the petro professional’s effective time with manual data entry. Then, a wave of mega M&As hit the industry in late 90s early part of the 2000s.

Mega M&As (mergers and acquisitions) continued into the first part of the 2000s, bringing with them—at least for those on the acquiring side – a new level of data management complexity.

With mega M&As, the acquiring companies inherit more databases and systems, and many physical boxes upon boxes of data. This influx of information proved to be too much at the outset and companies struggled – and continue to struggle – to check the quality of the technical data they’d inherited. Unknown at the time, the data quality issues present at the outset of these M&As would have lasting effects on current and future data management efforts. In some cases it gave rise to law suites that were settled in millions of dollars. 

Early 2000s

Companies started to experiment with the Internet.  At that time, that meant experimenting with simple reporting and limited intelligence on the intranet.  Reports were still mostly distributed via email attachments and/or posted in a centralized network folder.

I am convinced that it was the Internet that  necessitated cleaning technical data and key header information for two reasons: 1) Web reports forced the integration between systems as business users wanted data from multiple silo databases on one page. Often times than not, real-time integration could not be realized without cleaning the data first 2)  Reports on the web linked directly to databases exposed more “holes” and multiple “versions for the same data”;  it revealed how necessary it was to have only ONE VERSION of information, and that  had better be the truth.

The majors were further ahead but for many other E&P companies, Engineers were still integrating technical information manually, taking a day or more to get a complete view and understanding of their wells, excel was the tool moslty. Theoretically, with these new technologies, it should be possible to automate and instantaneously give a 360 degree-view of a well, field, basin and what have you. However, in practice it was a different story because of poor data quality.  Many companies started data cleaning projects, some efforts were massive, in tens of millions of dollars, and involved merging systems from many past acquisitions.

In the USA, in addition to the internet, the collapse of Enron in October 2001 and the Sarbanes–Oxley Act enacted in July 30, 2002, forced publicly traded oil and gas companies to document and get better transparency into operations and finances. Data management professionals were busy implementing their understanding of SOX in the USA. This required tightener definitions and processes around data.

Mid 2000s

By mid-2000s, many companies started looking into data governance. Sustaining data quality was now in the forefront.  The need for both sustainable quality data and data integration gave rise to Well Master Data Management initiatives. Projects on well hierarchy, data definitions, data standards, data processes and more were all evolving around reporting and data cleaning projects. Each company working on its own standards, sharing success stories from time to time.  Energetics, PPDM and DAMA organizations came in handy but not fully relied on.

Late 2000s

When working on sustaining data quality, one runs into the much-debated subject of who owns the data?  While for years, the IT department tried to lead the “data management” efforts, they were not fit to clean technical oil and gas data alone; they needed heavy support from the business. However the engineers and geoscientists did not feel it was their priority to clean “company-wide” data.

CIOs and CEOs started realizing that separating data from systems is a better proposition for E&P.  Data lives forever while systems come and go. We started seeing a movement towards a data management department, separate and independent from IT, but working close together. Few majors made this move in mid 2000s with good success stories others are started in late 2000s. First by having a Data Management Manager reporting to the CIO (and maybe dotted line to report to a business VP) then reporting directly to the business.

Who would staff a separate data management department?  You guessed it; resources came from both the business and IT.  In the past each department or asset had its own team of technical assistants “Techs” who would support their data needs (purchase, clean, load, massage…etc.) Now many companies are seeing a consolidation of “Techs” in one data management department supporting many departments.

Depending on how the DM department is run, this can be a powerful model if it is truly run as a service organization with the matching sense of urgency that E&P operations see. In my opinion, this could result in cheaper, faster and better data services for the company, and a more rewarding career path for those who are passionate about data.

Late 2008 and throughout 2009 the gas prices started to fall, more so in the USA than in other parts of the world. Shale Natural Gas has caught up with the demand and was exceeding it.  In April 2010, we woke up to witness one of the largest offshore oil spill disasters in history. A BP well, Macondo, exploded and was gushing oil.

For companies that put all their bets on gas fields or offshore fields, they did not have appetite for data management projects. For those well diversified or more focused on onshore liquids, data management projects were either full speed or business as usual.

 2010 to 2015 ….

Companies that had enjoyed the high oil prices since the 2007 started investing heavily in “digital” oilfields.  More than 20 years had passed since the majors started this initiative (I was on this type of project with Schlumberger for one of the majors back in 1998). But now it is more justifiable than ever. Technology prices have come down, systems capacities are up, network reliability is strong, wireless-connections are reasonably steady and more. All have come together like a prefect storm to resurrect the “smart” field initiatives like-never before. Even the small independents were now investing in this initiative. High oil prices were justifying the price tag (multiple millions of dollars) on these projects. A good part of these projects is in managing and integrating real time data steams and intelligent calculations.

Two more trends appeared in the first half of the 2010s:

  • Professionalizing the petroleum data management. Seemed like a natural progression now data management departments are in every company. The PPDM organization has a competency model that is worth looking into. Some of the majors have their own models that are tied to their HR structure. The goal is to reward a DM professional’s contribution to business’ assets. (Also please see my blog on MSc in Petroleum DM)
  • Larger companies are starting to experiment and harness the power of Big Data, and the integration of structured with unstructured data. Meta data and managing unstructured has become more important than ever.

Both trends have tremendous contributions that are yet to be fully harnessed.  The Big Data trend in particular is nudging data managers to start thinking of more sophisticated “analysis” than they did before .  Albeit one could argue that Technical Assistants that helped engineers with some analysis, were also nudging towards data analytics initiatives.

In December 2015, the oil price collapses more than 60% from its peak

But to my friends’ disappointment, standards are still being defined. Well hierarchy, while is seems simple to the business folks, getting it all automated and running smoothly across all types and locations of assets  will require the intervention of the UN.  With the data quality commotion some data management departments are a bit detached from the operations reality and take too long to deliver.

This concludes my series on the history of Petroleum Data Management. Please add your thoughts would love to hear your views.

For Data Nerds

  1. Data ownership has now come full circle, from the business to IT and back to business.
  2. The rise of Shale and Coal-bed Methane properties, fast evolution of field technologies are introducing new data needs. Data management systems and services need to stay nimble and agile. The old ways of taking years to come up with a usable system is too slow.
  3. Data cleaning projects are costly, especially when cleaning legacy data, so prioritizing and having a complete strategy that aligns with the business’ goals are key to success. Starting with well-header data is a very good start, aligning with what operations really need will require paying attention to many other data types, including mealtime measurements.
  4. When instituting governance programs, having a sustainable, agile and robust quality program is more important than temporarily patching problems based on a specific system.
  5. Tying data rules to business processes while starting from the wellspring of the data is prudent to sustainable solutions.
  6. Consider outsourcing all your legacy data cleanups if it takes resources away from supporting day to day business needs. Legacy data cleaning outsources to specialized companies will always be faster, cheaper and more accurate.
  7. Consider leveraging standardized data rules from organizations like PPDM instead of building them from scratch. Consider adding to the PPDM rules database as you define new ones. When rules are standardized data, sharing exchanging data becomes easier and cost effective.  

Part 2: Are we progressing? Oil & Gas Data Management Journey

In my previous blog, I looked back to the 1960s, 70s, and 80s, and how E&P technical data was generated and stored. For those three decades, data management was predominantly and virtually exclusively on paper. As I looked to the 90s, I found them packed with events that affected all areas of data value chain, from generation to consumption to archival.

Early 90s: Driving Productivity Forward

The early 90s continued one dominant theme from the late 1980s: the relentless drive for increased productivity throughout the business. This productivity focus coincided with three technological advancements that made their way into the industry. First of all, dropping costs of hardware with their growing capacity meant that computers became part of each office with meaningful scientific programs on them. Second, the increased capabilities of “networks” and “server/client” opened up new possibilities by centralizing and sharing one source of data. Third, proven success of relational databases and the SQL offered sophisticated ways to access and manipulate more data.

All this meant that, by the early 90s, engineers and the majority of geoscientists were able to do an increasing portion of their work on their own computers. At that time, the world of computer was divided into two; UNIX for G&G professionals and PC for the rest. Despite the divide of technologies, increases in productivity were tangible. Technology had proven itself useful and helpful to the cause, and was here to stay.

Petroleum Geoscience- and Engineering- specific software applications started springing up in the market like Texas wild flowers in March. Although some companies built seismic and log interpretation software back in the 70s using Cray super computers and on DEC mini computers, not many could afford an $800,000 computer (yes, one computer that is) with limited capacity. “I remember selling software on time share for CGG back in the 80s” my friend commented, “companies had to connect to expensive super computers on extremely slow connections” he adds.  So when the computer became affordable and with the right power for E&P technical applications, the software market flourished.

The industry was thirsty for software and absorbed all of what was produced on the market and then some; operators who could afford it created their own. The big service companies decided they were not going to miss out. Schlumberger acquired Geoquest in 1992 for its seismic data processing services and tools, then also acquired Finder, Eclipse and a long string of other applications.

The only problem with all these different software applications was that they existed standalone; each application had its own database and did not communicate with another. As a result, working on each hydrocarbon asset meant multiple data entry points or multiple reformatting and re-loading. This informational and collaborative disconnect between the different E&P applications was chipping away the very productivity and efficiency the industry was desperate to harness.

Nevertheless, the standardization of defining, capturing, storing and exchanging E&P data was starting to be of interest to many organizations. PPDM in Canada and later POSC in the USA (now Energetics) were formed in 1988 and 1990 respectively. PPDM’s mission at the time was focused on creating an upstream data model that could be utilized by different applications. POSC’s mission was broader; to develop a standardized E&P data model and data exchange standards.

Schlumberger had a solution for its own suite of applications; it offered both Geoframe and Finder as answers to the data mess with Finder being the master database that fed Geoframe with information, and Geoframe integrated the various software applications together.

Mid-90s: Making Connections

In the mid-90s, Halliburton acquired Landmark Graphics and unveiled the OpenWorks platform for its suites of applications in April 1997 at the AAPG. Their market positioning? Integrated reservoir management and a data management solutions. OpenWorks offered similar data integration to GeoFrame but with its own set of scientific software. Geoframe and OpenWorks would butt heads for years to come, both promoting their vision of data management and integrated workflows. It seemed that the larger companies were either a Schlumberger or Landmark shop.

In 1997, the Open Spirit Alliance funded by a consortium (Schlumberger, Shell and Chevron) was born and interoperability was its mission. PrismTech was to develop and market an application integration framework that any company could utilize, it was to be open. Open Spirit platform was officially launched at the SEG in 1998.

Late 90s: Big Industry Changes

Come the late 90s, another drop in oil prices combined with other macroeconomics appeared to trigger a surge in “mega” M&A activities starting with Exxon acquiring Mobil in 1998, BP acquiring Amoco in 1999, and then Conoco acquiring Philips in 2000, these mega acquisitions continued through early 2000s.

All this M&A in the 90s added complexity to what was already a complex technical dataflow environment.

For the data nerds

  • In the 90s, the industry rapidly evolved from hand-written scout tickets, and hand-drawn maps to electronic data.
  • The “E&P software spring” produced many silo databases. These databases often overlapped in what they stored creating multiple versions of the same data.
  • The IT department’s circle of influence was slowly but surely expanding to include managing E&P data. IT was building data systems, supporting them, uploading data to them and generating reports.
  • Engineers and Geoscientist still kept their own versions of data, but in MANY locations now. While hardcopies were the most trusted form (perceived to be the most reliable), technical data was also stored in disks, network drives, personal drives and in various applications’ databases and flat files. It compounded the data management problems of the years prior to computerization of processes.
  • Relational databases and SQL proved to be valuable to the industry. But it was expensive to support a variety of databases; many operators standardized and requested systems on Oracle (or SQLServer later).
  • Systems not on relational databases either faded away to the background or converted to relational databases that were accepted by operators.
  • Two standard data models emerged PPDM and POSC (now Energetics) and one data integration platform from the OpenSpirit (now part of the Tibco suite).
  • Geos and engineers validated and cleaned their own data (sometimes with the help of Geotechs or technical assistants) prior to their analyses.

 Stay tuned for the Millennium, and please add your own memories (and of course please correct me for what is not accurate ….)