Tag Archives: data management

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.

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.

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.  

Change Coming Our Way, Prepare Data Systems to Store Lateral’s Details.

Effectively, during the past decade, oil and gas companies have aimed their spotlight on efficiency. But should this efficiency be at the expense of data collection? Many companies are now realizing that it shouldn’t.

Consider the increasingly important re-fracturing effort.  It turns out, in at least one area, that only 45% of re-fracs were considered successful if the candidates were selected using production data alone.  However, if additional information (such as detailed completion, production, well integrity and reservoir characterization data) were also used a success rate of 80% was observed. See the snip below from the Society of Petroleum Engineer’s paper “SPE 134330” by M.C Vincent 2010).

Capture

Prepare data systems to store details, otherwise left in files.

Measurements while drilling (MWD), mud log – cuttings analysis and granular frac data are some of the data that can be collected without changing drilling or completion operations workflow and the achieved efficiency.  This information when acquired at the field will make its way to petrophysicists and engineers. Most likely it ends up in reports, folders and project databases.  Many companies do not think of this data storage beyond that.

We argue, however, to take advantage of this opportunity archival databases should also be expanded to store this information in a structured manner. This information should also funnel its way to various analytical tools. This practice will allow technical experts to dive straight into analyzing the wells  data instead of diverting a large portion of their time in looking for and piecing data together. Selecting the best re-frac candidates in a field will require the above well data and then some. Many companies are starting to study those opportunities.

Good data practices to consider

To maximize economic success from re-stimulation (or from first stimulation for that matter) consider these steps that are often overlooked:

  1. Prepare archival databases to specifically capture and retain data from lateral portions of wells. This data may include cuttings analysis, Mud log analysis, rock mechanics analysis, rock properties, granular frac data, and well integrity data.
  2. Don’t stop at archiving the data, but expose it to engineers and readily accessible to statistical and Artificial Intelligence tools. One of those tools is Tibco Spotfire.
  3. Integrate, integrate, integrate. Engineers depend on ALL data sources; internal, partners, third party, latest researches and media, to find new correlations and possibilities. Analytic platforms that can bring together a variety of data sources and types should be made available. Consider Big Data Platforms.
  4. Clean, complete and accurate data will integrate well. If you are not there yet, get a company that will clean data for you.

Quality and granular well data is the cornerstone to increasing re-frac success in horizontal wells and in other processes as well.  Collecting data and managing it well, even if you do not need it immediately, is an exercise of discipline but it is also a strategic decision that must be made and committed to from top down. Whether you are drilling to “flip” or you are developing for a long term. Data is your asset.

 

In-hand data, both internal and external, can be the difference between millions of dollars gained or millions of dollars lost

The Eagle Ford… Bakken… Permian… Montney… Booming plays with over 50 active operators each. Each operator brings its own development strategy and its own philosophy. While some operators appear successful in every unconventional play they touch, others always seem to come last to the party, or to miss the party altogether. Why?

 Information. With all things being equal (funding availability, access to geoscience and engineering expertise), one variable becomes timely access to quality information and understanding what the data is telling you, faster than the competition.

 “Few if any operators understand how (shale) behaves, why one fracture stage within a well produces 10 times more oil or gas than its neighbor, or how to find sweet spots to overcome inequity.”  Colorado School of Mines Rhonda Duey

 Over 60 operators in the Eagle Ford alone. Studying the strategy and philosophy of each operator in a play would, should, yield insight as to what works, what does not work and why? Landing depth, fracking parameters, lateral length, flow-back design, etc… All may matter, all may contribute to better production rates, better ultimate recoveries and better margins. And yes, each play really is unique.

 WHERE TO LOOK?

 A wealth of information from each operator is buried in shareholders’ presentations, their reported regulatory data, and published technical papers. Collecting relevant information and organizing it correctly will enable engineers and geo staff to find those insights. Today, engineers and geologists cannot fully take advantage of this information as it’s not readily consumable and their time is stretched as it is.

 We all agree, taking advantage of Shale plays is not only about efficiency, but it is also about being effective. The fastest and cheapest way to effectiveness is to build on what others have proven to work and avoid what is known not to work.

 Here are some thoughts on how to leverage external data sources to your advantage:

  • Understand the goal of the study from engineers and geoscientists. Optimized lateral completion? Optimized fracking? Reducing drilling costs? All of the above?
  • Implement “big data” technology with a clear vision of the output. This requires integration between data systems to correlate data from various external sources with various internal sources.
  • Not ready to invest in “big data” initiatives or don’t have the time? Outsource information curation (gathering and loading data) for focused studies.
  • Utilize data scientists and analytical tools to find trends in your data, then qualify findings with solid engineering and geoscience understanding.
  • Consider a consortium among operators to exchange key data otherwise not made available publicly. If all leases are leased in the play, then the competition among operators is over. Then shouldn’t play data be shared to maximize recovery from the reservoirs?
  • Build a culture of “complete understanding” by leveraging various sources of external data. 

Better Capital Allocation With A Rear-View Mirror – Look Back

In front of you are two choices: Tie up $100 million with low return or over spend by $50 million with no reliable return. Which option do you choose? Neither is acceptable.

“It seemed we were either tying up cash and missing on other opportunities, or overspending where we should not have in the first place,” said a former officer of a US independent. “We heard great stories at presentations from engineers and geoscientists as they were painting the picture to executives to fund their programs. But at the end of the year, the growth was never where we had expected it to be.”

Passing by poor investments through better allocation of capital greatly enhances company performance. To achieve this, executives needed a system to look back and evaluate what each asset team had predicted compared to the actual performance of the asset. They needed a look-back system where hindsight is always 20/20.

A look-back system is beneficial not only for better capital allocation, but also to identify and understand the reasons for low or high performance of an investment.

Implementing a look-back system is data intensive. The data needed, however, typically has already been collected and stored as part of everyday operations. For example most companies have an AFE system that captures predicted economics of well projects. All companies keep system(s) to capture production volumes and accounting data for both revenue and costs.  Data for evaluating an investment after-the-fact is already available – for the most part.  The reason executives did not have a look-back system was buried in their processes. In how each asset’s economic returns are calculated and allocated.

Here are few tips to consider when implementing a look-back system for an oil and gas company:

  • Start with the end. Identify the performance indicators (KPI) required to measure assets’ performance.
  • Standardize how economics are prepared by each asset team. Only then will you be able to compare apples to apples.
  • Allocate costs and revenue back to each well. Granularity matters and is key. With granularity, mistakes of lumping costs under a wrong category can be avoided and easily rectified.
  • Missing information for the KPI’s? Introduce processes to capture and enter data in company’s systems (historically this information may be in presentation slides and personal spreadsheets).
  • If well information is scattered across systems, data integration will be needed. Well, AFE, Production, Reserves, and Accounting data will need to be correlated.
  • Automate the generation of information to executives. Engineers and geoscientist should not have to prepare reports at the end of each month or quarter to management. Their time is FAR better spent making money and assets work harder for their investors.
  • Know it is a change to the culture. Leadership support must be behind the initiative and well communicated throughout the stake holders.

“Once we implemented a look-back system, we funded successful teams more and reduced the budget from under performing assets, then we utilized the freed money to grow. We were a better company all around” – Former Officer of a Large Independent.