Foundations, Volume 5 Issue 1 by PPDM Association

Foundations Journal of the Professional Petroleum Data Management Association

Print: ISSN 2368-7533 - Online: ISSN 2368-7541

Volume 5 | Issue 1

1st Place Foundations Photo Contest Winner; “Fall in Full Bloom” (Riyaz Husain)

Green Data Management A discussion on environmental data. (Page 4)

PLUS PHOTO CONTEST: This issue’s winners and how to enter (Page 16)

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Foundations Foundations: The Journal of the Professional Petroleum Data Management Association.

Table of Contents Volume 5 | Issue 1

COVER FEATURE

Green Data Management A Discussion On Environmental Data. By Kurt Hansen GUEST EDITORIALS

Data Management

DEPARTMENTS

Chief Executive Officer Trudy Curtis

An Accidental Career. By Jim Crompton

Senior Operations Coordinator Amanda Phillips

Education Opportunities 10

Senior Community Development Coordinator Elise Sommer

In Petroleum Data Management. By Sakthi Norton

Article Contributors/Authors Margaret Barron, Ann Clark, Mark Craig, Jim Crompton, Trudy Curtis, Kurt Hansen, Abhijeet Narvekar, Ellen West Nodwell, Sakthi Norton, PODS Association, Ali Sangster, Yogi Schulz Editorial Assistance Emma Bechtel, Beci Carrington, Jim Crompton, Dave Fisher Graphics & Illustrations Jasleen Virdi Graphic Design

The Oil & Gas Sector Is Changing

From Understanding To Prevention. By Trudy Curtis

This issue’s winners and how YOU can get your photo on the cover of Foundations.

SLC Corner

PODS

A Data Model Developed By Industry Experts.

Thank You To Our Volunteers

And We’ve Got To Keep Up With It. By Abhijeet Narvekar

Data Attenuation

Photo Contest

Featuring Five Great Volunteers.

FEATURES

Hands On With The PPDM Association Board Of 7 Directors

Upcoming Events, 31 Training and Certification Join PPDM at events and conferences around the world in 2018. Learn about upcoming CPDA Examination dates and training opportunities.

Data As A Product. By Ali Sangster

BOARD OF DIRECTORS Chair Robert Best Vice Chair Allan Huber Secretary Lesley Evans Treasurer Peter MacDougall Directors Allan Huber, Ali Sangster, Amii Bean, Christine Miesner, David Hood, Jeremy Eade, Lesley Evans, Paloma Urbano, Peter MacDougall, Robert Best, Shashank Panchangam, Trevor Hicks Head Office Suite 860, 736 8th Ave SW Calgary, AB T2P 1H4, Canada Email: info@ppdm.org Phone: 1-403-660-7817

Cross-Company Collaboration

PPDM’s PDC. By Ann Clark, Mark Craig, Tracy Heim & Margaret Barron

An Interview With Tammy Carter. By Jim Crompton

ABOUT PPDM The Professional Petroleum Data Management Association (PPDM) is the not for profit, global society that enables the development of professional data managers, engages them in community, and endorses a collective body of knowledge for data management across the oil and gas industry.

Publish Date: April 2018

Foundations | Vol 5, Issue 1 | 3

Cover Feature

Green Data Management

information for the LSA and RSA. Baseline information is presented and summarized in various ways including maps, graphs and temporal trends. Mathematical modelling is often used to interpret and summarize the baseline data. An example of this is exhaust plume dispersion models using pollutant emission rates, terrain topography, meteorological data and terrain surface characteristics. The figure below shows an example of modelled and plotted isopleths for hourly maximum SO2 concentrations (μg/m3) in ground-level ambient air in the Fort McMurray area - the square, triangular and diamond symbols represent emission sources as of 2010.

By Kurt J. Hansen, Green Inc

reen data” describes various types of data collected, processed and archived for environmental baseline characterization, environmental impact assessment, and compliance reporting. Green data is managed for the life cycle – and perhaps beyond - of an industrial development such as a natural gas processing plant or a bitumen processing plant with an associated oil sands mine. The data management approach is driven by the regulatory requirements for: • Environmental Impact Assessment (EIA) of a proposed development. • Compliance of an operating development with a permit. • Certification of completed reclamation of land and decommissioned facilities. Environmental protection legislation in many world jurisdictions has evolved and been modified to national conditions and priorities from legislation developed in the United States since the 1960s. The data acquisition and management activities are usually performed by an environmental service contractor. This article reflects the regulatory requirements in Canada but is generally relevant elsewhere. The

“G

author has also worked in Europe, Asia, the Middle East and South America.

ENVIRONMENTAL BASELINE DATA The data collected for Environmental Impact Assessment (EIA) is of many types and origins. They are required to delineate the baseline conditions in the local and regional study areas (LSA of about 10-20 km2 and RSA of thousands of km2) of a proposed development. LSA point samples of soil, surface water and groundwater are complemented by field observations and measurements for points or small polygons. Some of the data types are soil horizons and depths; water temperature, pH and electric conductivity; stream water flow rate; and groundwater depth. The samples are analyzed for dissolved minerals, soil leachable salts, etc. Additional data is acquired for vegetation and wildlife. Data origins of a point nature in the RSA are meteorological, air quality, stream flow and water quality. Long-term historical measurements by government agencies may be available. RSA data includes topography, soil and vegetation types. Green data is interpreted and processed into environmental baseline

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COMPLIANCE DATA Environmental compliance data for an operating development is generally the same as for a proposed development. The conditions of the operating permit require monitoring and reporting on hourly through annual cycles, and the data could be audited at any time. Examples of data types and management are: • Exhaust stack continuous emission monitoring (CEM) data (e.g. air pollutant sulphur dioxide) to show monthly compliance with maximum allowable limits. • Fugitive grab type emission surveys of toxic air pollutants and greenhouse gases for annual reporting. The fugitive sources include waste

Feature

•

water, tailings ponds and leaking equipment at a processing plant. Periodic fuel grab sample analysis and measured fuel consumption required to calculate annual GHG emissions. Ground level ambient air quality and meteorological data that are measured continuously to ascertain compliance with ambient air quality criteria. The data is archived for years for potential government data audits. Effluent waste water discharge data reported monthly to show compliance with discharge limits. Groundwater and stream surface water data that are acquired from quarterly samplings and chemical analyses at fixed LSA locations to annually report trends of impact on water quality, flow and groundwater level. The data is processed into spatial isopleth maps and temporal trend graphs, including data from previous years. They are archived for potential government audit. The data archive would include certified laboratory analysis sheets. Soil, vegetation and wildlife observational data for the LSA that are acquired annually to spatially and temporally augment the initially collected EIA data. The field observational data is processed and interpreted into summary GIS maps and summary graphs of temporal trends and statistical data, for annual reporting and for the longterm environmental Conservation & Reclamation (C&R) of the lands.

locations for later placement and reclamation use in mined-out areas. • Seeded areas of vegetation reclamation are surveyed annually. The progress and success of reclamation is reported annually as data, GIS maps, graphs, etc. If the reclamation is deemed successful, a reclamation certificate will be issued.

SUMMARY Environmental data (green data) is collected, processed and archived for decades and managed for the purposes of environmental protection, compliance, auditing and long-term land reclamation planning. The data nature and file formats are of many types (sample, chemical, analytical, observational, continuously measured, grab sampled, and processed model or GIS input and output data). An environmental data archive at the end of the approximately 50-year life cycle of a development would be in the order of terra

bytes. What gets measured gets managed. During the 1970s there were about 50 qualified environmental service companies in Canada. Today, there are thousands of them – very qualified and specialized in specific environmental data collection, interpretation, and data management. This evolution is due to the ever expanded legislative requirements regarding environmental protection and reporting since the 1960s. About the Author Kurt is of Danish birth and education. He acquired his M.Sc. degree in Civil and Environmental Engineering at the Technical University of Denmark (TUD) in 1974. He immigrated to Alberta in 1974 and has since worked in the environmental services sector in Alberta, Canada and internationally. He is a registered Professional Engineer (P. Eng.) in Alberta since 1977. He founded his own company, Green Inc., in 2006.

On The Lighter Side… Your data management skills are lacking. On the other hand, it would be great to hire someone who actually knows what a Kelly Bushing elevation is.

RECLAMATION DATA Reclamation data is acquired, processed and archived throughout the development, EIA and operational phases. Examples are: • Original EIA phase data on top soil and sub-soils depths and characteristics get updated based on more intensive annual surveys during the operational phase. • Top and sub soils are salvaged and stock-piled, and inventoried, at various peripheral mining area

Humour courtesy of Yogi Schulz, Corvelle Consulting

Foundations | Vol 5, Issue 1 | 5

Guest Editorial

Data Management: The Accidental Career By Jim Crompton, Reflections Data Consulting

ecently I was asked by PPDM to write an article on the history of data management in oil and gas for their Foundations magazine. I have been contributing to this journal for several years as a regular columnist but this request was different. As I began to write the article, I had an idea to try and get the “voice” of several industry veterans into the article so I put together a short “mini-interview” by email and sent it to a couple of dozen folks that I have run into during my career. Usually any request by email gets a pretty low response rate but I got lucky this time and many people answered the request. I added those personal insights to the article that I sent to PPDM. The three questions that I asked in my “mini-interview” were 1) how did you get into the data management business, 2) what was it like when you got involved and 3) how do you see the future of data management. One thing that stood out to me from the responses was almost everyone got into the data management business by some career accident, not by intent or by academic preparation. The diversity of backgrounds of the responders before their accidental introduction to data management was quite impressive. Many came from a traditional IT background where programming or data base administrator skills led to a data management position and later to a full career focus. Seismic data loading was another feeder pool for ORD EM E P RO GR

future data managers, as were engineering and operations support technicians, who became the go to data guy or gal for an asset team. There were also former earth scientists and petroleum engineers who made the fatal career mistake of going to senior management to complain about the state of data quality or data access, and for their trouble were given the responsibility to do something about it. However the individual came to data management, their career development was informal. Training was found whenever a challenge presented itself and the source of expertise varied from formal technical courses, to informal advisors but mostly provided by on-the-job experiences.

These veterans are mostly self-taught, self-motivated and self-directed. No wonder standards are hard to apply. An objective assessment of a company’s data management capability probably will show that the staff is quite mature (I really mean old), with few young professionals in the talent pipeline wanting to pursue a data management career. In many organizations, data management has little career ladder recognition (although a few organizations are trying to develop data management as a professional competency, remember: self-taught). Few of the staff have earned data management certification as their performance management systems doesn’t really recognize this step (remember self-motivated). The

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technologies that are routinely used are several versions behind the current release and often just a set of individual scripts and tools that the data management staff have cobbled together to try and get things done (remember: self-directed). The brave and usually unrewarded data managers are fighting a difficult battle. The IT department is more technology than information. The high fliers know that career recognition comes from areas other than data management. The business staff really don’t want to get that involved themselves but do appreciate the technicians that take care of their data requirements (my data gal/ guy). Functional and geographic silos are strong. Legacy and shadow data systems are well entrenched. Funding is low. Governance is spotty. Yet the pressure of Big Data Analytics is growing from the top and the data management staff is squeezed in the middle. The problem with data management is not a new one. Companies have been working on this for years. It is not even a technology one. Most companies have a lot of effective tools already and know a lot about emerging ones. The challenge is one of culture and priorities. The real

Feature

question is not of the importance of data management, but one of is the business culture the right place to grow the required data management profession? Management supports “getting the job done” but for developing and maintaining a strong data foundation the interest is more like “let someone else do it.” If the IT department is delegated this task but not supported well enough to build a strong data management capability, companies find themselves caught between a rock (the demand for good data to feed analytics) and a hard place (the realities of budget and resource constraints).

For the next generation of data managers, the path may be smoother and better defined.

Several professional organizations are building a professional discipline and certifications around data management. Examples include DAMA, PPDM, and CDA. Several universities (including the University of Houston, the University of Tulsa and the University of Aberdeen) have (or are planning to) put together degree programs for oil and gas data management. The CIO needs to ask if in their company, is it worth the effort for data managers to earn these certifications or do they need to recruit from the new specialized academic programs? Will these qualifications help new data managers with their career development? Are strong performers encouraged to focus on data management or is limited career paths in data management leading them to try other jobs to get promotions faster? Companies need to ask themselves how they are best set up to enhance this

professional discipline or if they can’t, what trusted partnerships do they need to have to provide this critical capability. My message here is that we need to listen to history. If we don’t like the position that we are in with a poor data foundation for analytics, automation or optimization, take a hard look at what investments (if any) are being made in the data management staff. Recruit and development intentionally. Support and encourage deliberately. Data management is too important to be left to accidental careers and informal development paths. About the Author Jim retired from Chevron in 2013 after almost 37 years. After retiring, Jim established Reflections Data Consulting LLC to continue his work in the area of data management, standards and analytics for the exploration and production industry.

Hands On With The PPDM Association Board of Directors ORD EM E P RO GR

By Ali Sangster, Drillinginfo

DATA AS A PRODUCT. WHAT DOES THAT MEAN? The large volumes of data that are generated in the Oil and Gas industry daily are nothing new. Operators are utilizing technology to better manage and understand their assets and industry standards for well data management have become widely accepted. Oil and Gas data is still extremely complex. It

is multidisciplinary, relational, multidimensional and time series. It also comes in different formats and real time, and different parts of the business commonly have their own software and tools that can complicate data quality and consistency. PPDM has helped drive industry standards and continues to serve as a professional society and advocate. What is a Well and The Business Life Cycle of the Well have

had a massive impact in the E&P space because not only has it served as a logical model for Oil and Gas data but helps bring together the other parts of the businesses within the organization…accounting, land, geology, geophysics, engineering, M&A…creating industry standards. With the creation of industry standards, Oil and Gas operators and companies can take control of their data to create valuable

Foundations | Vol 5, Issue 1 | 7

information that enables faster decision making and allows them to not only compete but aim to dominate in their space. Expectations are not always reality when it comes to understanding, evaluating or even pricing anything outside of their known space, and the demand, requirements and need for information outside the known proprietary assets and interests are imperative. Competitor Analysis is a critical component in determining who is doing what and where, best practices and development of new opportunities. US Oil and Gas regulatory agencies have filing processes in place to track well data but reporting requirements do not meet business requirements. Regulatory data is form based and every US State agency has their own filing system that can vary significantly. It can be extremely time consuming to dig though regulatory filings searching for answers that are not easily identified or buried in forms. From the vendor perspective, data assets can be transformed into a Data Product, and can fill in the gaps and meet the needs and requirements of the business.

WHAT IS A DATA PRODUCT? A data product consists of three main components: reported

data, manufactured data and derived data that meets industry standards and is ready for use. 1. Reported Data and Collection/ Acquisition Data can be acquired from multiple sources, but mainly Oil and Gas data collection and acquisition is from regulatory agencies. Each regulatory agency has its own governance and reporting variances. Production Reporting is a prime example of reporting variances by region: • Production is reported differently by region and on a lease level, well level, completion/formation level, and may be commingled and unitized depending on the agency. • First Production Date in one region means the date a well first reported production, while First Production Date in another region means the date the well was first physically capable of producing. Same term, but two different meanings. Permitting, Drilling and Completion reporting requirements also vary by region and vendors have to understand not only what they are collecting, but what it means by reporting entity in order to join the right pieces together to tell an accurate data story.

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2. Data Manufacturing Reported regulatory data is the source and provides the pieces of the puzzle in a multitude of formats and schemas, but the Data Manufacturing process ties the correct Well Life Cycle and segments (Planning, Drilling, Completion, Production and Abandonment) to the correct component, activity and event in a consistent format that is interoperable and easy to consume. Most regulatory agencies only require reporting to the Well Origin/Surface level, but unconventional drilling has increased the need for wellbore identification and reporting at the wellbore level. Data Manufacturing is required for reported data to serve its purpose. Data is tied together to be easily identified. A few examples of what can be identified when reported data is tied together and made easily accessible: • Identify and track a well through its regulatory and physical life. • Tie the correct permit to the correct wellbore/wellbores. • Identify stacked laterals, multi laterals and sidetracks. • Tie the correct Directional/Horizontal Survey and log to the correct wellbore. • Tie the correct completion to the correct wellbore. • Recompletion identification and technique. • Track operator history and trends. Data Manufacturing is necessary to solidify the foundation that a data product is built upon to meet and exceed the needs of the Oil and Gas industry. 3. Derived Data There are common misconceptions regarding Oil and Gas reporting requirements regarding what has to be reported and what doesn’t. Many data attributes required for detailed analytics and interpretation are not actually reported, they are derived. Data Product Derivatives are data attributes that are generated from mathematical calculations, algorithms, and/or logic. Data Derivatives add significant value to a Data Product. A few examples of derived data attributes required for multidisciplinary workflows and analysis include:

• Lateral Lengths - calculated from Direction Surveys, not reported. • Footage Calls - often converted to Lat/Long. • Operator Aliasing. • Unit of Measure Normalization. • Total Proppant and Total Fluid can be derived from stimulation data. • Perforated Interval Lengths. Derived Data adds significant value to a Data Product by transforming reported data attributes to usable values that are ready for analytics and interpretation. An Oil and Gas Data Product can be utilized in every market within the Oil and Gas industry for different purposes and to solve different problems. When a Data Product is built on a Well Business Life Cycle (Planning, Drilling, Completion, Production and Abandonment) a solid foundation is established and can support multidisciplinary workflows in order to solve problems and generate insights. Common uses of a Data Product by discipline include, but are not limited to: Landman, Lease Order Analyst, Division Order Analyst: • Leasing data and lease polygons visually see the lease outlines, who is leasing and where, what leases are coming up for expiration, what leases are being held by production and search for open acreage. • Operator history for all wells nationwide. Ownership interest, royalty interest and mineral interest. • Track how many wells are on a lease and identify the last well on a lease. • Well records and filings - relational data tied to API numbers operator history, well events, completions, permits, etc. • Permitting activity - who is doing what and where. • Determine what brokers are leasing for operators. • Evaluate acreage in a play. • Identify lease names and lease numbers for historic production. • Rig Data - Rig locations, operator, and driller; evaluate active rigs by Play.

Geologist/Geophysicist: • Reservoir and field analysis production and trends, geological characteristics by depth and conception, not just name. • Nationwide production with first production, peaks, declines, cums, and averages. • Analyze and predict production by operator, field, reservoir, and lease. • Well economics - time between spud date and completion for average drilling days. • Ability to sum production by field and reservoir, operator, lease, etc. Create an “average” well to generate type wells and type curves. • Directional surveys, well logs, and completion data at a wellbore level. • Trending and Play Characteristics - best practices. Engineers and Analyst: • Frac Analysis and Completion/ Recompletion effectiveness with production validation. • Generate Type curves for various plays, operators, fields, reservoirs, and wells. • Recompletion vs. Restimulation evaluation and PDP analysis. • Well economics and forecastinghow many days to drill a well, how many rigs running in a play, spud date vs. completion date, evaluate recompletion opportunities in mature fields. • Rig trend analysis for emerging play identification. • Evaluate Historical Production and Production over timedecline curve analysis. • Offset wells and production of offset wells to determine what other operators are doing and how they are doing it to maximize production. • Completion design and details to evaluate competitor wellbore design and efficiency. • Operator benchmarking for partnership and joint operating opportunities.

Business Development/New Ventures/ Mergers and Acquisitions: • Competitor Analysis - who is doing what and where? • Evaluate a play - top operator, reservoirs, top producing fields, oil vs. gas, completion technique and frac type identification. • Who owns what acreage and where? What do they own and how much? • Offset production around asset evaluation. • Permitting - activity monitoring, tracking and identification of new exploration areas. • Data Product interoperability for spatial analysis - bubble maps, operator maps, product maps, drill types. Mapping to show permitting activity by operator, who owns the acreage, is there current production or is the area HBP. • Pipelines - point of sale and product. • Rigs - Activity and rigs running in a play. • Production and forecasting - cums by year - 6 and 12-month basis, highest IPs, highest cums, depth of production. Many more ways to format production for different types of analytics and forecasts. • Wildcat permit identification in new exploration areas. In conclusion, reported regulatory data is collected, data is manufactured, and data is derived. It is the collection, manufacturing and derivation that create a Data Product. It is the Data Product that fills the gap between what Oil and Gas operators and companies know about themselves vs. what they know about each other. Also what they can learn from each other. About the Author Ali Sangster has been with Drillinginfo for almost 10 years, serving as Director of US E&P Data.

Data Managers Dream: regulators agree on rules for data submission, using the PPDM Rules Library. See the PPDM Regulatory Data Standards Committee.

Foundations | Vol 5, Issue 1 | 9

Guest Editorial

Education Opportunities in Petroleum Data Management By Sakthi Norton, Common Data Access ORD EM E P RO GR

o you have an academic qualification in Petroleum Data Management (PDM), and if not, did you know you can study and obtain one? Two years ago, there was no way for data managers to gain any sort of accredited education and qualification in their discipline, but as of September this year there will be a choice of three courses available: one at Graduate Certificate1 level (Robert Gordon University in Aberdeen, UK) and two at Master’s level (University of Aberdeen, UK and IFP School in Paris, France). You may now be thinking that there are plenty of PDM training courses in the industry, so what’s the difference? An academic course in this subject brings all the benefits of higher education, such as gaining in-depth knowledge that has been developed to rigorous and consistent academic standards, developing critical evaluation skills and independent research and reporting skills, and achieving internationally recognised qualifications such as a Master’s degree, all of which have a direct impact on professional development and career progression. It is important to note that education and training are complementary. Essentially, one learns about core concepts and principles through education, and through training learns to use various tools and methodologies to apply those

principles. Industry certifications such as the PPDM Certified Petroleum Data Analyst are similarly complementary, and demonstrate ongoing refreshing of core knowledge and continuous professional development beyond education. My own background is in the social sciences, and when I completed my Information Management degree I thought I was done with studying. But a few years down the line I found myself in the Oil and Gas industry, in a PDM role, with just on-the-job instruction and experience in the specific domains I was working in. This was certainly valuable, but I still felt adrift; unsure how to articulate why the activities I did were important to the business, unsure if what I was doing was always the right thing, and unsure how I’d progress in my career – a situation I’m sure many data managers can relate to. Later, as part of my role in Common Data Access (CDA), I became involved in industry professionalisation initiatives and began supporting the development of PDM education at universities, initially the online Graduate Certificate at Robert Gordon University. I saw first-hand how so many organisations across the industry were keen to advance this discipline, and to contribute their time, effort, and internal materials to help bring context to the theory – the project to develop this course even won an industry award

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for online education development2. Naturally, I was very pleased to be given the opportunity to study the course, which I began in September 2016. The experience was certainly challenging, but entirely rewarding. What I really appreciate is seeing the ‘big picture’ in a structured way – understanding the full PDM lifecycle alongside the Exploration and Production asset lifecycle. This has given me insight to when and how different data types relate to business processes and PDM activities, and enabled me to expand my thinking and apply concepts such as information governance and information service management more effectively. Equally, sometimes what I learnt confirmed what I already knew or practiced, and has served to build my own confidence in my competence as a data manager. The coursework assessments were not easy, but they really brought the taught concepts to life and made that connection with the ‘real-world’ industry perspective. They also reinforced the need to be critical and discerning when evaluating any source material, whether in the academic setting or in my own analysis and reporting at work. The other highlight was interacting with a cohort of my peers – fellow data managers around the world, from operators, regulators, and the supply chain, and at various levels of experience.

We gained different perspectives and learnt from each other during the course, and will no doubt continue to support each other professionally over the course of our careers. Some of us have been fortunate to keep in touch not just online but at industry events as well. So, eight years into my PDM career, I have much more to learn, but now with a structured foundation to build upon. If you too are looking to gain PDM knowledge, develop your current professional experience, and earn a recognised qualification in this discipline, then look no further: education opportunities now exist, whether you wish to study online while working anywhere in the

world, or you wish to take time out and study full time on-campus in the UK or France. CDA continues to work with the industry and other universities, with the aim of Masterâ&#x20AC;&#x2122;s level courses also launching in the USA over the next year, and indeed we hope to see more courses emerging worldwide as the academic and professional communities in PDM become more established. Further information on PDM education: Graduate Certificate at Robert Gordon University: www.rgu.ac.uk/datamanagement MSc at University of Aberdeen: www.abdn.ac.uk/pgt/pdm Specialized Masters at the IFP

School: http://www.ifp-school. com/jcms/r_17842/en/petroleumdata-management FOOTNOTES 1

The Graduate Certificate award is at Level 9 of the Scottish Credit and Qualifications Framework, equivalent to Year 3 of a four-year Scottish undergraduate degree. See www.scqf.org.uk/ interactive-framework/ 2 Best eLearning/Online Education at the 2017 UK Association Awards. See http://www. associationawards.org/ukAwardsWinners2017

About the Author Sakthi Norton is a Data Manager at CDA, and currently leads the industry project supporting delivery of the MSc Petroleum Data Management at the University of Aberdeen.

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Foundations | Vol 5, Issue 1 | 11

Guest Editorial

The Oil and Gas Sector is Changing, And We’ve Got to Keep Up With It By Abhijeet Narvekar, The FerVID Group

ata Management is vital to the Oil and Gas industry’s livelihood. It is what keeps the industry running efficiently and successfully. And yet, outside of cities that focus on Oil and Gas production, it remains one of the most obscure sectors of the industry, garnering less interest than its data related counterparts. The lack of interest in Data Management, stems from a few factors: the low interest in moving to Houston in general, the state of the industry and the ambiguities about pursuing a long-term career in geotechnical data management. ORD EM E P RO GR

HOUSTON, THE CITY Those of us who already live in Houston are enlightened to the qualities that make it one of the best cities to reside in. The city offers diversity and its people come together in difficult times to show what humanity at its finest looks like. However, for people who live outside of Houston and rely on a Google search to paint the city’s picture, it can appear as quite a grim place to live, especially when looking at the most highlighted news stories featuring an Oil and Gas industry crash and a devastating hurricane. While every city has its advantages and disadvantages, Houston often fails at marketing its advantages and therefore doesn’t appeal to newcomers as much as other American cities do.

DATA MANAGEMENT, THE STATE OF THE INDUSTRY AS IT PERTAINS TO OIL AND GAS When oil was first discovered, there wasn’t an immediate need to gather data. The commercial value of just digging a hole, drilling and striking was enough to overlook the importance of collecting data. Later, as geologists started to apply art and science to make predictions, data became vital to the field. Even when the price for oil is high and companies are making an immense amount of money, drilling engineers still overlook the use of data and rely mostly on their gut feelings and experiences alone to make decisions on the field. Only when the prices of oil plummeted in the last few years has data management proven to be essential to the survival of the oil industry. Companies started slowing their drilling down, allocating their budgets to better their processes and inventing new tools to optimize oil production. However, unlike other industries that rely on data management, the data that oil and gas industries use can be complicated. For example, Oil and Gas data does not tend to be structured. Most of it is gathered using field sensors and tools. This data usually has metadata in the header while the rest of the values are comma separated. Data scientists would have initial challenges to understand this data and would need to apply different algorithms to find trends.

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Perhaps the biggest challenge that we face with Oil and Gas data management is having to overcome antiquated practices that Oil and Gas companies still often operate with today. Very few companies share their data out in the cloud. This means that the majority of data is sealed within the oil and gas companies, halting the adoption of cloud-based technology and slowing down technological advances that could be made in the field. This closes the door for outsiders to understand the industry and get involved.

THE PURSUIT OF A CAREER IN GEOTECHNICAL DATA MANAGEMENT We at the Fervid Group performed a quick experiment. We opened up a search engine and typed in ‘data manager.’ The following results are what our searches returned: • Data Analyst. • Data Collector. • Data Base Administrator (DBA). • Workflow Analyst. • Business Data Manager. • IT Manager or Chief Information Officer • Clinical Data Manager. The problem with the image that these search results portray is that they all point to generic Information Technology. Going by these results alone, many will walk away thinking that data management requires a deep knowledge of databases and DBA. On the

? ?

DATA MANAGEMENT

Data Management is vital to the oil and gas industries’ livelihood. It is what keeps the industry running eﬃciently and successfully. BUT... It remains one of the most obscure sectors of the industry. WHY?

Our City

Houston enjoys a diverse community, low living costs and a real growth potential. However, the city has seen some recent setbacks, including an oil and gas industry crash and a devastating hurricane.

Our Industry

contrary, the most successful people in data come from business backgrounds and not information technology backgrounds. These people have a clear understanding of how data flows, who consumes it, how it is consumed and what the results that come from it mean. We performed another quick search experiment. This time we typed in ‘data management jobs.’ Of the 25,000 jobs that came up, only 94 jobs were in Oil and Gas. The following are some of the titles that came up in our search: • Records Management Analyst. • Business Intelligence and Analytics Supervisor. • Enterprise Master Data Manager. • Information & Data Governance Specialist. • Subsurface Well Data Analyst. • Data Scientist. • Data Processor. • Enterprise Data Mgmt. Manager. • DBA Analyst. • Engineering Technician. • Data Analytics Statistician. • Big Data Manager. • Decision Center Business Analyst / Data Engineer Data Entry Analyst. • Energy Intelligence Analyst. • Geoscience Technologist. In contrast, below is a list of the number of jobs that showed up for other industries that rely on data management: • 3,200 Jobs in Government. • 3,119 Jobs in Healthcare. • 3,000 Jobs in Insurance. • 2,316 Jobs in Education. • 1,800 Jobs in Manufacturing. • 1,172 Jobs in Media and Communications.

• 1,000 Jobs in Retail. • 1,000 Jobs in Transportation. • 921 Jobs in Banking. When our company, The Fervid Group, made a recruitment effort for a Retained AI Technical Executive, we had to reach out to people outside of Houston. Most of the candidates we spoke to had no knowledge of the tech level that the Oil and Gas industries operate at. We had to educate them about how AI may be applied in this industry. Most had not even heard of the biggest companies that exist in the Oil and Gas industries. They simply did not understand how AI was of importance to these companies. Once we explained it to them, they slowly started gaining interest. However, another problem arose. As only a few companies have a rigorous approach to governing data management, there has never been an articulated career path carved out for people who would want to pursue data management within the Oil and Gas industries. What happens once a specific project is completed? Does the team have a plan for what new hire would be doing next within the company? On the contrary, if we look into those who pursue data management as it pertains to Information Technology, we see that there is a linear career path where someone can move from being a Data Analyst, to a Workflow Analyst, to an Application Workflow Manager, and eventually to a Chief Information Officer. A similar track can be applicable to Oil and Gas Data Management. Ideally, someone who specializes in data tech would be able to move forward from being a Business Data Steward to a

The biggest challenge we face with oil and gas data management is having to overcome antiquated practices: Resorting to old techniques instead of reliable data for predictions

Companies overprotecting their data

Slow adoption of cloud based technology

Closing the door for newcomers to enter the ﬁeld

Careers

Of the 25,000 jobs that show up in a search for ‘Data Management Jobs’ ONLY 94 JOBS WERE IN OIL AND GAS In contrast, there were: 3119 jobs out of 25000 jobs for "data management" healthcare

2316 in Education

921 jobs – banking

1800 in Manufacturing

1172 in Media and Comm

3200 in Government

1000 in Retail

3000 in Insurance

1000 in Transportation

Currently, there is no articulated linear career path for those who pursue data management in oil and gas. In IT fields, the path is straight – one example: Data Analyst -> Workflow Analyst -> Application Workflow Manager -> Chief Information Officer Ideally, there would be a straight path in data management fields – one example: Data Tech -> Business Data Steward -> Function Data Steward -> Data Owner This isn’t always the case right now.

So…

Foundations | Vol 5, Issue 1 | 13

MENT

gas industry

WHAT ARE THE SOLUTIONS?

sectors

• Publish case studies on how science is being applied

• Start creating our own Technical Professionals

working with large E&P and operating companies

• Encourage current data managers and

companies who are advanced to communicate their accomplishments to others

ts and some crash

• Encourage companies to give recent graduates a chance to start from the bottom

• Hire data scientists in order to attract new engineers to enter the ﬁeld

• Create a program that allows recent graduates to shadow experienced professionals

• Allow visa opportunities for those who would like to pursue this career

data ed

• Fund petroleum engineers who would do the work when trained

ad of

or for enter

Data OIL

ion

cturing

ment

nce

path d

Function Data Steward, and eventually, to becoming a Data owner, gaining enough technical and organizational responsibilities along the path. But from the outside looking in, this is not at all clear. Most searches will showcase geotechnical data management as a less linear career path, where it is harder to move up. And these searches render truths about the industry. There is a serious lack of structure in regard to data management in most Oil and Gas companies. Not only does this put off people from entering the field, but it also means that those who specialize in data management are not stabilized in a position, causing a loss of data wisdom to the organization.

SO, HOW CAN THESE ISSUES BE RESOLVED? At our company, we have worked to understand the marketplace and to better define the roles of those who are interested in data management within the Oil and Gas industry.

As a community, we need to campaign and highlight Houston as one of the best places to raise a family. PPDM is already doing its part by working with universities to start a new track for Data Management. However, we can make efforts to marketing this growth to those outside of the industry. Current Data Managers and companies who are advanced in Data Management need to communicate their accomplishments in order to encourage others. Companies need to give recent graduates with degrees in data management related to Oil and Gas a chance to start from the bottom. There are Petroleum Engineers, but they lack IT skills. This should be taken into account by universities creating the program. There are several companies who are hiring Data Scientists. This is a brilliant way to attract and train new engineers. However, we need to publish case studies on how the science is being applied. Cybersecurity is gaining importance. This is an area where data can be applied. We could potentially start creating our own Technical Professionals working with large E&P and operating companies. We would only need experts to train the rest. We can recruit and bring in people from overseas who are eager to pursue careers in Data Management. We need to allow visa opportunities for Petroleum Engineers who would like to pursue this career track but need to be funded to start training them. Create a program that allows recent graduates to shadow experienced professionals. Our company has already seen success in doing this. About the Author Abhijeet Narvekar has experience in the Upstream Oil & Gas industry with companies like Schlumberger, Petris and has worked in various roles, gaining invaluable domain expertise in the Data Management domain.

Data

14 | Journal of the Professional Petroleum Data Management Association

Imagine your article here! Do you have something to share or to educate the Data Management Community? Be an author in Foundations. Submit your abstract to foundations@ppdm.org.

Data management professionals provide safe, reliable, competitive, and ever improving energy solutions.

Benefits are realized when trusted data and information can be reliably and swiftly accessed for decision making.

Photo contest

Foundations photo contest

“THE ROARING DATA MONSTER” BY HASMIK BELICH 2nd Place in the Volume 5, Issue 1 Foundations Photo Contest “Exhausted Lion Yawning After Roaring In the Fort Worth Zoo” – December 26, 2015 Hasmik Belich has more than 10 years of experience in Oil and Gas industry data management, information technology and business intelligence systems. She is currently the Data Manager at RigData and lives in the cultural district of Fort Worth, Texas, with her husband and two beautiful children. Photography is her newly discovered passion.

16 | Journal of the Professional Petroleum Data Management Association

Photo contest

On the cover:

“FALL IN FULL BLOOM” BY RIYAZ HUSAIN 1st Place in the Volume 5, Issue 1 Foundations Photo Contest

“Life Is Beautiful – Golden Autumn, Nose Hill Park, Calgary.” – September 27, 2015 Riyaz Husain is a Subsurface data management professional with 20+ years in the upstream Oil and Gas ventures.

Enter your favourite photos online at photocontest.ppdm.org for a chance to be featured on the cover of our next issue of Foundations!

Foundations | Vol 5, Issue 1 | 17

Industry News

CROSS-COMPANY COLLABORATION: THE PPDM PROFESSIONAL DEVELOPMENT COMMITTEE By Ann Clark, Ellen West Nodwell, Mark Craig & Margaret Barron, PPDM’s Professional Development Committee This paper and presentation are dedicated to the memory of Arthur Boykiw, who passed away in January 2017. Art was one of the early pioneers in petroleum data management and served as Chairman of the PPDM Board of Directors between 1996 and mid-2009. In 2008, Art oversaw the change and was instrumental in the organization’s progression from an organization oriented towards the PPDM data model, to an organization focused on supporting the petroleum data management profession.

INTRODUCTION The Professional Development Committee (PDC) of The Professional Petroleum Data Management (PPDM) Association has been working hard to establish a professional path forward for petroleum data managers. Multiple initiatives currently underway support this goal. The following paper was presented at the 2017 PNEC Conference. The message and content are even more relevant today, reflecting the importance of professionalization for our existing employees and future generations entering the field.

ABSTRACT Professional development is essential to establishing and maintaining a standard of excellence within a professional discipline. Professional development encompasses the needs of individual data managers, companies who employ data managers, supervisors, managers and Human Resources groups. In 2015, the PPDM Association created the member-led Professional Development Committee to raise awareness, coordinate professional development activities and support opportunities to members within the petroleum data management discipline to help guide and advance the profession itself. This is a standing committee of

the PPDM Association (PPDM, 2015). Currently, the committee has several focus areas: 1. Centralized catalog of qualified educational and training opportunities. 2. Job families, titles and descriptions. 3. Career path recommendations. 4. Compensation surveys (salaries and benefits). 5. Additional competency specifications. 6. Identify and support initiatives to develop curriculum, sample data, case studies and other support materials for training and education.

PROBLEM STATEMENT & OPPORTUNITY “Poor data management is bad for business; good data management supports success” (Fleming, 2013). In the petroleum industry, data is closely aligned with highly specialized business activities that require understanding interactions of several disciplines through a multiplestreamed business lifecycle. The data management profession was born out of practical need; and as records management became information management, jobs morphed (West Nodwell, 2016). This evolution increased rapidly with the move from paper and analog to digital, and the profession became more sophisticated. Digital technologies and concepts, such as security and big data, impact methodologies and best practices. As data in the oil patch continues to diversify and grow, the petroleum data management professional likewise must adapt and continue to train and to be educated as to how data types integrate into the existing body of information that describes the industry from its birth to present times. This professional discipline, by necessity, has been enabled and nurtured by the PPDM Association, which was born from rebranding and refocusing

18 | Journal of the Professional Petroleum Data Management Association

the earlier form of the Association focused on data modeling. This re-branding occurred under the governance and decisions of PPDM’s Board of Directors and was enacted by a vote of its membership at the 2008 Annual General Meeting. The PPDM Professional Development Committee’s current concept of a data manager’s professional purpose is “to develop and steward an environment where data can remain available and accessible to all stakeholders and processes through the long and complex E&P life cycle without being lost or corrupted” (PPDM, 2017). Competencies currently in scope of PPDM’s Certified Petroleum Data Analyst (CPDA) certification include: data governance, data analysis, data quality management, data security; as well as knowledge of spatial data, the E&P life cycle process, master data management, and communication (PPDM, 2017). Petroleum data management appears to be facing similar challenges as the related librarian profession, where the rise in self-service information access and diversification of roles fuels continued debate regarding the level of professionalism understood in and imbued by the degreed title Librarian, and future specializations expand beyond the traditional concept of a library and need to accommodate the changing nature of information retrieval and use (Clark, 2015). For data management, Oestreich & White declared: “hybrid roles … spanning functions and departments will continue to blend IT and business roles to become almost the norm” (2016).

AUTOMATION/DIGITAL OILFIELD The most recent industry downturn hastened the already-approaching future of automation, particularly in the oilfields (Krauss, 2017). Ahmed Hashmi, head of upstream technology

Industry News

for BP Plc., recently noted: “To me, it’s not just about automating the rig, it’s about automating everything upstream of the rig. The biggest thing will be the systems” (Wethe, 2017). One field worker’s perspective is that “even though modern technology is great; you can’t eliminate the person. To make sure it never fails, you’ve got to have somebody there watching it, to verify it” (Wethe, 2017). Do you?

SECURITY AND BIG DATA One data management aspect gaining prominence amongst organizations and professionals is the change digital technologies place on methodology and best practices. Digital security and big data are placing pressure on organizations to address these challenges. Digital security has traditionally been the jurisdiction of information technology (IT) professionals but increasing volumes and accessibility of data have placed pressure on the traditional solutions with respect to operating costs, business continuity and recovery times. This has encouraged partnership with subject matter experts to develop targeted, cost effective solutions based on business priorities. Data management professionals need to become familiar with the limitations and business ramifications of these solutions. Providing guidance and representing business interests to IT systems design is crucial. For example, identifying which data systems would create significant business disruption if compromised allows IT to design prioritized and fit for purpose solutions without incurring excessive costs. Professionals also need to be familiar with the limitations and jurisdictional restrictions that online (cloud) storage entails given its global distribution and restricted influence that organizations have on security practices (Rao, 2015). Big Data is also affecting the way knowledge workers access and utilize data though IT systems. The massive increase in data creation and retention makes it increasingly difficult, and often

impossible, for data management practices to keep pace with the initial evaluation and rating of data quality. Thus, less than 40% of knowledge workers have the insight and resources to utilize data in an effective manner (Christofferson, 2011). These resources are defined as: • Attainability: information is available and easy to find. • Usefulness: information is of a known quality and usable format. • Capability: employees have the ability and predisposition to analyze information effectively. Data management, therefore, needs to evolve to accommodate the changes that are occurring in the increasingly digital world and data management professionals need to become knowledgeable with these IT-derived challenges. As additional skills required of future data managers are identified, so will different areas for specialization and education. Professionalizing data management “is not about the individual but the value that an individual brings to support consistent practices within an organization” (PPDM, 2017). “Data and analytics leaders cannot master the opportunities and challenges of digital business transformation with yesterday’s roles and yesterday’s organizational design. Now is the time to create new data and analytics roles

that are fit for the digital business future” (Oestreich & White, 2016).

COMMITTEE According to futurist John Seely Brown: “if every game is changing over the half-life of 5 years for most skill bases, that means that almost all our learning is going to happen in workspaces” (PPDM, 2016). PPDM believes professional development is essential to establishing and maintaining a standard of excellence within a professional discipline (PPDM, 2016), and that a core value of professional associations is to lead the charge in professional development. The need to identify, coordinate, and advocate for current and future training and competencies required to advance data management as a profession is why, in 2015, the Professional Development Committee (PDC) came into being. Founding members were drawn from large and small exploration, drilling, and property management companies. The value of this inter-corporate collaboration is that a diverse committee “can generate knowledge and insights beyond the reach of its individual members” (Jehn et al., 1999). The experiences and perspectives brought to the committee by individual members mean each issue and deliverable will be conceptualized, constructed, and evaluated with and in

Foundations | Vol 5, Issue 1 | 19

Industry News

consideration of multiple perspectives. This will make committee findings more broadly applicable and adaptable to a variety of organizational settings.

COMMITTEE FOCI PDC members work on sub-teams researching and developing deliverables for each focus area; with key items and deliverables promoted to and collaborated on by the PDC as a whole. Key 2017 deliverables and initiatives are briefly described in the following sections. • Professional Development Catalog One committee focus area is developing a centralized catalog of qualified educational and training opportunities hosted on the PPDM Member’s site. In 2016 a sub-team began identifying relevant content and designed the catalog’s overall look and feel. A PPDM staff member created the web space. The Committee’s first collective 2017 task is developing and documenting professional development catalog governance. • Value Proposition As the PDC works to define the value of data management as a profession in general, the committee needs to identify how PDC members can best utilize expertise and resources to deliver value. In developing a value proposition, the committee will examine and continue refining scope and identify/define key terms and concepts applicable to work efforts and deliverables. • Career Paths and Competencies Competency work is a long process of reviews by various industry organizations, labor bureaus and agencies in multiple countries, and other professional disciplines who would recognize and adopt competencies that are required for the industry – acceptance and adoption grow over time (West Nodwell, 2016). The connectivity between job families, career paths, competencies, and compensation led to cross-work

stream efforts. These work streams are in the process of identifying what information is available and what is needed around the topics of: retaining and attracting professionals, identifying business needs, connecting needs and opportunities to qualified professionals, and continued people development. The final focus area, identifying and support initiatives to develop curriculum, sample data, case studies and other support materials for training and education, is an ongoing effort also interwoven amongst all work streams.

CONCLUSION “Data and analytics leaders cannot master the opportunities and challenges of digital business transformation with yesterday’s roles and yesterday’s organizational design” (Oestreich & White, 2016). Professional associations are a key contributor and enabler for data management professionals to meet petroleum industry needs.

PDC UPDATE 2018 AND BEYOND The PD Catalogue web pages are currently under construction and a governance document has been written. We anticipate pilot testing and revisions over the coming months and a launch in 2018. This will be a valuable online resource for petroleum data managementrelated courses (webinars, seminars, classroom training, field trips, etc.). Keep an eye on our website for your opportunity to submit your courses. The Value Proposition workstream has been working hard on developing a value proposition template and a deliverables document. Next steps will involve reaching out to multiple stakeholders to gain a broader understanding of our community views. If you have an interest in this dialogue, please reach out. 2018 will be a big year for the Career Paths and Competencies workstream. Research was launched at the Calgary Data Management Symposium in October 2017, and this will be followed up with online surveys, focus groups,

20 | Journal of the Professional Petroleum Data Management Association

one-on-one interviews and many other methods of collecting this critical data. We’re beginning to connect with Human Resource departments to gain their unique perspective on petroleum data management positions, and inviting our data management community to share your stories – what was your career path? What does your current job description look like? Are you on a fair and equitable compensation framework that rewards you for your expertise?

FINAL CONCLUSION PPDM is moving fast to keep up with our volunteers and our membership interests and demands, and we’re excited about the challenges for the coming year. If you’d like to get involved with any of our initiatives and ensure your company’s voice is heard, please join us by contacting volunteer@ppdm.org.

References for this article are available at www.ppdm.org/foundations.

Would you build your house on a weak foundation?

Why build your business on weak data?

More than 25 years of collaborating with industry expertsâ&#x20AC;Śwe know about foundations! www.ppdm.org

Data Attenuation

By Trudy Curtis, PPDM Association ORD EM E P RO GR

ave you ever played the gossip game? Check with your “younger me” and find out. Here’s how it works. Everyone sits in a big circle. Someone whispers a meaningful sentence into the ear of the first person in the circle. The whisperer only gets one try – no do-overs. The listener repeats exactly what they heard into the ear of the next person, and so on until the message gets back to the last person. The last person says what they heard out loud, so everyone can hear it. The results are strange and hilarious. Never accurate! The capability, bias, expectations and knowledge of both sender and recipient frame the game. Rather than passing along an exact message, it’s natural to synthesize what we think we heard (the perceived message), rather than pass along the message verbatim. From this, children are supposed to learn that gossip is dreadfully untrustworthy, and that it won’t get clearer or more positive as it spreads. Based on my daily media consumption, this is an unfortunate truth. Attenuation refers to the degradation of signal strength as it decays over time, across distance, or through interference. Your cell phone signal suffers from attenuation – at home you have excellent signal (lots of bars!), but if you get into a subway, drive through some hilly country, or get too far from home, the signal can’t reach you anymore (no bars!). Working out what causes attenuation takes lots of time and patience. It’s worth the effort to solve the problem, because doing so strengthens customer trust and loyalty.

THE COST TO INDUSTRY Oil and Gas data suffers from the same problem. Fully formed, complete and robust data is “assumed to exist” at the point when it’s first being created. That’s an over statement, of course. Actually, lots of attenuation can and does happen while – and even before – physical data is created! Sadly, without intentional intervention the content, completeness, quality, usability and reliability of data decays a little bit almost every time data is moved from stakeholder to stakeholder, or from process to process. This is data attenuation, and it’s preventable. The total value of the Oil and Gas industry varies year by year, but most estimates place it at around a trillion dollars a year. A study done by CDA a few years ago suggested that 25-33% of the value of industry was embedded in the data. That’s at least a quarter of a trillion dollars every year that’s at risk if critical data is attenuated. Business runs at the speed of data. Getting useful data into your analytics and decision software faster can improve your competitive edge. Digital is pushing Oil and Gas to the edge. Rich McAvey, Gartner Research says, “The ability to digitally innovate faster, more effectively and at scale is now the primary difference between Oil and Gas leaders, and laggards.” However, in another study, Gartner projected that in 2016 less than 10% of self service Business Intelligence initiatives would be governed sufficiently to prevent inconsistencies that adversely

22 | Journal of the Professional Petroleum Data Management Association

affect the business. (https://www. gartner.com/newsroom/id/2970917) Today’s petroleum data managers spend up to half of their time receiving, validating and preparing data that has come to them in digital form, often from trusted vendors and suppliers. Users who require data run into critical delays or even show stoppers, because it requires intervention before it’s suitable for what they need to do. The Gartner report suggests that problems can happen when frustrated and impatient business units circumvent internal (data) control systems in order to use analytics tools. Delays caused by reworking digital data to make it “fit for purpose” slow business down, and can cost your company competitive advantage. On the flip side of the problem, the risk introduced by bad data can be incalculable. Why does our industry spend so much time working and re-working digital data? Why is data that is fit for one purpose not fit for another?

THE ROLE OF THE DATA MANAGER Data managers are tasked with figuring out where and how data attenuation occurs and then prevent it from happening, repair the damage, or mitigate the impact of data attenuation to our many stakeholders and users. Data attenuation that happens before you receive data can be difficult to prevent, particularly when the attenuation results from proprietary systems and processes applied to the data by another stakeholder. Often, the data receiver has

Editorial

Who Has the Data?

Service Company

Operator

Regulator

limited or no control over systems and processes applied before the data arrives. Ultimately, this means that data managers are cast in the role of fixing data problems upon receipt, rather than solving them at the source. Consequently, data managers at operating companies, vendor companies, regulators and service companies are locked into an endless cycle of “fixing” data over and over again. While it’s intuitively obvious that solving problems at the source in order to break the cycle is ideal, it’s difficult to accomplish.

KINDS OF ATTENUATION An almost endless array of opportunities exists for data attenuation. There are at least two main mechanisms for data attenuation to happen. Physical attenuation is the outcome of physical data loss; it’s often intentional, and can have unanticipated consequences. Contextual attenuation results in the loss of data context, meaning and trust, and is often the unintended consequence of “the need for speed”.

PHYSICAL ATTENUATION 1. Data Transfer Attenuation The ecosystem of Oil and Gas data depends heavily on data received from outside sources. In fact, the vast majority of data we rely on is generated by outside sources over which we have no control. Data created during hydraulic fracture operations illustrates this idea. Complete original data isn’t usually transferred between processes or stakeholders; some is lost. Sometimes, this is intentional, particularly if the recipient does not want certain kinds of data, or the

sender is not legally obligated to provide it. Hydraulic fracture jobs are expensive and time consuming. Throughout the job, vast quantities of data in many data categories may be generated, including a lot of real time information about volumes, materials, pressures, rates and more. Typically, the service company who conducted the job will keep a complete set of all the data generated, at least for a time. The operator will receive summary information and technical details as outlined in the service contract, but they don’t normally receive the entire data set. Often, this is because the primary users don’t need every bit of data; the real-time data streams from each of the pumpers may not be important to a geologist at an oil company! Regulatory reporting requirements are fulfilled to the extent required by law; in nearly all cases, only a very tiny subset of data (if any) must be provided to the regulator. What happens when our capability increases, or our obligations change? What happens when the owner of a well goes bankrupt and the regulator assumes responsibility for the well? Social, regulatory and technology pressures are escalating to demand transparency and risk mitigation at all levels. Data that satisfied stakeholder needs a few years ago doesn’t fit the bill today. Today’s data analytics tools often languish for lack of data, as technical capacity for huge data volumes expands. How many business intelligence projects fail because they are starved for good data? If all stakeholders had full access to complete data sets, the science of hydraulic fracturing might advance more swiftly to address global impact concerns and the need for better productivity. 2. Content Attenuation Data recipients, perhaps one or two processes removed from an earlier recipient, may find themselves with data that is not complete, or which they think is wrong. A couple of typical situations may help illustrate this point. Aggregation: Sometimes, the sheer

size of a dataset forces users to aggregate data to make it useful and manageable. Production SCADA readings are often aggregated to daily or monthly summaries. Millions of 3D seismic survey data positions may be reduced to just a few points. Real time drilling data will often be summarized for easy consumption. Calculation: Original data may be used to derive new data points; in geochemical studies, original instrument readings are used to construct ratios or other calculated values. Often, only the derived data, and not the original data, is included in the resulting data set. Loss of content can be frustrating for users, many of whom will go to extreme lengths to reconstruct the original data. While content attenuation made a lot of sense when data streams had to be kept small, and data storage was challenging, today’s technology has solved these problems. Keeping original data is a critical element in establishing the provenance of scientific data. Results only: Despite the scientific method training we get in school, the methods by which data were created are often discarded, and only the resulting measured data retained. Scientists often struggle to interpret this data correctly, particularly when information about the instrumentation, materials, conditions, and other experiment values are not present to give the measured readings context. Methods, instrumentation, and even details about the analysts who conducted work are critical for scientists. Ignoring this need can result in wasted time, improper analysis or interpretation errors.

CONTEXTUAL ATTENUATION Users may need to create or change what they received, to make it fit for their purposes; this revised data is later passed along. Sometimes altered, interpreted or calculated information is transmitted, often without data about who did the work, or the conditions and criteria used. Recipients can’t tell what data is original and what is not. The result is loss of user trust,

Foundations | Vol 5, Issue 1 | 23

data that is unusable, and tremendous amounts of time and resources expended before “useful work” can be started. Provenance matters. Keeping track of each bit of data’s history (think what, where, why, who, when, and how), helps data recipients understand their data faster and more efficiently. And that can make the difference between success and failure!

FROM UNDERSTANDING TO PREVENTION The objective of data management is to create an environment where trusted data remains available and accessible to all stakeholders and processes through the E&P life cycle without being lost or corrupted. Earlier, I said that data attenuation is preventable. Prevention requires everyone to identify the sources of data attenuation and develop process to prevent attenuation where it’s appropriate. That means we need to work together, because attenuation that’s not a problem for one group of users may be catastrophic for others, or may delay the adoption of new systems and technologies in the future. Theoretically, this is possible, but achieving it isn’t easy. There are a lot of social, economic, legal and regulatory barriers in place, so it will be an interesting journey. The prize we seek is worth it. Oil and gas prices are always going to be volatile. A strong foundation will help us position industry for efficiency, safety, and effectiveness. Having good, trusted data helps industry make good decisions that balance the needs of all stakeholders. Lau Tzu, a Chinese philosopher, is reputed to have said “Do the difficult things while they are easy and do the great things while they are small. A journey of a thousand miles must begin with a single step”. It’s probably a bit late to claim that the road ahead is easy, or the task small, but we must all take the first steps together if we are to make progress. Here are a few first steps. 1. Standardize our foundational data management practices. Industry-

wide collective action allows us to align the needs of all industry stakeholders, who all rely on data created outside their own sphere of direct influence. A common foundation will help us develop cost effective software, deploy “ready to work” consulting Data A�enua�on Be�er teams, and leverage business Stewardship Strategy intelligence and analytics tools faster. The PPDM Knowing where data came from, how Association is working with industry it was created, when it was changed, to create a library of data management who made the changes, and why those practices and useful data standards. changes were made provides critical 2. Teach everyone. Clear, consistent contextual information that builds trust programs that teach industry standards in user communities. The PPDM Data and best practices to both technical and Model allows you to track provenance at business students help position industry a high level, or in very specific detail. for the future. Data managers, supporting 6. Enforce our expectations in staff, business users, managers, regulators, contracts, regulations and procedures. students, scientists – everyone who uses Once we agree what these standards petroleum data should understand the are, data can be tested by each recipient fundamental concepts behind what stakeholder and rejected if it fails makes data good, and the consequences to meet these criteria. Regulators, of breaking the chain. The PPDM operators, vendors and service companies Professional Development Committee is will all benefit from this clarity. The developing a global library of training PPDM rules library (Rules.PPDM.org) opportunities at train.PPDM.org. contains over 3,000 rules; industry 3. Manage data for “WE”, rather than experts are continuing to build the “ME”. Industry consensus adds strength library with this goal in mind. to data expectations enforcement. If our data processes are focused on what SUMMARY makes the data “fit for purpose” for one Data attenuation has deep roots in the user community, other communities social, cultural and technological history are likely to face the added expense and of the oil and gas industry. Increasingly, time required to find or remediate data. the negative consequences to industry Rather than thinking of data behavior stakeholders is resulting in unacceptable as our competitive advantage, let’s think levels of inefficiency and redundancy. about it as the life blood for the industry, Solving the problems inadvertently remembering that nearly all the data we created over decades of legacy practice is get comes from an outside source. We can difficult, but a necessary step in making only get it right if we solve the problem for industry more competitive, safer and all of us! That’s what standards are for! more compliant. Working collectively 4. Agree what “good” data looks like. through industry associations such as the Common expectations set the foundation PPDM Association will help us to solve for unattenuated, usable data sets that help the problems that we can only fix over us avoid the time and expense of reworking and over again if we work individually. data again and again. The PPDM Rules Get involved! www.PPDM.org Library (Rules.PPDM.org) contains a About the Author growing, industry developed set of rules. Trudy Curtis is the CEO of 5. Remember that history matters. the PPDM Association.

24 | Journal of the Professional Petroleum Data Management Association

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Service Co

Op Op user Reg Reg user Data receiver receiver Vendor Original

Changed

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Service Op Op user Reg Reg user Data Co receiver receiver Vendor Original

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SLC Corner

SLC

SLC Corner PODS: A Data Model Developed By Industry Experts

s database technologies and the volume of data being produced continue to expand on a daily basis (this is a good thing), many challenges in database management have been revealed. Among the problems that have arisen as a result of this exciting technological trend are: establishing a comprehensive yet flexible data management framework and creating a holistic management system that supports not only the pipeline industry, but any industry. The pipeline industry approached these challenges head-on in the late 1990’s with the formation of the PODS Association (Pipeline Open Data Standard), a non-profit Association made up of seasoned pipeline industry professionals, which continually evolves and adapts to technologies, regulations and operational trends and has done so since its inception. Most recently, the PODS Association has undertaken an initiative to transform the data model, completely redesigning it in order to reshape the standard into an easyto-understand, simplified, and rigorously documented database model that utilizes current technologies. The deliverables from this Next Generation (“PODS Next Gen”) initiative will result in a significant step forward for pipeline industry data management and drastically expand on the already strong value proposition for active participation in the PODS Association and for implementation and/or upgrades to this latest iteration of the PODS data model.

PODS ASSOCIATION STRUCTURE Established in 1998 the PODS Association is a non-profit vendorneutral pipeline industry association that is member driven and volunteer run. The Association, led by Kathy Mayo,

the executive director, is governed by a 12-member Board of Directors, a Technical Committee on Governance and supported through the Technical Committee On Data Modeling, a Communications Committee, and formalized Work Groups that spearhead various initiatives. The Board of Directors and each of these Committees and workgroups are comprised of a healthy distribution of personnel from both pipeline operator as well as industry vendors. This structure, supported by the Association’s 170+ member organizations, provides a strong and well-versed knowledgebase and skillset that spans across all facets of the pipeline industry and has allowed the Association to support and improve upon standardization and data management improvements throughout the pipeline industry.

VALUE – WHAT’S IN IT FOR YOU? The value of the PODS Association to industry operators, vendors, government entities and organizations is derived through networking opportunities among Member organizations and vendors as well as through the creation of a robust and pertinent Data Model Standard that involves: 1. Shared purpose and powerful collaboration among member organizations in developing and maintaining the data model. 2. Development of tools to analyze and manage assets. 3. Improved interoperability across the industry gained by leveraging a standardized data model that is viewed as the benchmark for pipeline data management.

VALUE #1) SHARED PURPOSE AND POWERFUL COLLABORATION VIA NETWORKING AND KNOWLEDGE SHARING OPPORTUNITIES As with many industry organizations, membership in the PODS Association provides an inherent value to its members through networking and knowledge sharing opportunities with its extensive network of industry professionals. The PODS Association accomplishes this open communication through: • Annual member Forums (inperson and teleconference). • Pipeline Week Conference (an annual industry conference with global recognition). • Webinars (at least six per year). • Newly redesigned interactive Member Portal website (includes, among other things, a ‘Community Forum’ for textbased discussions and support). • Newsletters. The development of this data model incorporates decades of combined operator and vendor experience and provides the most comprehensive starting point for pipeline data management that any company could ask for. The model is flexible enough to incorporate organization-specific requirements, resulting in the need to develop an extensive proprietary data model for managing asset data becoming a thing of the past. Implementation of the PODS data model provides vendors the ability to leverage their decades of experience and technical prowess for tailoring this data model to a given organization’s specific requirements in order to implement an organization-specific model.

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VALUE #2) TOOLS TO ANALYZE AND MANAGE ASSETS To be fully leveraged, a data model (propriety or standardized) requires tools to extract and analyze the data within it to convey information and knowledge about the contents. In this case, PODS Association vendor members have provided the industry with a wide set of data management, analysis and reporting tools that are largely plugand-play when paired with a PODS data model. Each PODS Association Vendor member has created their own, unique set of tools that leverage the standardized PODS model to execute various tasks: • pure data management (read/ write/modify/delete the database), • advanced spatial analysis that supports integrity, compliance or any number of other use cases within the industry, and, • generating scheduled or ad-hoc reporting tools that again provide simple to use and reliable methods to a wide variety of use-cases. Vendor development and operator implementation of these broad sets of tools is made possible by the PODS Association’s commitment to standardization and the members’ commitment to advancing the pipeline industry as a whole.

VALUE #3) INTEROPERABILITY - SAVES MONEY AND TIME In an industry that undergoes a continuous cycle of acquisition and divestitures, the value of interoperability, in any aspect of the transaction, becomes a marketable financial and time advantage for both entities executing a deal. When both entities are PODS Association members and have implemented a PODS data model, there is an immediate known synergy between the entities that provides the operations teams confidence in their ability to quickly transfer/integrate the pipeline to the respective system and begin executing their operational activities, reporting and analysis. While data integration activates aren’t eliminated altogether,

PODS NEXT GENERATION DESIGN PRINCIPLES Design for the Future – The next PODS Pipeline Data Model will be transformational not an incremental update to the existing standard. A Solid Core Coupled with Agility and Flexibility – The Data Model will allow for independent extension of the model in a flexible fashion yet in alignment with core modeling and design principles. Support Time and History – The Data Model will support time-based events and track asset lifecycle history. Support Location and Spatial Representation – The Data Model will provide flexibility to include or exclude spatial representation of assets and events. Support Interoperability – The Data Model will support data exchanges between systems and work streams within an organization as well as data exchanges between organizations. Easy to Understand – The Data Model will be easy to understand, implement, extend and use. data integrations between two PODS Association members tend to focus more on the standards, processes and workflows used to collect and manage the data rather than focusing on the complex and timely processes of transferring data between two dissimilar systems and evaluating the raw data. This benefit expedites the overall integration process and provides both entities advantages during acquisitions and divestiture processes. This broad structure to development, support, and enhancement the PODS data model, comprised of both operators and vendors, provides a strong foundation for any pipeline operator to be able to execute best in class data management without the timely and costly burden of internally developing proprietary solutions in-house.

PODS NEXT GEN – THE NEXT GENERATION DATA MODEL As with any technology (which data models most certainly are) it is important to remain proactive in the assessment of innovative solutions, which expand on the technology and more importantly to act when said innovations become proven, accessible and provide a return on investment.

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The PODS Association has done just this with technologies such as improved data exchange protocols, system integrations via service-oriented approaches, and compatibility with Esri’s ArcGIS Pipeline Referencing (APR). This redesigned data model will truly standardize and modernize data management and reporting across the pipeline industry. The PODS Association’s Next Generation or “Next Gen” initiative is focused on a complete transformation of the PODS Pipeline Data Model and is driven by PODS Association Strategy objectives as defined in its 2016-2019 Strategic Plan, as well as from nearly 20 years of PODS Pipeline Data Model implementation experience and lessons learned. It’s slated for completion in 2018 with the formal release of a new data model – the PODS Pipeline Data Model Version 7.0 – as well as a Data Exchange Specification and migration and implementation guidance and instructions. The Next Gen Data Exchange Specification will facilitate data translations between the PODS Pipeline Data Model Versions 7.0 and earlier as well as other data models and will also enable system integration via service-oriented approaches.

NEXT GEN DETAILS The PODS Next Gen standard will include several new elements and capabilities not present in current PODS standards. This will include a business intelligence (BI) presentation layer for easier and more efficient queries and analysis of data in a PODS database, an XML-based data exchange specification for data interchange and migration, support for big data analysis, and implementation guidance and templates for open source platforms. Additionally, PODS Next Gen is adopting the Open Geospatial Consortium (OGC) Geographic Markup Language (GML) as core construct for logical modeling of the new standard. The native compatibility with Esri’s ArcGIS Pipeline Referencing extension within the PODS 7.0 Geodatabase implementation is an exciting advancement to the pipeline industry and has received broad support and excitement across the

industry. This enables operators to better define their asset hierarchy, manage the linear referencing system, and maintain asset data within the common and familiar Esri platform while still maintaining a standardized data model that adheres to the PODS data exchange specification and can leverage the broad industry tools and features that are readily available.

PODS LITE (A WORKING SUBSET OF NEXT GEN) AVAILABLE NOW! The Next Gen initiative has already resulted in the release of the PODS 7.0 Lite model, an abbreviated version of the full PODS 7.0 data model, and allows both PODS members and nonmember to explore how implementation of this recognized and established data model may improve their pipeline data management, integrity and risk management, operational and regulatory reporting activities.

PODS lite is available in an Oracle, MS SQL Server, PostgresSQL and Esri Geodatabase implementation pattern; the Esri Geodatabase pattern being compatible with the Esri’ APR extension.

HOW YOU CAN BECOME INVOLVED More information about the PODS Association and the Next Gen initiative can be found on the Association’s website www.pods.org and through our many networking and communication channels. The Next Gen initiative is currently making calls for volunteers to support sub-work groups associated with the development of specific modules (data model extensions that support a specific use-case relevant across industry) such as ‘In-Line Inspection’ and ‘Tracking Database History’. Contact Kathy Mayo, PODS Association Executive Director, at Kathy.mayo@pods.org or 907-347-3279 for more information about volunteer opportunities.

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Community Involvement

CI Thanks to

our Volunteers

OCTOBER 2017 Christopher Hudson Christopher Hudson, Secretary of PPDM’s Australia West Leadership Team, is the October 2017 Volunteer of the Month. Chris is an instrumental member of this small but mighty leadership team, which recently doubled attendance at both their luncheons and the Perth Data Management Workshop – through hard work, involving meetings with current and potential new members. “Chris has been incredibly helpful with our events in Australia, providing assistance with logistics, supporting communications with our Calgary team, and helping to make sure everything ran smoothly. Chris and the Australia West Leadership Team have achieved tremendous results, showing how an effective leadership team works with local stakeholders. We look forward to building on their work in the rest of Australia,” said Jess Kozman, Asia Pacific Representative. Chris Hudson has held the position of lead subsurface data coordinator at INPEX Corporation for more than five years. Prior to joining INPEX, Chris was the senior data management analyst at Woodside Energy, a geological data coordinator at BP, and he also worked with Chevron. Chris spent time as a research geophysicist with Curtin University, from which he holds a Bachelor of Science (BSc. Hon) in Petroleum Geophysics.

NOVEMBER 2017 Derek Garland Congratulations to Derek Garland, the November 2017 Volunteer of the Month. Derek is the Executive Vice President at WellDrive. He has more than 20 years experience in the Oil and Gas industry, and holds degrees in Applied Physics and Mechanical Engineering from Jacksonville University and Georgia Tech. He began his career as a field engineer, acquiring and processing downhole data with Schlumberger, and advancing to several senior management positions with Schlumberger, Supreme Services, and Dialog prior to joining WellDrive in 2013. A member of the Dallas/Fort Worth Leadership Team, Derek has travelled across North America to show his support of PPDM. “Derek has been a tremendous supporter of PPDM over the last few years, attending events, sponsoring luncheons, speaking at workshops, and more. Derek’s constant support, stepping in when needed, and enthusiasm make him a delight to work with, and we are excited to grow the Data Management community with his help,” said Pam Koscinski, PPDM’s USA Representative.

JANUARY 2018 Giovanni Ramos Giovanni Ramos is PPDM’s January 2018 Volunteer of the Month. Giovanni is an engineer in the Crude Oil Group of the Resource Management Branch of the Saskatchewan Ministry of the Economy (ECON). “Giovanni has been a significant part of the success of the What Is A Completion Work Group. His helpful demeanour and willingness to step in whenever needed is appreciated by the whole team. His presentation

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on behalf of the work group at the Calgary Symposium was well received by all attendees, generating interest for the work that the group has done,” said Ingrid Kristel, Senior Project Manager with the PPDM Association. Beginning in 2011, Giovanni has been with the Saskatchewan provincial regulator for most of his professional career, initially starting at a field office in Estevan and most recently with Reservoir Applications at the head office in Regina. He joined the What Is A Completion Work Group in the summer of 2016. Giovanni is highly involved with the Group’s co-op students, providing training and supervision, and is also a passionate volunteer with Resource Management’s Social Committee, ECON’s Occupational Health and Safety Committee, and the Petroleum and Natural Gas Division Employee Engagement Committee.

FEBRUARY 2018 Sue Carr The February 2018 PPDM Volunteer of the Month is Sue Carr, Chair of the Calgary Leadership Team and Manager, Consulting Services with Katalyst Data Management. Sue has more than 35 years of implementing software and data management systems and leading subsurface data teams. Sue is focused on building a DM Consultants Group to help solve E&P companies’ data challenges. “Sue was invaluable in pulling together the 2017 Calgary Data Management Symposium, Tradeshow & AGM, providing leadership for the team, ideas and suggestions, including the popular Executive Panel, and helping to secure attendees and sponsors,” said Elise Sommer, Senior Community Development Coordinator. “Sue is always available for advice, suggestions, and a helping hand to make the Calgary community stronger. We truly appreciate all her hard work.”

Community Involvement

MARCH 2018 Cindy Cummings Cindy Cummings is the March 2018 PPDM Volunteer of the Month. Cindy is a member of both the Professional Development Committee and the Houston Leadership Team. With more than 35 years of work experience in the Oil and Gas industry in areas of exploration, development, production and information management, Cindy has worked for Conoco, IHS, Energy IQ and is currently in Exploration

Technical Services at Repsol in The Woodlands, Texas. Cindy has authored and presented papers on Oil and Gas Data Management, Data Governance, Technology and various other topics over the years at PNEC, PPDM, and ARMA conferences. Cindy is also a certified Project Manager which helps to keep the many data management strategies, programs and projects synchronized and moving forward in this ever changing industry. “Cindy has been a valuable and engaged volunteer with the Professional Development Committee since its inception. Through her work on the PD

Catalogue sub-committee, we have seen the acceleration of content development and an enriched rollout plan. Cindy is a visionary with the experience, knowledge and energy to make things happen. We’re very fortunate to have her at the table.” said Margaret Barron, Chair, Professional Development with PPDM. “Her work with the Houston Leadership Team has been greatly appreciated, including her terms as Secretary for the team. She has helped to make our events, including the upcoming Houston Expo, possible for our attendees, and we appreciate her willingness to always lend a hand,” said Pam Kosinski, USA Representative.

An Interview with Tammy Carter, Infosys By Jim Crompton, Reflections Data Consulting

WERE YOU USING, WHAT WERE THE KEY CHALLENGES)?

There is so much I could say. The best way to make improvements is to know and understand history. I am amazed at how much has changed since 1980 but almost equally amazed at how much has not changed. I can remember when the CIO position was created. I remember when I first read about the CDO position. The CIO position has moved forward, but the CDO position has not (in some industries it has). I think the CDO position will move forward as technology continually changes and business decisions must be made faster and faster based on solid, sound quality data.

1) HOW DID YOU GET INTO DATA MANAGEMENT (WHEN, WHAT WAS THE REASON, WHAT DID THE DATA MANAGEMENT GROUP LOOK LIKE THEN, WHAT TECHNOLOGY

I do not think I ever “got into data management”. I completed a computer science degree in 1980 and entered the business world in what was then called “Information Systems”. One might say that my first data management was working on computer solutions in 1980 with data entry input screens. Of course we had to build-in data entry rules. These rules definitely qualify as data quality rules! So, over time I was probably involved in difference aspects of data management, but not ever in a formal DM team until I joined Noah Consulting in Aug 2012.

2) WHAT CHALLENGES HAVE WE RESOLVED AND WHAT CHALLENGES ARE THE OLD DRAGONS WE ARE STILL FIGHTING? We have met the challenge that everyone understands the slogan “trashin trash-out / garbage-in garbage-out”. Everyone also understands inputprocess-output. But, the challenge still remains for people taking ownership! Even though data entry has moved from

a “data entry team” out into the client community, there is still reluctance to take ownership of ensuring that data is valid, and of quality, to use for making sound business decisions. The best solution is for technology to move forward so that automation removes the human interface for data entry (as much as possible). We have made some progress with business clients taking data ownership for its quality, but there is still reluctance for full ownership. Behaviors and change management plays a huge role for continuous improvement in the ownership.

3) LOOKING TO THE FUTURE, WHAT NEW CHALLENGES DOES THE DATA MANAGEMENT COMMUNITY FACE? Future challenge will be handling large amounts of diverse unstructured data and structuring it to ensure that business decisions made from the data are sound decisions. Business teams will continue to look to the Information Technology teams to aggregate, clean, and structure the data so that “they can use it”.

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2019 Stay posted for 2019 dates to be announced soon!

ONLINE & PRIVATE TRAINING OPPORTUNITIES

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Online training courses are available year-round and are ideal for individuals looking to learn at their own pace. For an in-class experience, private training is now booking for 2018. Public training classes are planned for Q3/4 2018 and will be run based upon demand.

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