Experiences on the Hidden Complexities of Data Standards Integration

Public data standards are sometimes viewed as a panacea, but the reality is somewhat different. There are hidden complexities that often delay the adoption of the standard or stall subsequent migration to future revisions. The purpose of this paper is to share our experience as data migration specialists working with PPDM. We will discuss the challenges of both moving to Public Petroleum Data Model (PPDM) for the first time and transitioning between different versions of the PPDM model.

Drawing on real life experiences, we will discuss key learning points and review lessons learned. This will cover the importance of managing complex data relationships and working with expanding data sets. We will also explain how reusable data mappings and models can overcome some of the hurdles usually associated with traditional methods.

The Data Standards Paradox

Adopting a data standard throws up a paradox because the price of progress is complexity.

If the data standard remains fixed and static, then adoption is relatively easy. But to accommodate the needs of its community, the functional capabilities of the standard must develop. This will also drive adoption, but it is this constant change that creates the very complexity that causes standard initiatives to stall.

Unless this functional evolution is factored into a standards adoption strategy, the task becomes too onerous. The result is that sometimes adopters freeze at various versions of the standard, and this creates an unintended and unwanted legacy system.

Drivers of Change

In the PPDM context, we have noticed two factors that have been key drivers of change, and these are data explosion and data interpretation lineage.

Data Explosion

We live in an era of unprecedented change, and this means new devices are being used in every phase of exploration and production to gather more data with which better decisions can be made.

Timescales are also collapsing. Once drilling and logging data were distinct activities separated by days, but now they happen simultaneously.

It is not just that new devices return more terabytes of raw data, but also that this data is more sophisticated than was previously possible.

Metadata (in the Dublin core and IOS 19115 sense) are becoming ever more important in providing context. This has a direct impact on proprietary database design and functionality. It is also being factored into PPDM model development and this is why PPDM will and must evolve to promote adoption and use. The price of progress is growing data complexity.


There is also something unique about E&P;: the assets remain hidden deep underground. There is no certainty that a prospect is viable before it is drilled and massive investment decisions are made on interpretations of current and historic data.

The interpretation of data and judgments applied to that data are what provide “knowledge.” Professional interpreters work with raw data to derive increasingly valuable knowledge that underpins how decisions are made. This is intrinsically complex because most interpretation is based on previous interpretation, which can be wrong.

To complicate matters further, theories of interpretation also change.

This is illustrated by Canadian Geologist Ashton Embry who contrasts Exxon and Statoil’s sequence stratigraphy for Brent sandstone.

Exxon declined but based on their interpretation, Statoil proceeded and developed an economic play.

So PPDM must not just store raw data, it must also store the accumulated knowledge.

Tracking the history of previous interpretations is also essential and we must record:

  • When decisions were made
  • What information was used in making decisions
  • How decisions were made
  • Who made them and why

Data governance, master data management (MDM) initiatives and chains of custody all want answers to the “when, what, how, who, why” questions. This can identify inconsistencies and indicate where errors of judgment or poor data was used.

To provide such answers, data models develop and a time series data ownership graph becomes crucial. If we don’t understand this process, complexity is buried and we can’t fix problems we can’t see.

A PPDM Example of Complexity Driven by Change

This is based on recent experiences building a PPDM 3.7 data connector for Open Spirit Corporation, and investigating migration from earlier versions of PPDM. To illustrate further, we use the example of checkshot surveys and how the data is represented in PPDM.

PPDM 3.2 terms represents checkshot surveys in a simple way with little data.

PPDM 3.3, however, is far more functional. The WELL_CHECKSHOT and WELL_CHECKSHT_SRVY tables are renamed and they expand. For each point, the new WELL_CHECKSHOT_DETAIL now provides 13 in place of 7 attributes, the header record WELL_CHECKSHOT_SURVEY has 30 in place of 6. What’s more 7 of those are now foreign keys to various reference tables where yet more data can be stored.

PPDM 3.4 has limited development although some tables are renamed and the model starts to mature.

PPDM 3.5 matures and there are subtle semantic changes. Whereas 3.4 provided a ROW_CHANGED_DATE and ROW_CHANGED_BY, 3.5 now provides both a space for ROW_CREATED date and owner, as well as a ROW_CHANGED pair. This is typical of the kind of subtle change we’d expect to support data governance, but we must understand it. In 3.5, the implied semantics of ROW_CHANGED are different. If we missed this, let this complexity remain buried and continued to filling the changed date in a data loader, we could be in serious trouble. A later interpreter may believe that items have been updated, massaged or cleaned, whereas all we had done was load raw data.

PPDM 3.6 is a big step-change that could cause all kinds of problems. There is now no table called CHECKSHOT, or anything like it. The whole well check shot concept is entirely remodelled. Now, logically, check shot is handled as seismic data, which is what it is and all seismic is uniformly classified and stored.

PPDM 3.7 applies four new attributes ACTIVE_IND , EFFECTIVE_DATE, EXPIRY_DATE, ROW_QUALITY to almost everything. Using these, we can record the lifecycle of these objects, it’s something that maybe in the future could be remodelled using 4 dimensionalism. It is a kind of complexity that would be hidden if we didn’t understand this is all about answering those “when, what, how, who, why” questions.

Managing the Complexity

Now just imagine we had built PPDM data loaders for all these versions with the standard tools of the geologist turned data manager: PERL, Excel and SQL.

Nothing in such a methodology would help track those model improvements. Nothing in those technologies supports any process for managing these changes. A PPDM loader developed using these tools would probably need to be rewritten for each new version.

And here’s a bit of real complexity we don’t want to hide. Building successive loads into successive versions of PPDM is not the entire task. What about data loaded into previous versions? We’ve loaded it, cleaned it, and it is now quality controlled data so we don’t want to have to load all this again. We need an upgrade path and a strategy that can build those upgrades. We need a plan to insert the relevant metadata that was missing in earlier versions. We need a consistent process of Transform Regeneration.

And this must ensure the upgrade logic reflects the same business logic embedded in the data loaders. We need to ensure that the same data is loaded identically by different paths. The requirement is for a strategy that can simultaneously tackle initial and ongoing model load and upgrade between versions, and this is what we have developed.

A Structured Methodology

To make a robust and repeatable approach work, we use a data integration toolset we developed, called Transformation Manager (TM). This is an approach we have adopted for many years in this industry, and this is how it works.

1. Separate source and target data models and the logic which lies between them.

This means we can isolate the pure model structure and clearly see the entities, attributes and relationships in each model. We can also see detail such as database primary keys and comments. As exposing relationships is the key in handling PPDM and other highly normalized models, this is a critical step.

2. Separate the model from the mechanics of data storage.

The mechanics define physical characteristics such as “this is an oracle database” or “this flat file uses a particular delimiter or character set.” It is the model that tells us things like “a well can have many bores,” “a wellbore has many logs,” and that “log trace mnemonics” are catalog controlled. At a stroke, this separation abolishes a whole category of complexity.

For both source and target, we need a formal data model because this enables us to read or write database, XML, flat file, or any other data format.

3. Specify relationships between source and target.

In all data integration projects, determining the rules for the data transfer is a fundamental requirement usually defined by analysts working in this field, often using spreadsheets.

But based on these, or other forms of specification, we can create the integration components in TM using its descriptive mapping language. This enables us to create a precisely defined description of the link between the two data models.

From this we can generate a runtime system that will execute the formal definitions. Even if we chose not to create an executable link, the formal definition of the mappings is still useful because it shows where the complexity in the PPDM integration is, and the formal syntax can be shared with others to verify our interpretation of their rules.

4. Error detection.

To ensure that only good data is stored, TM has a robust process of error detection that operates like a series of filters. For each phase, we detect errors relevant to that phase, and we don’t send bad data to the next phase where detection becomes even more complex.

We detect mechanical and logical errors separately; and if the source is a flat file, a mechanical error could be malformed lines. Other logical errors could include dangling foreign key references or missing data values.

Next we can detect errors at the mapping level – inconsistencies that are a consequence of the map itself. Here, for example, we could detect that we are trying to load production data for a source well that does not exist in the target.

Finally there are errors where the data is inconsistent with the target logical model. Here simple tests (a string value is too long, a number is negative) can often be automatically constructed from the model. More complex tests (well bores cannot curve so sharply, these production figures are for an abandoned well) are built using the semantics of the model.

Here a staging store is very useful in providing an isolated area where we can disinfect the data before letting it out onto a master system. Staging stores were an integral part of the best practice data loaders we helped build for a major E&P; company, and it is now common practice that these are stored until issues are resolved.

5. Execute a runtime link to generate the code required to effect the integration.

This will generate integration components (in the form of java code) that can reside anywhere in the architecture that it can be best accommodated. This could be on the source, target or any other system to manage the integration between PPDM and non-PPDM data sources.

Managing PPDM Change

Once we have mapped and executed non-PPDM to PPDM integration, what happens when a new version of the model is released? What is the process of migration and how difficult is it?

The answer depends on the degree of change, but we approach it in the following way;

1. Load the new model.

TM will then generate a difference report that clearly identifies the elements that have been added, deleted, shortened, lengthened, subject to new constraints, described differently or any other change.

2. Replace the old model with the new model.

This is a relatively simple task, and we can then begin the process of generating a runnable transformation or integration component. Doing this will bring to our attention all those model features that have altered; and using the mapping capability, we can redesign the transformation.

Even in a worst case scenario (such as the type of complete redesign we saw for the check shot example earlier), we don’t have to start from scratch and re-write everything.

Our process of transformation regeneration means we can start from a solid platform because the descriptive code previously written allows newcomers to get up to speed fast.

It also means we can execute far quicker because modification is only required for critical differences represented by the source and target changes.

When the transforms are created, TM will also inform us if we are not writing all essential items to the target data store; this cuts down on errors and ensures a far more complete transformation.

This obviously provides much greater accuracy and a huge productivity advantage than working with traditional hand-coded methods.

Using this approach, we simultaneously build both the new loader and the upgrade path from the previous version. This maximizes the investment already made in moving to PPDM by re-using significant portions of logic and expertise.

Our experience is that this best practice approach exposes many of the complexities that otherwise will be a major obstacle to the ongoing adoption of PPDM standards.

In Conclusion

PPDM is an evolving standard; and although this introduces complexity, with the right approach it can be simplified.

By creating an industrial-strength integration capability, we have shown that transformation regeneration is an excellent architecture for building a process to support ongoing PPDM adoption and migration.

With care and foresight, the complexities of data integration do not have to remain hidden. With the right approach, they can be seen, controlled and addressed, enabling you to track the future standard and maximize the investment you make in moving to PPDM.

About ETL Solutions

ETL Solutions was formed in 2002 and is based at the Centre for Advanced Software Technologies in Bangor, North Wales.

Our people have unrivalled experience of delivering successful projects across industries with clients such as Shell, Honda, Electrolux, HP, Thorn Lighting and BNP Paribas.

For more information please visit the website, call or email info@etlsolutions.com.

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