If you’ve spent any time around upstream data, you’ve heard about PPDM. And depending on who you talked to, you’ve probably heard one of two things.
The first version: PPDM is the answer. Adopt it, structure your data around it, and your integration problems go away. The second version: PPDM is a thousand-table beast that nobody actually implements all the way, and you’re better off rolling your own model.
Both of those are wrong. Or rather, they’re both half-right, which is worse.
We’re at the PPDM Energy Data Convention in Houston this week, and we’re having the same conversation at the booth that we have with every operator who calls us. People want to know what they actually get from adopting the model. And just as importantly, what they don’t.
Here’s the honest version.
What PPDM is
PPDM is a data model. Specifically, it’s a relational model for upstream oil and gas data, maintained by the Professional Petroleum Data Management Association.
The current model has thousands of tables covering wells, production, land, facilities, geoscience, drilling, completions, and pretty much every other domain in upstream. It’s been refined over decades by people who actually work in the industry, with input from operators, vendors, and consultants who have hit every edge case the rest of us are still discovering.
If you build new data systems from scratch and want a starting point that already understands the difference between a wellbore and a well, between a working interest and a net revenue interest, between a permit and a spud date, PPDM gives you that. You’re not inventing the schema. You’re adopting one that the industry already understands.
That’s the value. It’s real, and it’s significant.
What you actually get
Adopting PPDM gives you a few things that you’d otherwise have to build for yourself.
A shared vocabulary. When a new engineer joins your team, or you bring in a consultant, or you swap vendors, the data model isn’t a mystery. The naming conventions, the entity relationships, the cardinality rules are all documented. Onboarding stops being a months-long archaeology project.
A schema that doesn’t bend in unexpected ways. Most homegrown data models break at some specific edge case that nobody anticipated. Recompletions. Sidetracks. Operator changes mid-quarter. A 14-digit API number that needs to map back to a 10-digit one for legacy reporting. PPDM has been around long enough that those cases have been thought through.
Interoperability with vendor tools. A lot of the upstream software ecosystem either supports PPDM directly or maps to it. If your data lands in a PPDM-aligned model, the integration cost with new tools is much lower than if you’ve built something bespoke.
A foundation for governance. This one is underappreciated. You can’t really govern a data domain you can’t describe. PPDM gives you a structure to point at when you’re defining ownership, quality rules, and access controls.
What you don’t get
This is the part most operators learn the expensive way.
PPDM doesn’t move your data. It’s a target schema, not a migration tool. Adopting PPDM doesn’t pull anything from your existing systems. You still have to build the pipelines that extract, transform, and load your data into the PPDM-shaped tables. That’s data engineering work, and it’s not optional. We walked through what that pipeline work looks like in OCC Data Ingestion: Automating What Most Companies Still Do by Hand.
PPDM doesn’t clean your data. If your existing well master has the same well listed under three API numbers, those three records will land in your PPDM tables as three records unless you do the entity resolution work. The model doesn’t know that “XYZ Energy LLC” and “XYZ Energy” are the same operator. You have to teach it.
PPDM doesn’t define your business logic. Production allocation rules. Working interest calculations. Reserves classification. These are decisions your company has to make. PPDM gives you a place to store the results, not a place to derive them.
PPDM doesn’t make decisions easier by itself. The decision-makers in your company aren’t going to query PPDM tables directly. They want dashboards, reports, and analysis. Building those still requires the analytical layer on top of the model. PPDM is the foundation. It’s not the building.
PPDM is not a small commitment. The full model is enormous. Most shops don’t implement all of it, and they shouldn’t try. The implementation question isn’t “should we use PPDM” but “which parts of PPDM do we actually need, and in what order.”
The implementation question that actually matters
When operators ask us about PPDM, the question they think they’re asking is “should we adopt the model.” The question they should be asking is “what’s the minimum viable subset of PPDM that solves our current problem, and how do we expand from there.”
For most mid-size operators, the answer starts with the well master and production tables. That’s where the day-to-day pain is. Get those right, in PPDM-aligned tables, with a clean ingestion pipeline feeding them. Then add land. Then completions. Then whatever the next priority is.
This is the same incremental approach we wrote about in From Spreadsheets to a Real Data Stack: A Realistic Migration Path for Mid-Size Operators. The strategy doesn’t change just because you’re adopting an industry-standard model. It just gives you a better target to migrate toward.
The trap of over-implementation
The opposite mistake is more common than you’d think. Some operators look at PPDM, see the full scope of the model, and decide they need to implement all of it before they can use any of it.
That’s a multi-year project that nobody actually finishes. We’ve seen companies spend two years building PPDM-aligned tables for entities they never query, while the production data they actually use stays stuck in the same spreadsheets it always was.
The model is a reference. Use the parts you need. Document why you skipped the rest. Come back to them when you have a business reason to. Nobody is grading you on PPDM completeness.
Where PPDM struggles
It’s worth being honest about the rough edges.
The model is large. Genuinely large. Even people who work with it daily don’t have the whole thing in their head, and the documentation, while thorough, is dense.
Some of the conventions reflect industry practices that don’t map cleanly onto every operator’s reality. Smaller shops sometimes find that pieces of the model assume infrastructure or data sources they don’t have. The fix is usually subsetting and adapting, not rejecting the model wholesale.
And PPDM, like any standard, lags slightly behind the bleeding edge of operational practice. If you’re doing something genuinely novel with your data, you may need to extend the model or store some things outside it. That’s fine. The base model is a starting point, not a constraint.
What PPDM is really for
The honest framing is this: PPDM is a way of avoiding the work of inventing your own data model. That’s the whole pitch. You could build everything yourself, and some companies have. But the people who built PPDM have already solved most of the problems you’d encounter, and they’re solutions the rest of the industry already recognizes.
If you’re going to build a serious upstream data foundation, you’re going to need a structured, documented, well-thought-out data model anyway. PPDM is one that already exists, has been tested for decades, and comes with a community of people who can help you use it. That’s worth a lot. It’s also not a magic answer.
The companies that get the most out of PPDM are the ones that treat it as a tool, not a destination. They subset what they need, build clean pipelines into the model, layer their analytics on top, and grow the implementation as the business actually needs it. The companies that struggle are the ones that bought into PPDM as the goal, instead of as a means to a goal.
PPDM solves the schema problem. The rest is still yours.
The data quality work, the governance, the analytical layer. None of that is in the model itself. We covered the quality piece in Data Quality in Upstream Oil and Gas: What Goes Wrong and Where to Start, and the broader migration story in Why Oklahoma Energy Companies Can’t Afford to Ignore Data Engineering. PPDM gives you a foundation that those other pieces can stand on. It doesn’t replace them.
See Us at PPDM 2026
We’re at the PPDM Energy Data Convention in Houston, April 27 through 29. Stop by Booth #2 if you want to talk about PPDM scoping decisions, where to actually start, or any of the specific headaches in your current stack. We’d love to hear what you’re working on.
Further Reading
- Why Oklahoma Energy Companies Can’t Afford to Ignore Data Engineering
- From Spreadsheets to a Real Data Stack: A Realistic Migration Path for Mid-Size Operators
- OCC Data Ingestion: Automating What Most Companies Still Do by Hand
- Data Quality in Upstream Oil and Gas: What Goes Wrong and Where to Start