When One Well Becomes a $12M Assumption: Sky Identified a 20–25% Overstatement in EUR—Using the Same Data, in Under an Hour
The Problem
In acquisition workflows, value is driven by forecasted sales volumes—not what is measured at the wellhead, but what ultimately reaches market after separation and processing.
For volatile oil systems, this becomes non-trivial. Small changes in pressure and temperature from reservoir to surface cause fluid composition to shift—liquids flash into gas, condensate behavior changes, and measurement points diverge. What is reported is not what is sold, and what is modeled is often a simplification of both.
Now layer in a more common commercial reality: A private equity group is evaluating a 3-mile lateral development in an onshore US play, but:
- Only two nearby 3-mile wells exist
- The surrounding dataset is dominated by 2-mile laterals
- The seller provides a high-end EUR (~783 Mboe equivalent) based largely on a single strong well
How It's Done Today
- Engineers screen nearby wells manually
- Filter by lateral length, geography, completion parameters
- Build type curves from limited analogs
- Apply engineering judgment to scale results
- Reconcile to sales volumes assumptions
Two structural issues persist:
Sample Bias Is Unavoidable
When only one or two wells exist, the "type curve" becomes a narrative choice.
Time Constrains Rigor
Expanding the dataset (even modestly) requires days of manual effort, so most analyses stop early.
The result: The answer is often less about what the data says—and more about what the process allows.
What Sky Did
Using the same publicly available data and standard workflow logic, Sky:
- Pulled wells across a broader, relevant geographic window (not just immediate offsets)
- Included 2-mile laterals and scaled behavior appropriately
- Evaluated multiple depletion points across the system
- Reconstructed type well behavior using actual production distributions
- Produced a repeatable, data-driven EUR range
Result
- Seller case: ~783 Mboe
- Sky-derived range: ~585–650 Mboe
- Delta: ~20–25%
"The client's own engineer independently ran the workflow, arrived at a similar lower estimate, and chose to stay with the higher EUR well."
Key Insight
Sky did not introduce a new method. It exposed the outcome of applying the same method—without constraint, bias, or time limitation. The real difference was completeness and speed.
Why This Matters
The Market Often Relies on Incomplete Analysis
In transactions:
- Sellers highlight best-case wells
- Buyers struggle to validate quickly
- "Fair market value" becomes a negotiation anchored on incomplete analysis
Sky provides a more complete starting point.
Rigorous Analysis Is Needed That Fits Real Deal Timelines
- Traditional workflow: 1–2 days
- Sky workflow: ~1 hour
Sky delivers complete, data-driven EUR ranges fast enough for acquisition due diligence — without sacrificing accuracy or breadth.
Reproducibility Forces Accountability
- Same data → Same answer
- Different users → Same result
The debate shifts from "what is the number?" to "why are you choosing a different one?"
Engineers Are Not Replaced
For users, Sky does not remove engineering judgment.
It removes:
- Data limitations
- Time excuses
- Hidden assumptions
If a number is aggressive, it is now visibly aggressive.
Sky delivers field-faithful forecasts grounded in full-system data. What you do with that answer is your decision — but it is no longer hidden behind process.
In this case, Sky did not "find a better answer." It made the real answer clear and data-driven.
Next Steps
AlphaX Sky delivers complete, data-driven EUR ranges fast enough for real acquisition timelines. Contact us to see it in action on your next deal.