Who you are actually hiring.

Gossans is led by Jonathan Settelmeyer, who holds a Bachelor of Science in Petroleum Engineering from the University of Wyoming, with a concentration in unconventional resources, taken in 2018.

That concentration is where most of this site comes from. Hyperbolic decline behaviour, the gap between a fit to per-well rate and a fit to field rate, and the reason a type curve entered on the wrong basis stays wrong for the life of a forecast are not incidental interests here. They are the subject.

The person who takes the scoping call is the person who builds the model and presents the finding. Nothing is handed to an analyst you have not met. That is a deliberate limit on how much work we take, and it is the reason a two-week engagement is two weeks rather than a quarter.

The commodity list on this site looks broad for a single practice. It is close to the list one state produces, and it is the state this practice was trained in.

Wyoming is the largest coal producer in the United States and its largest uranium producer. It mines the world's largest trona deposit, separates helium out of its own declining gas streams, holds one of the few advanced rare earth projects in the country, and produces oil and gas across the Powder River and Green River basins. Those assets share regulators, a labour market and frequently an owner, which makes the comparison between them a practical question rather than an academic one.

The arithmetic of extraction economics is the same in every one of those cases. What differs is the physics feeding it, and the engine carries that difference explicitly rather than averaging it away.

Gossans works from Gillette, Wyoming, in the Powder River Basin. Not from Houston, and not from Denver.

Gillette sits inside both halves of what this practice covers. The largest coal mines in the United States are a short drive out of town, and the oil and coalbed gas of the basin are underneath it. The comparison this site keeps insisting on, between a mine and a well valued on one basis, is a local question here rather than a theoretical one.

That proximity matters more than a mailing address usually does, because of how these engagements run. The costing workshop is the step that decides whether an opportunity list is worth anything, since the price and the odds on every line have to come from the crew who would execute it rather than from an assumption somebody typed into a spreadsheet. Being a drive away rather than a flight and a hotel turns that from a production into a half day.

It also means the regulators, operators and service companies whose public filings feed two thirds of our data request are on our doorstep, in our time zone, filing with agencies we read every week.

We sell no software, resell no data, and take no commission on any transaction we are asked to value. There is no product whose adoption our recommendation could favour, which is the usual reason a technical opinion quietly bends.

When our model disagrees with yours, that disagreement is the deliverable. We would rather tell you the project is worth less than you thought and not be hired again than tell you what the scoping call suggested you wanted to hear.

Data arrives under a non-disclosure agreement signed before the request goes out. It is held in a single-client workspace, is never pooled with another client's, and is returned or destroyed at your instruction when the engagement closes.

We publish method, never clients. Every figure on this site is either public record or model output built to demonstrate the method, and no engagement is referenced without written permission.