The Five Customer Segments That Need Subsurface Digitisation

A total addressable market answers one question well and another not at all. It tells you whether a category is large enough that a serious company could be built inside it, and for raster well-log digitisation the answer is plainly yes: the global oil and gas transactions market is a hundred and thirty-four billion dollars, the serviceable software slice of it is six point seven billion, and the obtainable target we set for the end of year five is a hundred and eighty million. We have walked that ladder elsewhere. What the ladder is silent about is the question a sales team actually has on Monday morning, which is not how big the market is but who inside it will pick up the phone first, sign first, and renew first. Those buyers are not interchangeable, and treating the six point seven billion dollar software market as one undifferentiated pool is the fastest way to build a product that is mildly useful to everyone and urgent to no one.

So this piece does the opposite of the sizing exercise. Instead of collapsing the market into a single number, it fans the number out into the five buyer segments that genuinely need to pull a curve off a scanned log, and it asks, of each, what job they are hiring the product to do, how much of the obtainable revenue they represent, and how fast they convert. The product strategist's instinct that you win by dominating one urgent segment before you broaden is older than any of our slides [1], and the only way to honour it is to know your segments by name.

Why a scanned log is five different problems wearing one file format

The thing that makes this market segmentable at all is that a raster log is not one artefact with one use. It is a fifty-year-old greyscale image of a curve that several very different people need turned back into numbers for several very different reasons. The file is identical. The job is not, and the jobs-to-be-done literature is blunt that buyers in one category are often hiring a product for entirely separate underlying jobs, which means the right cut of demand is by job and not by firmographic label [2]. A geologist racing to high-grade acreage before a lease sale is hiring digitisation to answer a question this quarter. A diligence analyst pricing a package of producing wells is hiring it to avoid mispricing an acquisition by tens of millions. A carbon-storage engineer is hiring the very same curve extraction to prove a reservoir will hold injected carbon dioxide. Same pixels, three jobs, three willingness-to-pay curves.

That is the demand-side fact the rest of this piece is built on. When you sort the six point seven billion dollar serviceable market by the job rather than by the industry code, five segments fall out cleanly, and they do not contribute to the obtainable target in proportion to how many of them there are.

The five who need the curve, in their own words

The first segment is the explorers, the exploration and appraisal teams who need legacy logs from neighbouring wells to interpret a new prospect. Their job is speed: a digitised offset log this week is worth far more than a perfect one next quarter, because the decision it feeds, whether to drill or to drop, is on a calendar set by lease terms and partner commitments. They are not the largest pool, but they are among the most urgent, and urgency is what converts.

The second is private-equity diligence teams. Their job is risk, not exploration. When a fund prices a package of producing assets, the difference between a digitised log set and a box of paper is the difference between a defensible valuation and a guess, and the cost of getting it wrong is measured against the size of the deal. This segment is small in headcount and disproportionate in willingness to pay, because the product is cheap relative to the mistake it prevents.

The third is the national oil companies, and they are the structural giant of the demand side. They sit on the largest paper and microfilm archives on the planet, often the only record of basins that have been producing for generations, and their job is institutional memory: digitisation is how decades of subsurface knowledge survives a retirement wave. They are the largest share of the obtainable pool by some distance, and also the slowest to convert, because their procurement runs on a clock that no founder controls.

The fourth is the independent operators, the backbone of onshore activity in places like the basins administered by the Texas Railroad Commission. Their job is to compete with the majors on a fraction of the budget, which makes them numerous and price-sensitive in equal measure. A per-seat product at a few hundred dollars a month is exactly the shape that fits them, and there are a great many of them, which is why they are the second-largest slice of the obtainable target even though no single account is large.

The fifth is the newest and, in 2022, the fastest-growing reason to care about a fifty-year-old log: the ESG and carbon-storage teams. Their job did not exist when most of these logs were recorded. To qualify a depleted reservoir or a saline aquifer for carbon dioxide injection, an engineer needs the petrophysical properties that only the old logs hold, and the broader survey of where machine learning is being pulled into upstream work shows storage screening and reservoir characterisation as exactly the kind of task the field was beginning to automate at the time [4]. This segment is small today and strategically large tomorrow, which is a different thing from urgent.

Reading the fan-out: who converts first, not who is biggest

Lay those five segments against the obtainable target and the seat price, and the order in which they should be approached stops being a matter of taste. The instrument below fans the six point seven billion dollar serviceable market out into the five segments, threads the hundred and eighty million dollar obtainable target across them as the orange band, and lets you advance a readiness front from segment to segment. It converts each segment's slice into the number of 1,200 dollar seats a team would actually have to close, and it flags which segments are the three signed letters of intent already in hand.

The lesson the fan-out makes visible is that the biggest pool is not the first sale. National oil companies hold the largest obtainable slice, but they sit behind procurement cycles that a young company cannot wait out, so converting them first would starve the company before the contract closes. The segments that convert first are the ones where a signed letter of intent already proves the urgency: the explorers who need the offset log this quarter and the diligence teams who cannot price a deal without it. The readiness front, walked left to right, is therefore not the order of size. It is the order of how soon real money clears at the seat price, and the three letters of intent are the evidence that the order is real rather than wished for.

How the segments quietly shape the product, not just the pipeline

The reason this segmentation matters beyond the order of sales calls is that each segment pulls the product in a slightly different direction, and you cannot serve all five with the same first version. Explorers want throughput and a fast turnaround on a handful of offset wells, so the product they need is batch digitisation with a tight quality bar on the few curves that drive an interpretation. Diligence teams want auditability above all, a record of exactly how each value was extracted, because the output has to survive a data room. National oil companies want scale and security, the ability to run across hundreds of thousands of logs inside their own walls, which is a procurement and deployment problem as much as a modelling one. Independents want a price point and a self-serve motion that does not require a sales engineer per account. Storage teams want specific petrophysical curves, density and neutron porosity above all, rendered with enough fidelity to feed a containment model.

This is the part of the analysis that a single market number can never give you. The system we built to do the extraction, which we call VeerNet, has to be one product, but the roadmap that decides which capability ships first is a direct read of which segment converts first. Because the urgent, signed segments are the explorers and the diligence teams, the early product weights throughput and auditability over the at-scale, in-tenant deployment that the national oil companies will eventually require. The demand-side segmentation is not a marketing artefact bolted onto a finished product. It is the thing that tells engineering what to build in what order, and it does so only once the obtainable market has been broken into the five buyers who will actually pay for it rather than left as one comfortable number on a slide [3].

What the five-way cut changes about the plan

The discipline here is to refuse the convenience of the single number twice over. The first refusal, sizing honestly down to an obtainable slice, we have made before. The second, the one this piece is about, is to refuse to treat that obtainable slice as homogeneous, because the moment you do, you lose the only information that tells you where to start. Five segments, five jobs, five conversion speeds, and three of them already carrying a signed letter of intent: that is a far more useful object than a hundred and eighty million dollar target, because you can act on it tomorrow and you can hold a team to it. The largest segment is the prize you grow into. The urgent, proven segments are the ones that keep the company alive long enough to reach it, and knowing which is which, by name, is the whole point of looking at demand instead of just at size.

The demand-side cut that tells you where to start

1. 1A total addressable market tells you a category is large; it does not tell you who buys first. The honest demand-side move is to fan the obtainable market out into named buyer segments with different jobs and different conversion speeds, because a single number hides the only information a sales team can act on Monday.
2. 2Raster well-log digitisation has five distinct buyer segments hiring the same curve extraction for five different jobs: explorers chasing a play on a lease-sale deadline, private-equity diligence teams pricing an acquisition, national oil companies preserving the largest paper archives, independent operators competing on a thin budget, and ESG and carbon-storage teams qualifying reservoirs for injection.
3. 3Pool size and conversion speed are different axes. National oil companies hold the largest share of the hundred and eighty million dollar obtainable target but convert slowest behind long procurement cycles; explorers and diligence teams are smaller slices that convert first because their job is on a deadline. The three signed letters of intent point the motion at urgency, not size.
4. 4Each segment pulls the product in a different direction: throughput for explorers, auditability for diligence, scale and in-tenant security for national oil companies, a self-serve price point for independents, and specific petrophysical curves for storage teams. The order in which segments convert is the order in which capabilities should ship in VeerNet.
5. 5Refuse the single number twice: size down to an obtainable slice, then refuse to treat that slice as homogeneous. Five segments, five jobs, five speeds, three already proven by a signed letter of intent, is a plan a team can be held to, where a single hundred and eighty million dollar figure is not.

The market sizing tells you the prize is worth chasing. The segmentation tells you which door to knock on first, and the answer is never the biggest room. It is the one where someone is already waiting with a pen.

References

[1] Moore, G. A. Crossing the Chasm: Marketing and Selling Disruptive Products to Mainstream Customers. HarperBusiness (1991, 3rd edition 2014). The beachhead-segment argument that a new product wins by dominating one narrow, urgent buyer segment before broadening, which is why a single addressable market must be decomposed into segments that adopt at different speeds. https://www.harpercollins.com/products/crossing-the-chasm-3rd-edition-geoffrey-a-moore

[2] Christensen, C. M., Hall, T., Dillon, K., and Duncan, D. S. Know Your Customers' Jobs to Be Done. Harvard Business Review, September 2016. The jobs-to-be-done framing that buyers in the same category hire a product for different underlying jobs, so a demand-side segmentation should be cut by the job each segment is trying to get done. https://hbr.org/2016/09/know-your-customers-jobs-to-be-done

[3] Skok, D. How to Calculate Your TAM, SAM and SOM. For Entrepreneurs / Matrix Partners (accessed 2022). The operator framing that only the serviceable obtainable market should drive a plan, which forces a founder to allocate that obtainable slice across the segments that will actually produce it. https://www.forentrepreneurs.com/

[4] Koroteev, D., and Tekic, Z. Artificial Intelligence in Oil and Gas Upstream: Trends, Challenges, and Scenarios for the Future. Energy and AI, Volume 3 (2021). The survey of where machine learning is being adopted across upstream oil and gas, which maps the demand context the digitisation segments sit inside. https://www.sciencedirect.com/science/article/pii/S2666546820300446