유전 탐사를 위한 프론티어 AI

OVERVIEW

AI is changing how oil is found, and prices are being set before the evidence is in. This edition classifies 23 public AI claim records by evidence class, examines who may lawfully train a model on licensed seismic data, sets out every vendor and operator disclosure, and works through all 31 deals, contracts and financings since 2022 with the few prices anyone disclosed. It then gives the working tools an investment committee needs: a scorecard covering eighteen vendors, a break-even worksheet, a sixteen-risk register and 49 diligence questions. Where independent evidence does not exist, the edition says so and shows what was searched.

WHAT THIS EDITION COVERS

Fourteen parts: where AI enters the chain from seismic survey to first oil; what "frontier" means, sorted by maturity; an evidence ledger of 23 public claim records; who owns what the models learn from; vendors and what their numbers include; operators' in-house programmes and the computing arms race; the transactions and the few disclosed prices; seven market-size estimates reconciled; the economics of a better prospect; the rules on reserves, AI law, chips and sovereignty; a sixteen-risk register; geothermal, minerals and carbon storage; the investment case; and twelve recurrent interpretation pitfalls.

WHO THIS IS FOR

Equity analysts covering oilfield services and seismic, venture and growth investors, private-equity and infrastructure funds, corporate development teams at oil and national oil companies, and lenders against reserves.

WHAT YOU GET

• Six evidence classes applied to every claim, including one we looked for and did not find
• An evidence ledger of 23 public AI claim records, classified
• The data-rights analysis: who may train a model on licensed seismic, across six release regimes
• All 31 deals, contracts and financings since 2022, with disclosed prices and their limits
• Tool 1 · Vendor Scorecard - eighteen vendors against five tests
• Tool 2 · Break-even Worksheet - what a product must achieve to pay for itself
• A sixteen-risk register: who carries each risk, and what to ask
• A 49-question diligence checklist with pass or concern fields
• Registers of 39 specialist companies and 117 SEC filings

Table of Contents

Executive summary

The thesis, the numbers that frame it and the seven findings

Part 0 The Map

• Where AI enters the chain from survey to first oil

Part 1 What "Frontier" Means

• The technology, sorted by maturity

Part 2 The Evidence Ledger

• 23 public claim records, classified

Part 3 The Data Is the Moat

• Who owns what the models learn from

Part 4 The Vendors

• Capability, disclosure and what the numbers include

Part 5 The Operators

• In-house programmes and the computing arms race

Part 6 Deals and Valuation

• The transactions, and the few prices anyone disclosed

Part 7 Sizing the Market

• Seven estimates, three quantities, one reconciliation

Part 8 The Economics of a Better Prospect

• Where the value would come from

Part 9 The Rules

• Reserves, AI law, chips and sovereignty

Part 10 The Risk Register

• Sixteen risks, who carries each, and what to ask

Part 11 Beyond Oil

• Geothermal, minerals and carbon storage

Part 12 The Investment Case

• Where exposure can be owned, and how to underwrite it

Part 13 Pitfalls

• Twelve recurrent interpretation risks illustrated by the record

Tool 1 The Vendor Scorecard

• Eighteen vendors, five tests

Tool 2 The Break-even Worksheet

• What a product must achieve to pay

The Diligence Checklist

• 49 questions, tear-out

Appendices

• Timeline, glossary, method, sources and registers
• 2000–2026 timeline; method and limitations; 333 sources; 39 companies; 117 SEC filings