Original research · 2026-07 edition

AI SEO Statistics: Oil and Gas (2026-07 edition)

40 questions · 119 AI responses · 3 models · measured 2026-07-06

See how AI talks about YOUR businessFull data table

The question bank

The questions we tested — sampled from real buyer journeys in oil and gas.

Each model answered every question once, same wording, same day. These are the prompts behind every percentage on this page.

What are the key differences between API 6A and API 6D valve manufacturers for high-pressure systems?
How do I know if my refinery's heat exchanger needs a full retube or just a chemical cleaning?
What's the average lead time for custom-fabricated pressure vessels in the current market?
Is it cheaper to hire a specialized pipeline integrity firm or do in-house inspections with our own pigging equipment?
What specific safety certifications should I look for when hiring a third-party contractor for offshore rig maintenance?
How do I evaluate the safety record and TRIR of an oilfield service provider before signing a long-term contract?
What are the warning signs that a subsea equipment manufacturer is cutting corners on material quality?
Can I find a local machining shop that can handle NACE-compliant materials for sour gas environments?
Show all 40 questions
How much does it typically cost to decommission a small onshore well site in the Permian Basin?
What are the pros and cons of using a modular skid manufacturer versus building on-site for a new gas plant?
What should I include in an RFP for a digital twin implementation of an existing midstream facility?
How do I compare the long-term ROI of different centrifugal pump manufacturers for crude oil transport?
Why is my current maintenance contractor taking so long to source spare parts for legacy turbines?
Are there any specialized firms that handle emergency blowout preventer repairs on a 24-hour notice?
What questions should I ask a potential EPC contractor about their supply chain resilience for long-lead items?
How do I verify if a manufacturer's low-carbon steel claims are actually verified by a reputable third party?
What is the standard day rate for a senior petroleum engineer consultant for an enhanced oil recovery project?
Should we switch from manual ultrasonic testing to automated phased array for our pipeline weld inspections?
How do I find a service provider that specializes in brownfield optimization for aging offshore platforms?
What are the major red flags in a bid for a multi-million dollar refinery turnaround project?
How can I tell if a valve repair shop is actually authorized by the original equipment manufacturer?
What is the price difference between renting and buying a fleet of hydraulic fracturing pumps for a 6-month project?
Who are the top-rated firms for environmental remediation after a minor pipeline leak?
How do I vet a remote monitoring service for unmanned wellhead sites in remote areas?
What technical specs do I need to provide for a custom manifold fabrication quote to get an accurate price?
Is it worth paying a 20 percent premium for a contractor with a zero-incident safety record over five years?
How do I transition from reactive to predictive maintenance without shutting down production for weeks?
What are the logistical challenges of hiring an international manufacturer for a project based in the Gulf of Mexico?
How do I compare the throughput efficiency of different gas dehydration units before making a purchase?
What specific clauses should I look for in a Master Service Agreement with a new drilling contractor?
How can I verify the metallurgical test reports provided by a new overseas pipe supplier?
What is the typical markup for field labor on an emergency repair call-out during a holiday weekend?
Are there any firms that offer performance-based contracts for optimizing artificial lift systems?
How do I handle a situation where a critical component manufacturer for our refinery goes out of business?
What is the best way to audit a waste management contractor for oilfield fluids to ensure compliance?
How do I choose between a local boutique engineering firm and a large global consultancy for a pipeline expansion?
What are the common hidden costs in a fixed-price contract for modular refinery units?
How do I find contractors who are experienced with hydrogen blending in existing natural gas infrastructure?
What are the specific insurance requirements I should demand from a subsea diving contractor for deepwater work?
How do I evaluate the software compatibility of a new flow meter manufacturer with our existing SCADA system?

Model by model

22-point average divergence: which AI you ask changes the answer.

The divergence index is the average gap between the most and least likely model per behavior. Higher = the models disagree more about oil and gas buyers.

Behavior rates across 40 oil and gas buyer questions, 2026-07 edition. Last column: average across models.
ChatGPTClaudeGeminiConsensus
Recommends hiring a professional62%28%33%49%
Suggests DIY first28%8%18%80%
Names specific providers13%23%23%72%
Gives price or cost info13%15%18%85%
Tells to check reviews13%5%0%87%
Tells to verify credentials36%30%20%64%
Mentions case studies / portfolio23%18%3%69%
Mentions local proximity28%33%13%62%
Gives selection criteria44%48%50%46%
Warns about red flags15%5%20%82%
Asks a clarifying question49%33%0%39%
Recommends multiple quotes13%8%3%77%

By model

How each assistant handled Oil and Gas questions.

Reading the 119 answers model by model shows how differently the three assistants treat the same oil and gas questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 61.5% (ChatGPT) down to 27.5% (Claude), a 34-point gap on an identical question set.

Across the 39 oil and gas answers it produced, ChatGPT recommended hiring a professional in 61.5% of them and suggested a DIY approach first 28.2% of the time. It named a specific provider in 12.8% of answers (about 0.7 distinct providers per answer) and included price or cost information 12.8% of the time. ChatGPT asked a clarifying question before answering in 48.7% of cases, warned about red flags or scams in 15.4%, and told the buyer to verify credentials in 35.9%, averaging 751 words per answer. On the remaining cues it told the buyer to check reviews in 12.8%, pointed to case studies or a portfolio in 23.1%, and framed the choice around local proximity in 28.2%; a selection-criteria checklist appeared in 43.6% of its answers and a recommendation to gather multiple quotes in 12.8%.

Across the 40 oil and gas answers it produced, Claude recommended hiring a professional in 27.5% of them and suggested a DIY approach first 7.5% of the time. It named a specific provider in 22.5% of answers (about 1 distinct providers per answer) and included price or cost information 15% of the time. Claude asked a clarifying question before answering in 32.5% of cases, warned about red flags or scams in 5%, and told the buyer to verify credentials in 30%, averaging 330 words per answer. On the remaining cues it told the buyer to check reviews in 5%, pointed to case studies or a portfolio in 17.5%, and framed the choice around local proximity in 32.5%; a selection-criteria checklist appeared in 47.5% of its answers and a recommendation to gather multiple quotes in 7.5%.

Across the 40 oil and gas answers it produced, Gemini recommended hiring a professional in 32.5% of them and suggested a DIY approach first 17.5% of the time. It named a specific provider in 22.5% of answers (about 1.1 distinct providers per answer) and included price or cost information 17.5% of the time. Gemini asked a clarifying question before answering in 0% of cases, warned about red flags or scams in 20%, and told the buyer to verify credentials in 20%, averaging 224 words per answer. On the remaining cues it told the buyer to check reviews in 0%, pointed to case studies or a portfolio in 2.5%, and framed the choice around local proximity in 12.5%; a selection-criteria checklist appeared in 50% of its answers and a recommendation to gather multiple quotes in 2.5%.

Taken together, ChatGPT is the assistant most likely to route an oil and gas buyer to a professional (61.5%) and Claude the least (27.5%). ChatGPT produced the longest answers, at 751 words on average. Specific providers were named most often by Claude (22.5%) — even there, roughly one answer in 4 carried a name.

Where they disagree

The behaviors where the choice of model changes the answer.

The divergence index for this study is 21.7 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant an oil and gas buyer happens to ask matters most:

  • Asks a clarifying question: from 0% (Gemini) to 48.7% (ChatGPT) — a 49-point spread.
  • Recommends hiring a professional: from 27.5% (Claude) to 61.5% (ChatGPT) — a 34-point spread.
  • Suggests a DIY approach first: from 7.5% (Claude) to 28.2% (ChatGPT) — a 21-point spread.
  • Mentions case studies or portfolio: from 2.5% (Gemini) to 23.1% (ChatGPT) — a 21-point spread.
  • Mentions local proximity: from 12.5% (Gemini) to 32.5% (Claude) — a 20-point spread.

The widest single gap — asks a clarifying question, 49 points — means an oil and gas buyer can receive materially different guidance on the same question depending only on which assistant they happen to open, so any visibility strategy built on a single model's behavior describes only part of the oil and gas market.

Where they agree

The points of near-consensus in Oil and Gas.

On other behaviors the three models move almost in lockstep — the points of near-consensus for oil and gas, where all three landed within a few points of each other:

  • Gives price or cost information: 12.8%–17.5% across all three (a 5-point spread).
  • Gives selection criteria: 43.6%–50% across all three (a 6-point spread).
  • Names a specific provider: 12.8%–22.5% across all three (a 10-point spread).
  • Recommends multiple quotes: 2.5%–12.8% across all three (a 10-point spread).

Measured question by question, the three assistants coded a response the same way most consistently on "tells the buyer to check reviews" (identical coding in 87.2% of questions) and least consistently on "asks a clarifying question" (38.5%).

Every behavior, measured

All twelve coded behaviors for Oil and Gas, averaged across the three models.

The behaviors AI models reproduce most often for oil and gas are gives selection criteria (47% on average), recommends hiring a professional (40.5%) and tells the buyer to verify credentials (28.6%); the rarest are tells the buyer to check reviews (5.9%), recommends multiple quotes (7.6%) and warns about red flags or scams (13.5%). Each figure below is the share of a model's 40 answers in which the behavior appeared at least once, averaged across the 3 models with the full per-model range in parentheses:

  • Gives selection criteria: 47% on average (ChatGPT 43.6%, Claude 47.5%, Gemini 50%) — a 6-point spread.
  • Recommends hiring a professional: 40.5% on average (ChatGPT 61.5%, Claude 27.5%, Gemini 32.5%) — a 34-point spread.
  • Tells the buyer to verify credentials: 28.6% on average (ChatGPT 35.9%, Claude 30%, Gemini 20%) — a 16-point spread.
  • Asks a clarifying question: 27.1% on average (ChatGPT 48.7%, Claude 32.5%, Gemini 0%) — a 49-point spread.
  • Mentions local proximity: 24.4% on average (ChatGPT 28.2%, Claude 32.5%, Gemini 12.5%) — a 20-point spread.
  • Names a specific provider: 19.3% on average (ChatGPT 12.8%, Claude 22.5%, Gemini 22.5%) — a 10-point spread.
  • Suggests a DIY approach first: 17.7% on average (ChatGPT 28.2%, Claude 7.5%, Gemini 17.5%) — a 21-point spread.
  • Gives price or cost information: 15.1% on average (ChatGPT 12.8%, Claude 15%, Gemini 17.5%) — a 5-point spread.
  • Mentions case studies or portfolio: 14.4% on average (ChatGPT 23.1%, Claude 17.5%, Gemini 2.5%) — a 21-point spread.
  • Warns about red flags or scams: 13.5% on average (ChatGPT 15.4%, Claude 5%, Gemini 20%) — a 15-point spread.
  • Recommends multiple quotes: 7.6% on average (ChatGPT 12.8%, Claude 7.5%, Gemini 2.5%) — a 10-point spread.
  • Tells the buyer to check reviews: 5.9% on average (ChatGPT 12.8%, Claude 5%, Gemini 0%) — a 13-point spread.

Trust signals

How well the models protect the oil and gas buyer.

Beyond whether to hire, the rubric codes how carefully each assistant protects the oil and gas buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 5.9% of answers on average. Verifying credentials or certifications appeared in 28.6%. Warning about red flags or scams appeared in 13.5%.

On structuring the decision, a selection-criteria checklist showed up in 47% of answers on average and a recommendation to gather multiple quotes in 7.6%. The single least-reproduced protective signal for oil and gas is "tells the buyer to check reviews" at 5.9% on average — the clearest opening for content that supplies it, since the models are not yet reliably surfacing that guidance on their own.

Referral behavior

Do AI models name Oil and Gas providers?

For service providers the decisive question is whether these systems name anyone at all. Across 119 oil and gas answers, a specific provider was named in 19.3% of responses on average — roughly 0.9 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for oil and gas: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.

The question set

What these 40 Oil and Gas questions cover.

The 40 questions behind every percentage on this page were drawn from real oil and gas (manufacturing / industrial B2B; buyer hiring decisions for this specific service) buyer journeys. Each was put to all 3 models once, with identical wording, so the rates above describe how the assistants handled this exact oil and gas question set — not a general prior or a hand-picked subset. The full list is shown earlier on this page; the coded percentages are what those specific questions produced.

How to read this

A note on the numbers.

A percentage here is the share of a model's 40 answers in which the behavior appeared at least once — not a confidence score. Because each model answered every question exactly once on 2026-07-06, the figures describe this specific oil and gas question set and snapshot rather than a general prior. The full protocol and coding rubric are documented in the study methodology.

Methodology

A controlled snapshot, documented end to end.

40 standardized buyer questions per industry, one response per model per question (ChatGPT (gpt-5-mini), Claude (claude-sonnet-5), Gemini (gemini-3-flash-preview)), collected 2026-07-06, coded against a fixed 12-behavior rubric with human QA. AI outputs vary with model version, location and time — figures describe this sample and window, and are refreshed each edition. Read the full methodology →