PEI Validation
PEI is designed to challenge the quality, consistency, applicability and uncertainty of an engineering analysis before the result is used to support a technical decision.
Validation principle
A language model can produce a convincing answer without proving that the answer is technically correct. PEI therefore treats validation as a separate engineering discipline rather than as a by-product of generated output.
Reliability should be informed by observable factors such as data quality, analytical suitability, independent agreement, sensitivity, uncertainty and professional review.
Validation basis
Validation workflow
Validation is a sequence of challenges rather than a single confidence score. Each stage addresses a different potential source of technical error or uncertainty.
Confirm that the information supporting the analysis is complete enough, internally consistent and suitable for the intended engineering decision.
Checks
Confirm that the analysis has been performed within the appropriate physical, data and methodological conditions.
Checks
Check the integrity of the calculation and confirm that the reported result can be reproduced from the stated inputs and method.
Checks
Where practical, compare the result with an independent calculation, alternative method, historical behaviour or another relevant engineering reference.
Checks
Determine whether reasonable changes in important inputs or assumptions materially affect the result or the engineering decision.
Checks
Identify evidence, observations or analytical results that conflict with the current interpretation and determine whether they require further investigation.
Checks
Characterise material uncertainty arising from data quality, assumptions, model limitations and the engineering analysis itself.
Checks
Present the validated analysis for qualified engineering review before consequential technical decisions or field actions are taken.
Checks
Validation dimensions
A result can be mathematically correct but still unsuitable for a particular engineering decision. PEI therefore evaluates several dimensions rather than reducing technical reliability to one number.
How complete, reliable, consistent and relevant is the information supporting the analysis?
Is the analysis appropriate for the physical problem and the conditions under which it is being applied?
Can the reported result be reproduced and independently checked?
Do independent sources, measurements, historical behaviour or alternative analyses support the result?
Does reasonable variation in important inputs materially change the result or decision?
Are the important limitations and uncertainties understood well enough for the intended use?
Reliability assessment
A future PEI reliability assessment should record the factors supporting or weakening an engineering result. This is fundamentally different from asking an AI model how confident it feels about its own answer.
Assessment factors
Engineering example
Consider a calculated productivity index. The arithmetic may be correct, but the engineering conclusion may still depend on the quality of pressure and rate data, pressure conditions, the selected flow model, wellbore effects and the assumptions behind the analysis.
Validation asks not only whether the calculation was performed correctly, but whether the result remains defensible for the decision being considered.
Validation discipline
An engineering intelligence system should not be rewarded for producing an answer at all costs. When evidence is inadequate, the correct engineering behaviour may be to identify what is missing, explain the limitation and request further investigation.
Engineering assurance
Validation provides structured challenges around an engineering analysis, but consequential technical decisions remain subject to qualified engineering review and professional judgement.
Where the evidence is incomplete, contradictory or outside the applicable range of the analysis, the appropriate outcome may be further investigation rather than a definitive recommendation.