An important aspect in corrosion prediction for oil and gas wells and pipelines is to obtain a
realistic estimate of the corrosion rate. Corrosion rate prediction involves developing a predictive model
that utilizes commonly available operational parameters, existing lab/field data and theoretical models to
obtain realistic assessments of corrosion rates. The Case-based Reasoning (CBR) model for CO2
corrosion prediction is designed to mimic the approach of experienced field corrosion personnel. The
model takes knowledge of corrosion rates for existing cases and uses CBR techniques and Taylor series
expansion to predict corrosion rates for new fields having somewhat similar parameters. The corrosion
prediction using CBR model is developed in three phases: case retrieval, case ranking, and case revision.
In case retrieval phase, the database of existing cases is queried in order to identify the group of cases
with similar values of critical corrosion parameters. Those cases are ranked in the second phase, using a
modified Taylor series expansion of the corrosion function around each case. The most similar case is
passed to the third phase: case revision. The correction of the corrosion rate by using a mechanistic
corrosion model is utilized in order to predict the corrosion rate of the problem under consideration. The
(CBR) model has been implemented as a prototype and verified on a large hypothetical case database
and a small field database with real data.