Fuzzy expert system for assessing the quality of well completion in complicated geological conditions

This study presents an expert system utilizing fuzzy logic to evaluate well completion quality in geologically intricate environments, exemplified by the diverse oil and gas resources of Azerbaijan.Traditional evaluation methods, often dependent on expert judgment and inconsistent field evaluations, struggle with confusing and qualitative input data.To address this issue, we developed a fuzzy inference model incorporating critical geological and operational variables like as perforation density, reservoir pressure gradient, completion fluid compatibility, mud loss severity, and formation permeability.To establish a fuzzy rule basis derived from field experience and expert knowledge, these characteristics were transformed into linguistic variables.The Completion Quality Index (CQI) is a quantifiable and interpretable measure of completion efficacy, serving as the model's output.A combination of synthetic and empirical field data was employed to evaluate the system, and the findings indicate that the model can facilitate decision-making by generating outcomes that are dependable, flexible, and comprehensible to people.The proposed technique enhances the reliability of well completion quality evaluation under uncertainty, offering engineers a potentially valuable resource under difficult drilling conditions.