Automatic identification of drilling fluid loss types based on symbolic aggregate approximation

Currently, the determination of drilling fluid loss types relies on detailed geological engineering information, supplemented by manual analysis, leading to subjectivity and delay in identification. Based on the drilling fluid loss causes and parameter characterization patterns, characteristic curve templates for four types of fluid loss types, that is, fracture, pore, dissolution, and induced fracture, were established, and the characteristic curves were transformed into symbolic sequences using symbolic aggregate approximation (SAX) method. By comparing the SAX string representation of the characteristic curve from the well under investigation with template strings and calculating the similarity, the fluid loss type can be automatically identified based on quantified similarity. Validation results from sample wells show that this method, utilizing logging data directly, can automatically identify the drilling fluid loss types, and achieves an identification efficiency improvement of over 90% compared to traditional manual analysis methods. This approach can be applied to large-scale historical data mining for analysis to guide future drilling operations, or can be applied to real-time fluid loss type judgement to provide scientific basis for selecting plugging measures.