Real-valued data sequences are often affected by structured noise in addition to random noise. For example, in pressure transient analysis (PTA), semi-log derivatives of log-log diagnostic plots show such contamination of structured noise; especially under multiphase flow condition. In PTA data, structured noise refers to the response to some physical phenomena which is not originated at the reservoir, such as fluid segregation in wellbore or pressure leak due to a brief opening of a valve. Such noisy responses commonly appear to mix up with flow regimes, hindering further reservoir flow analysis. In this paper, we use the Singular Spectrum Analysis (SSA) to decompose PTA data into additive components; subsequently we use the eigenvalues associated with the decomposed components to identify the components that contain most of the structured noise information. We develop a semisupervised process that requires minimal expert supervision in tuning the solitary parameter of our algorithm using only one pressure buildup scenario. An empirical evaluation using real pressure data from oil and gas wells shows that our approach can detect a multitude of structured noise with 74.25% accuracy.