Wavelet Transform Processing in Detecting Failures in Offshore Well Production

Abstract

Brazil has a significant offshore oil production, which dates back to the late 1960s and is currently focused on exploring pre-salt reservoirs. The drilling technology Petrobras uses is considered a world standard: in 2020, it allowed offshore production to reach 97% of the country’s total oil production. During the process, however, unwanted events, and even operational failures may occur, which are capable of significant damage. Thus, failure detection is extremely important to prevent production losses or delays, to reduce costs and to avoid accidents. This study uses a real, public database on offshore production, and proposes using wavelet transforms to detect production failures. With the technique, we pinpointed which time intervals between measurements showed relevant variability, and then clustered the data, according to mobile averages, to shrink the record number. Using wavelet transforms, we analyzed which variables could be used as predictors of production failures and identified the temperature read by the Temperature and Pressure Transducer sensor (T-TPT) and the pressure at the Production Choke sensor (P-PCK) as possible predictor variables. We also observed the creation of a filtered series, averaged from the original data series, which maintained its variability, showing the viability of record regrouping in shorter series.