Well surveillance using multivariate thermal measurements

Traditional methods to measure and survey the productivity of oil wells mainly consisted of using test-separator units with expensive instrumental, mechanical, electrical, piping, and safety devices along with technical and protective inspections, repair and operation services, facilities, and infrastructures. Their inherent limitations are time and cost consuming, uncertainty of well isolation in test separator, and need to close the co-line wells, which are diminished using multivariate thermal well testing. In this study, an alternative method is presented using multivariate regression on thermal analysis data. The objective of this study, which covered three distinctive major fields of statistics, thermal analysis, and well testing, is predicting the accurate productivity of oil wells using a single sample point at the blend oil pipeline. This method is based in performing multivariate regression of thermogravimetric data obtained from the samples of Iranian offshore oil wells. The results revealed that the used model appropriate for crude oil blends, which thermal traces significantly differ from each other. The calculated error function corrected the blend equation by considering the eutectic points and catalytic pyrolysis in lower and higher temperatures, respectively. The model predicted the accurate productivity of oil wells in real samples of blend oil pipeline.