An Integrated Approach for Predicting the Downhole Leaks Utilizing Surface Measurements

Estimating the downhole leaks is very important in order to evaluate the well performance and assess the wellbore integrity. Different approaches can be used to estimate the downhole leaks such as temperature surveys, noise logs, and corrosion logs. However, most of the available techniques require downhole measurements which will increase the operational cost and time. This study presents an integrated approach for predicting downhole leaks based on surface measurements, which will reduce the cost and effort significantly. The proposed method was implemented on actual field data, where reasonable accuracy was observed. In this work, the casing leaks were quantified by diagnosing the pressure and flow rate profiles at the surface, without using any downhole flow spinner. Three main steps were used to estimate the downhole leaks; building the inflow performance relationship (IPR) model, predicting the reservoir productivity/injectivity index, and calculating the pressure profiles along the wellbore using the nodal analysis technique. The suggested approach can be used to predict the downhole leaks for production wells as well as injection wells. In this study, the proposed technique was tested for a water injection well, where the fluid leakage was detected and the leak depth was determined. The studied well suffers from severe corrosion problems, that resulted in downhole leakage. For the well under investigation, the leakage rate is 8500 barrels per day (bpd) and the expected leakage depth is around 1374 ft., based on the wellbore survey. Applying the proposed approach showed that the leak rate is 8831 bpd and the leakage depth is 1380 ft. Therefore, the estimations errors are 3.89% in the flow rate and 0.43% in the leakage depth. Overall, an integrated approach is proposed to detect the downhole leaks, the proposed method has been analytically derived and tested using actual field data. The presented method uses surface measurements without a need to stop the production or injection operations. The new method showed very acceptable prediction performance, where estimation errors of 3.89 and 0.43% were obtained for the leakage rate and depth, respectively.