Stimulation techniques are performed to improve the well productivity in fields where a large number of wells is present. However, the selection of candidate wells for stimulation treatments, in particular acidizing and hydraulic fracturing, is a challenging task. The conventional candidate selection technique that is based on analyzing manually a large volume of data is not efficient and cannot capture the impact of all parameters correctly. This study was carried out with the aim of developing a decision-making tool (FIScreT tool) based on Fuzzy logic that would allow the selection of candidate wells for stimulation to be automated rather than manually examining the performance of each well. The FIScreT tool uses a simple and efficient workflow that initially consists of eliminating problems related to completion status, reservoir characteristics and well integrity. Subsequently, a selection is made through two fuzzy interference systems using accessible production and reservoir data. Finally, the production gain is estimated in order to assess the wells’ productivity after treatment. 1. INTRODUCTION: Well stimulation refers to any treatment carried out to restore or improve the productivity of an oil/gas well. Its main objective is to improve the economics of oil fields through faster and greater deliverability of hydrocarbons without major investment (Michael J. Economides, 2000). In addition to reservoir performance and the quality of completion, the effectiveness of the treatment depends on three essential, interrelated elements: the selection of the candidate well, the treatment design, and the execution of the field operation (Zoveidavianpoor et al., 2012)(See Fig. 1.) Applying the best treatment design and effective field operation to the wrong well will result in the failure of the entire operation. This implies that selecting the most appropriate wells for stimulation is one of the greatest challenges affecting reservoir production and field economic development.