Decision-making has always been a challenging matter, and this holds true in the oil and gas industry. Selecting candidate wells for shut-in is one example, which becomes even more challenging when it is for an extended period. Long-term shut-ins can be caused by uncontrollable factors such as environmental reasons or safety considerations. The oil industry also turned to shut-ins as a method to mitigate the impact of economic downturns. The petroleum industry recently experienced this problem due to the COVID-19 pandemic, which led to around a year of extremely low prices. Many small and large companies resorted to shutting their wells for extended periods to cope with the economic downturn. The issue with such practices is that there is no concrete standard to select the most optimal candidates. The decisionmaking process usually involves analysis-based techniques that can be timeconsuming. This study aims to utilize fuzzy logic in assisting with the real-time decision-making challenge of selecting candidate wells for extended shut-ins. Fuzzy logic implementation in the petroleum industry has been widely embraced, improving processes in different areas like reservoir characterization, drilling operations, and decision-making problems. That is done thanks to fuzzy logic’s ability to resemble human-like thinking and account for the uncertainties and vagueness inherent in the subsurface systems. A fuzzy expert system was built using Python to select the best candidates for shut-in based on the net present value (NPV). The system was designed using simulation data of a mature field producing from a sandstone reservoir. The workflow of the system can be summarized in three parts. The first clusters the wells into three distinct groups based on performance, then the second part recommends the shut-in scenario for the clusters based on the economic conditions. The last component makes the final shut-in decision based on the results of the two previous parts. Exploratory data analysis techniques and earlier studies were used as guidelines to build the system. An economic analysis of a 3-year forecast was conducted for several scenarios to validate the results of the system. This study demonstrates the efficiency and convenience of fuzzy inference systems when used in real-time decision-making problems. However, adjustments are needed when applying it to different fields and reservoirs, as the production behavior can differ greatly.