This thesis describes the development and capabilities of the 2020 World Gas Model (WGM), an updated mixed complementarity problem model of the global natural gas market derived from the 2014 World Gas Model. The significance of this research applies to industry professionals and academics alike as the developed processes and analysis further expands the capabilities and flexibility of equilibrium modeling. Through an understanding of the current state of the natural gas market, the WGM determines the economic behavior of various market players with the deployment of Karush-Kuhn-Tucker (KKT) optimality conditions in conjunction with market-clearing conditions. The capabilities of the World Gas Model are highlighted through two case studies that are of varying international importance. The case studies are specifically selected from different issues that face the natural gas market such as a United States and China trade war and U.S. Coast Guard liquefied natural gas (LNG) inspection workforce forecasting. The goal of the United States and China Trade War case study is to analyze the potential long- and short-term affects of a prolonged trade war under several different possible scenarios. Results from the study indicate that while increased tariffs on LNG trade from the U.S. to China greatly reduce the amount of trade volume between the two countries, the overall economic effect is negligible and of greater concern to other affected nations. Another found result, is that if the potential geopolitical consequence of China increasing their domestic production of natural gas in an effort to reduce reliance on imports, this will cause a global natural gas market effect. The U.S. Coast Guard LNG inspection workforce forecasting case study utilizes the WGM to provide the future workforce demand for U.S. regulatory personnel and the associated costs based on the growth of the U.S. LNG industry. The results from the study indicate that in order to avoid future costs and restriction on the U.S. LNG industry, the USCG must increase its LNG inspection workforce by a factor of .3 to 1 from current forecasts.