Reservoir performance forecasting technology commonly involves flow testing and fluid sampling in appraisal wells. This study introduces a new Gaussian Pressure Transient (GPT) method for rapidly evaluating well-test data. The GPT method provides an alternative for the current rate transient analysis (RTA) methodology, which relies on specialized knowledge and involves subjective steps of tangent line-fitting based on flow regime assumptions. The newly proposed method was validated with field well-test data and is different from the traditional PTA method in that it directly fits Gaussian well performance curves to well-test data. The GPT method can be used (1) to determine the uncertain reservoir parameters, and (2) predict future well productivity under production conditions. Well-test data from an offshore condensate discovery with low well deliverability are analyzed. The test data encompass five individual tests, all revealing low gas and condensate flow rates based on GPT matches. The method involves estimations of the hydraulic diffusivity and can generate both short-term and long-term well-performance forecasts. A sensitivity analysis explores the impact of the various parameters on the recoverable resource volume, identifying which data acquisition would be needed to improve the production forecast's accuracy. Validation with field tests and commercial simulator outputs demonstrates the model's robustness in formation evaluation and predicting well performance. The study recommends hydraulic fracturing for future field development, and includes simulation results valuable for reservoir management and decision-making.