Oil and gas construction projects are of great importance to support and facilitate the process of operation and production. However, these projects usually face chronic risks that lead to time overrun, cost overrun, and poor quality, affecting the projects’ success. Hence, this study focused on identifying, classifying, and modeling the risk factors that have negative effects on the success of construction projects in Yemen. The data were collected through a structured questionnaire. Statistical analysis, relative important index method, and probability impact matrix analysis were carried out to classify and rank the risk factors. The partial least squares path modeling or partial least squares structural equation modeling (PLS-PM, PLS-SEM) is a method for structural equation modeling that allows an estimation of complex cause–effect relationships in path models with latent variables. PLS-SEM was employed to analyze data collected from a questionnaire survey of 314 participants comprising the clients, contractors, and consultants working in oil and gas construction projects. The results showed that the goodness of fit index of the model is 0.638. The developed model was deemed to fit because the analysis result of the coefficient of determination test (R2) of the model was 0.720, which indicates the significant explanation of the developed model for the relationship between the causes of risks and their effects on the success of projects. The most impacted internal risk categories include project management, feasibility study design, and resource material availability. The main external risk elements include political, economic, and security considerations. The created risk factor model explained the influence of risk factors on the success of construction projects effectively, according to statistical and expert validation tests.