Delineating the distribution of oil and natural gas resources is a prerequisite of exploitation. The delineation methods usually include conventional techniques of reservoir evaluation and mathematical models. The conventional reservoir evaluation results generally depend on the experts' knowledge and experience in the field. The mathematical methods mostly require accurate models to be proposed. Considering spatial relationships and characteristics of geological reservoir problems, including nonlinearity, complexity and uncertainty, a novel model called geospatial case-based reasoning for oil-gas reservoir evaluation was proposed in this article. The key components of the new model, including: (1) the joint representation of spatial relationship and attribute features; (2) the model of spatial relationship and attribute similarity joint reasoning; and (3) the methods of establishing weights for the spatial relationship and attribute features, are completely constructed. A case study of the proposed model for gas reservoir evaluation was carried out. Compared with the backpropagation artificial neural network (BP-ANN) and the geological empirical evaluation (GEE) methods, the model presented in this article performs6.38% and 46.81% better than BP-ANN and GEE, respectively. Furthermore, its execution is simpler, more convenient, and importantly, its utilization hardly requires any professional knowledge of the field.

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