In Hangjinqi block, Ordos Basin, both benefit exploitation and effective reserve recovery of tight sandstone gas reservoirs mainly rely on horizontal wells by volume fracturing. However, most high- or low-production wells vary greatly in their productivity after fracturing, and failure fracturing intervals with extra-low production just account for 37% contributing to horizontal wells. Therefore, it is essential to make clear the main influential factors on interval selection and their sensitivity, so as to construct one method for quantitatively evaluating and accurately selecting horizontal intervals for fracturing. In this paper, the sensitivity of each factor to fracturing effect was figured out via grey relational analysis, and a fracturing sweetspot model was established by integrating equal weight with BP neural network approaches. With this model, the sweetspot was assessed for 21 horizontal wells. Results show that the key factors controlling the fracturing effect in the study area are geological parameters, including permeability, average total hydrocarbon, gas saturation and brittleness index, in a descending order of sensitivity; and (2) through the established model, the best correlation between comprehensive sweet degree and post-fracturing productivity can be obtained with the correlation as high as 88.71%, compared to 73.35% by one model based on the BP neural network approach and 23.74% by the other model based on the equal weight approach. It is concluded that, boasting not only the stability in the theoretical level but the pertinence in the mathematical aspect, this sweetspot evaluation method can effectively guide the selection of fracturing intervals for horizontal wells in tight sandstone gas reservoirs.
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