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Acid fracture treatment is one of the most effective way of stimulation in fractured and cavernous carbonatite reservoir. Because there are too many factors that influence the acid fracture effect, such as geologic factor, technic factor, material's factor, it is difficult to forecast the effect of acid fracture before the treatment has been done. The relationship between these factors and result of acid fracture is very complex, and it is different from hydraulic fracture in clastic reservoir . Although much more experience has been gathered from exercise, the relationship between affect factors and effect is also very difficult to be found. This paper designs two ways to solve this problem. One way is based on experts' experience and statistical method, and, another way is artificial neural network. By using practical data, and compared these two ways, we find that the precision ratio of the first way is 66 percent and another is 86 percent. Both of these two ways are suitable to forecast the effect of acid fracture in fractured and cavernous carbonatite reservoir. Furtherly, the artificial neural network is better.