Aiming at the forecast of oilfield development indexes, a fuzzy neural networks model is proposed that includes input layer, fuzzification layer, rules layer, and output layer. The Gauss function is applied in fuzzification layer, and each node in rules layer corresponds to a fuzzy logic rule. The adjustable parameters of proposed model include the fuzzy set parameters and the weight value of output layer. For determining these parameters, an improved quantum particle swarm optimization is presented. With forecast of moisture content as an example, the experimental results show that this method is effective and feasible.