This paper studies the problem of correlated data gathering in wireless sensor networks. For a maximum network utility, a efficient data capture and transmission framework is proposed for correlated sources, where localized S-W source coding, network coding based flow control and opportunistic routing are jointly optimized. To increase the throughput and guarantee the decodability simultaneously, a dynamic network coding strategy is proposed, with which the intermediate node can easily decide whether to make a combination among the incoming flows. Also, an opportunistic routing approach is presented, which adopts a new metric (minimum congestion price) for forwarding node selection and results in a maximum utility benefit for the sensor nodes. Through the Lagrange dual and gradient approach, a fully distributed algorithm is represented. And the convergence and performance are validated by the numerical results.