Due to the oil price fluctuations in recent decades, international and national oil companies have developed programs of strategically oriented development and assets optimization. Many companies have also promptly promoted opportunities for oilfield joint ventures. Therefore, a fast and accurate assessment methodology of oilfield assets, including planning and costs assessment, is expected to be proposed. Oilfield development cost assessment can be dynamically affected by a number of factors, including oilfield internal indexes and macroeconomic indexes. Based on a machine learning algorithm and combining mathematical and statistical methodology, the Microsoft Azure machine learning studio has been used for modeling the oilfield development cost. The proposed method has adopted three algorithms: a neural network, a boosted decision tree, and a decision forest. Results showed that the boosted decision tree and decision forest algorithms can achieve PFI ranking with stable training results. The results of the machine training model have been analyzed, and they showed that the permutation feature importance (PFI) model can provide reasonable scientific and technical support for oil companies and help to attain more effective and accurate estimation and prediction of oilfield assets.