Moisture content of the well oil is a key production variable in the oilfield

and it has great significance for improving the oil production efficiency by timely and accurate measurement of it. In order to overcome some deficiencies of the traditional manual measurement

the soft sensor technology is introduced to establish a soft sensor model based on automatic spectral clustering - multiple extreme learning machines (ASC-MELM). An automatic spectral clustering (ASC) algorithm is proposed and an improved firefly algorithm (FA) is applied to reach an optimal selection of the clustering number and the scale parameter. The proposed improved firefly algorithm (IFA) adopts a mechanism of jumping out of the current solution at a certain probability

which can avoid the deficiency of falling into the local optimal solution earlier of the traditional FA. For different training subsets after the clustering

the multiple extreme learning machines (ELM) are adopted to establish the different sub-models

in which IFA is used to reach an optimal selection of the hidden layer input weights

the hidden layer biases and the number of the hidden layer nodes. Finally

the output is obtained by calculating the weighted average of multiple sub-models. An application example of an oil well in a domestic oilfield is given. The simulation results show that the proposed method has better predicted accuracy and it is reasonable and effective to realize the soft sensor for moisture content of the well oil.