Favorable reservoir evaluation method based on one class support vector machine

Real oil reservoirs are of strong heterogeneity, there are few samples of high-yield wells and their data are of high value. In order to make full use of effective information of high-yield wells, this paper carried out unsupervised learning on the geological characteristics in the geological model of high-yield well by virtue of one class support vector machine (OCSVM). Then, the optimal decision function was obtained by optimizing the super parameters of the model. Based on this function, the distribution of possible high-yield zones of heterogeneous oil reservoirs was determined and accordingly the favorable reservoirs were determined, so as to provide guidance for oil reservoir development and well deployment. Case study results indicate that the correlation coefficient between OCSVM decision function value and production rate is higher, so the decision value calculated by means of OSCVM can effectively determine the distribution of high-yield zones in the whole area and accordingly determine favorable reservoirs when there are fewer well samples.