摘要
A model to characterize lithology of volcanic rock reservoirs by using nine types of volcanic rocks was presented to indicate lithologic control over high-quality reservoirs.Based on this model,we selected 15 petrogeophysical logging parameters sensitive to lithology,fabrics,genesis and pore structures of volcanic rocks,and adopted three machine learning algorithms,i.e.multiple regression analysis(MRA),artificial neural network(ANN) and support vector machine(SVM),respectively,to identify lithologic characteristics of volcanic rocks.Taking the Niudong oilfield of the Malang sag in the Santanghu Basin as an example,we employed data from three wells where volcanic rock reservoirs in Well N9-10 and Well N9-19 served as learning samples while that in Well N8-10 as a prediction sample.In particular,1 361 samples from Well N9-10 and 881 samples from Well N9-19 were employed and each of them contained 15 parameters of logging and lithology,the knowledge to predict lithologic characteristics of volcanic rocks could be obtained,respectively,by these algorithms.Then,961 samples from Well N8-10 were used and each sample only had 15 logging parameters,while their lithologic characteristics were gained based on the aforementioned knowledge obtained by learning.The result shows that as for the learning samples,the absolute value of mean relative errors(%) between calculated and field-measured results for MRA,ANN and SVM are 51.84%,48.66% and 0,respectively;and as for the prediction samples,these errors are 52.44%,46.31% and 6.30%,respectively.Therefore,in this case only SVM is applicable because a nonlinear relationship between lithologic characteristics of volcanic rocks and 15 petrogeophysical logging parameters is very strong.