Incorporation of Spatial Characteristics Into Volcanic Fqcies and Favorable Reservoir Prediction

摘要

Compared to clastic reservoirs, volcanic reservoirs exhibit higher heterogeneity. Lithological facies type is one of the most important indicators of favorable volcanic reservoirs. Traditionally, facies are identified by core observation or log classification. However, spatial-distribution characteristics and geological conceptual models, which are important in the early stages of exploration, are seldom incorporated quantitatively in facies prediction. Based on previous work, a new method has been developed to incorporate volcanic spatial information with limited well data (three wells) to improve facies prediction. This method was applied to a volcanic clastic reservoir of the Cretaceous Yingchen member of the Xin-shan fault depression, northeastern China. For better well control, an artificial neural network (ANN), a beta-Bayesian method (BBM), and a discriminant analysis (DA) algorithm, were used to predict log-based facies. Confidence analysis was applied to evaluate the log facies prediction. Analysis of variance (ANOVA) verifies that the overall prediction accuracy is above 82%. Indicator kriging was used to estimate the conditional probabilities of facies occurrence given residual thickness. This is based on the assumption that the residual thickness of the volcanic formation is controlled by distance from the eruption center, a major factor defining the geological facies. The geological conceptual models (areal sedimentary facies maps and diagenetic facies maps) were converted into the conditional probability of facies occurrence in given geological settings using multinomial logistic regression. These conditional probabilities were combined with well-log facies data within a Bayesian framework. Three favorable reservoirs were predicted based on the method above, and the predictions were proved by the subsequent drilling.