Abstract
With the rebound of international oil prices, complex non-clastic volcanic rock reservoirs have been gaining much attention from the scientific community in recent years. However, due to the complexity of volcanic diagenesis and the intensity of tectonic activity at its development location, volcanic reservoirs generally exhibit strong heterogeneity. In particular, the volcanic rock reservoir in Block Jinlong 2 is a low-medium porosity, ultra low to low permeability reservoir with complex lithology and diverse storage spaces. Therefore, the author proposes a petrophysical rock typing method based on the VGG16 deep learning network and hierarchical clustering, which takes MCP curve images and physical data as input. First, the author uses the learning ability of the convolutional layer and max-pooling layer in the VGG16 network to extract the characteristics of each MICP curve. Second, through the PCA dimensionality reduction method, the feature of each picture is transformed into a low-dimensional feature vector. Then, the distance of the image feature vector and the distance of the physical point are combined into a new distance matrix, which is used as the input of hierarchical clustering. Finally, the scores of the Davies Boulding Index and Sillhouette Coefficient are used to determine the final clustering result. Comparing with the FZI and FZI* methods, this method has a better application effect in Jinlong-2 Block volcanic reservoirs. The volcanic rocks of Jinlong 2 are divided into 4 types. Type 1 has the best reservoir characteristics, type 4 is relatively compact, and type 2 and 3 are in the transition zone, but type 2 will develop some micro fractures to increase reservoir seepage capacity. Such petrophysical rock typing has laid a good foundation for the subsequent reservoir description and reservoir evaluation research of Jinlong-2 Block volcanic reservoirs.
NSTL
Elsevier