Adaptive multi-resolution graph-based clustering algorithm for electrofacies analysis*
Wu Hongliang 1, Wang Chen 3, Feng Zhou 1, Yuan Ye 1, Wang Hua-Feng?2,3, and Xu Bin-Sen 1
1. Research Institute of Petroleum Exploration & Development, PetroChina, Beijing 100083, China.
2. School of Computer Science and Technology, North China University of Technology, Beijing 100144, China.
3. College of Software, Beihang University, Beijing 100191, China.
Abstract：
Logging facies analysis is a signifi cant aspect of reservoir description. In particular,as a commonly used method for logging facies identifi cation, Multi-Resolution Graph-based Clustering (MRGC) can perform depth analysis on multidimensional logging curves to predict logging facies. However, this method is very time-consuming and highly dependent on the initial parameters in the propagation process, which limits the practical application effect of the method. In this paper, an Adaptive Multi-Resolution Graph-based Clustering (AMRGC) is proposed, which is capable of both improving the efficiency of calculation process and achieving a stable propagation result. More specifically, the proposed method, 1) presents a light kernel representative index (LKRI) algorithm which is proved to need less calculation resource than those kernel selection methods in the literature by exclusively considering those “free attractor” points; 2) builds a Multi-Layer Perceptron (MLP) network with back propagation algorithm (BP) so as to avoid the uncertain results brought by uncertain parameter initializations which often happened by only using the K nearest neighbors (KNN) method. Compared with those clustering methods often used in image-based sedimentary phase analysis, such as Self Organizing Map (SOM), Dynamic Clustering (DYN) and Ascendant Hierarchical Clustering (AHC), etc., the AMRGC performs much better without the prior knowledge of data structure. Eventually, the experimental results illustrate that the proposed method also outperformed the original MRGC method on the task of clustering and propagation prediction, with a higher effi ciency and stability.
2020, Vol. 17 
