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By analyzing the relation between mud logging data, well logging data and formation drillability, a novel method for predicting formation drillability based on particle swarm optimization and support vector machine (PSO-SVM) is proposed. The prediction model for formation drillability is established using the data of drilling pressure, rotary speed, hydraulic horsepower, bottom hole differential pressure,...
This paper proposes an algorithm which combines Particle Swarm Optimization (PSO) with Least Squares Support Vector Machines (LSSVM) to identify lithology by using well logging data. First of all, PSO is used for optimizing the main parameters of LSSVM, and then by using the optimized parameters to obtain a better PSO-LSSVM classification model which can be used to identify lithology with logging...
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