Research on the Optimization of a Drilling Rock Breaking Method Based on Fuzzy Cluster Analysis

Abstract

Improving drilling efficiency is the best way to reduce drilling costs and the choice of the drilling mode is instrumental in doing so. At present, however, a standard approach for the optimization of these processes does not exists yet. Through a comparative statistical analysis of the rock-breaking mechanisms and the characteristics of different drilling methods, this research proposes a set of cues to achieve this objective. Available statistical data are classified by means of a fuzzy cluster analysis according to the anti-drilling characteristic parameters of formation. The results show that different drilling methods rely on their own rock breaking mechanisms and have distinct characteristics. The rotary table drilling method is the most commonly used drilling mode, however, it displays some limitations with regard to deep wells, ultra-deep wells and difficult formations. The combined drilling method has the advantages of both the rotary table drilling and the down-hole power drilling modes. Polycrystalline diamond compact (PDC) drill bits can lead to good results for medium hardness and weakly abrasive formations. Underbalanced drilling for formations with high hardness and strong abrasiveness displays some limitations.

Research on the construction of a visualization platform for customer demand analysis based on big data technology

“…The general steps of the fuzzy cluster analysis method for clustering customer requirements are specification of data, construction of fuzzy similarity matrix, calculation of fuzzy equivalence matrix, and setting the clustering level value classification [27][28].…”