Research On Establishment And Application Of Drilling Parameter Optimization Model Based On Big Data And Intelligent Algorithms

The ROP is one of the key factors affecting the drilling cycle and operating cost,and it is also an intuitive reflection of the overall level of drilling operations.Over the years,experts,scholars and engineering technicians in the field of drilling have been committed to the improvement of ROP.Many models have been established in terms of rock physical and mechanical properties,drilling technology and equipment capabilities,drilling fluid rheological properties,and drilling engineering parameters.Basically,it is based on experience and logical reasoning.The modeling and solving process is relatively complicated,and the consideration of influencing factors is also very limited.In recent years,with the development of big data and artificial intelligence technology,drilling operation decision-making has shown a trend from experience-driven and logic-driven to data-driven,which brings new technical ideas for comprehensively considering multiple factors to increase ROP.This article is based on this development trend that a drilling parameter optimization model and application research based on big data and intelligent algorithms are proposed.Based on the investigation of the drilling data acquisition,transmission and storage process,the thesis firstly provides a method for preprocessing of drilling big data volume,and realizes the selection of model input parameters based on mutual information analysis;secondly,comprehensive consideration of blocks,strata,and rocks based on the large data sources of drilling in specific research blocks,a clustering analysis algorithm model based on logging data is established,and the research blocks are divided into multiple categories according to the differences in geological characteristics;Partitioning,using drilling engineering parameters as the data source,established an intelligent drilling rate prediction model based on the improved BP neural network;then,using the drilling mechanical drilling rate intelligent prediction model as input to establish the minimum unit footage cost and the specific energy of the drill bit.And other multi-objective optimization models,the tolerant stratified sequence method and particle swarm algorithm are used to solve the optimization model to realize the optimization of drilling parameters.Finally,based on the establishment of the model,a set of logging,mud logging,and drilling data is developed.The drilling parameter intelligent optimization system of the data source is inverted for specific research blocks.The results show that the model established in this paper can achieve the goal of increasing the drilling speed of drilling machinery,and the drilling parameter intelligent optimization model has a short modeling period.There are many considerations and the model is solved quickly.The content studied in this paper has certain reference significance for realizing the data-driven intelligent decision-making of drilling operations under the background of big data,and promoting the development of informatization,automation and intelligence of drilling operations.