Multi-objective Optimization Of Drilling Parameters Based On Improved Ant Colony Algorithm

With the rapid development of drilling technology,the increasingly complex environment of oil and gas,engineering equipment and testing instrument technology unceasing enhancement,the basis of drilling parameter optimization related disciplines and theory has a great development,the existing single target parameters of drilling parameter optimization method is hard to meet the needs of drilling engineering,therefore,urgent need to introduce new technology and new theory.It is an inevitable trend of drilling optimization technology to realize multi-parameter,multi-objective,interactive and dynamic drilling parameter optimization according to the identification of heterogeneous lithology in drilling rig.The application of multi-objective optimization strategy to guide the setting of control parameters in drilling process is an urgent requirement for the rapidly developing intelligent automatic drilling technology.The research contents of this paper mainly include the following points:Firstly,reasonable drilling parameters need to be selected in order to achieve optimal technical and economic indexes during drilling.Aiming at the existing single objective limitations and disadvantages of drilling parameter optimization,drilling process were analyzed in detail the various drilling parameters and drilling rate and the relationship between bit wear,WOB and the rotation speed as decision variables,under the condition of certain constraints,established by mechanical drilling rate maximum,and drill bit life expectancy than minimum as the target of optimization model.Secondly,for the traditional method to solve the problem of multi-objective optimization and intelligent algorithm,the dominant position of intelligent algorithm,lists several intelligent optimization algorithms,points out their respective advantages and disadvantages,and illuminates the ant colony algorithm is not successful application in the field of drilling parameter optimization,so the ant colony algorithm is used to study in this paper.At the same time,the shortcomings of basic ant colony algorithm are analyzed and corresponding improvements are made.Therefore,this paper uses the improved ant colony algorithm to carry out multi-objective optimization research on drilling parameters.Finally,Matlab simulation experiment is carried out in a specific drilling case.Before simulation,various parameters involved are set.Simulation results show that the algorithm can obtain the optimal solution set with uniform distribution.The rationality and validity of the model and algorithm are further proved by the experimental data analysis.At the same time,the optimization results of the improved algorithm in this paper are compared with the genetic algorithm.The data shows that the improved ant colony algorithm in this paper has a faster convergence rate,which provides an effective basis for the selection of the actual optimization scheme in engineering.