Multi-objective Optimization And Case Analysis Of Drilling Engineering Parameters In Changling Area And Dehui Area

In recent years,as the depth of recoverable oil and gas reservoirs has gradually become deeper and geological conditions have become increasingly complex,intelligent drilling has become a future development trend.Drilling engineering parameters have a great impact on ROP and drilling costs.Optimizing drilling engineering parameters is of great significance for speed increase and cost reduction.The existing optimization methods for drilling engineering parameters are relatively traditional,with a large amount of calculation and long calculation time,which is difficult to apply in the field,and the optimization model is more theoretical and has less guiding significance for actual production.Therefore,applying the intelligent optimization algorithm to the drilling engineering parameter optimization model with practical guiding significance has high research value.This paper studies the multi-objective optimization theory,establishes a multi-objective optimization model of drilling engineering parameters through clustering and fitting,and uses intelligent optimization algorithms to solve the optimal parameter combination of weight on bit,rotation speed,and displacement.Case studies in Changling area and Dehui area show that the use of optimization models and intelligent optimization algorithms can increase ROP and reduce drilling costs.According to the characteristics of field engineering applications,the NSGA?algorithm is improved.The improved NSGA?algorithm can reduce the number of iterations required for optimization and save optimization time.The specific research content is as follows:(1)Compare and analyze the intelligent multi-objective optimization algorithms NSGA?and NSGA?.The calculation steps of the two are basically the same,and the selection mechanism is different.NSGA?uses the crowding degree calculation method,and NSGA?uses the reference point method.When there are many objective functions,the NSGA?algorithm can better maintain the diversity of the population.(2)According to the variables involved:weight-on-bit,rotational speed,and displacement,choose the quaternary drilling rate equation as the objective function.Using actual field data and nonlinear fitting methods,the coefficients of the quaternary drilling rate equation of each well section can be calculated.Perform on-site verification,apply the obtained quaternary drilling rate equation to the next well section,and compare the calculated drilling rate with the real drilling rate.The fitting accuracy of the quaternary drilling rate equation is relatively high,R~2 is between 0.81 and 0.90,and the calculation error is between8.94%and 22.1%.(3)Grouping and clustering the data of unit footage cost,rotation speed and weight-on-bit of different layers,control variables to fit the relationship between rotation speed and unit footage cost,weight-on-bit and unit footage cost.The fitting method is used to find the relationship between the two variables of rotation speed,weight on bit and the unit footage cost.Perform on-site verification,apply the obtained equation to the next well section,and compare the calculated unit footage cost with the actual unit footage cost.The fitting accuracy of the equation is high,R~2 is between 0.87-0.98,and the calculation error is between1.38%and 12.00%.(4)Taking the maximum ROP and the minimum unit footage cost as the objective function,and taking the weight on bit,rotation speed,and displacement as decision variables,a multi-objective optimization model of drilling engineering parameters is established.Case analysis was carried out in Changling area and Dehui area to compare the optimization process and results of NSGA?and NSGA?algorithms applied to the same well section.The optimized combination of WOB,rotation speed,and displacement parameters can increase speed by 21.3%-44.6%and reduce costs by 8.06%-16.15%.(5)In view of the poor convergence of the NSGA?algorithm and other problems,combined with the actual situation of engineering applications,the NSGA?algorithm is improved.Output the contemporary non-dominated solution set after each iteration.In the Changshen A well,the improved NSGA?algorithm can reduce the number of iterations and save optimization time.