Multi-objective Intelligent Dynamic Optimization Design for Well Trajectory while Drilling in Horizontal Wells

Abstract

Abstract During the landing process of horizontal wells, the target formation may deviate from the expected depth, either deeper or shallower, which presents a significant challenge for the trajectory to accurately penetrate the reservoir. Wellbore track monitoring while drilling and dynamic intelligent optimization of the wellbore trajectory can effectively enhance the accuracy of hitting geological targets. To achieve this goal, it becomes particularly important to rapidly and accurately design an optimal wellbore trajectory that meets geological and engineering requirements. The traditional method of wellbore trajectory design is to give a set of wellbore trajectory design parameters and the corresponding evaluation target of the trajectory, and then get the wellbore trajectory design parameters in line with the expectation by continuously adjusting the parameters and carrying out trial calculations. This method is computationally intensive and time-consuming, and cannot meet the current requirements of intelligent drilling for the intelligent design of trajectory. This paper describes a multi-objective particle swarm optimization algorithm for the optimal design of track parameters, which takes the wellbore energy and the length of the track as the objective of evaluating the track, and uses the multi-objective particle swarm algorithm's characteristics of fast convergence and good optimization to rapidly design the optimal wellbore trajectory design parameters to meet the geologic and engineering requirements, so as to improve the efficiency of the design of the wellbore trajectory. A solution based on dynamic penalty function was proposed to address the constraint problem of the objective solution, aiming to improve the quality of the solution. Additionally, the TOPSIS algorithm was used to select the most suitable solution from the Pareto front. By analyzing the real well landing into target cases of shale gas horizontal wells, the method can quickly and effectively optimize the optimal design parameters of the required trajectory, which helps to improve the efficiency of the trajectory design and reduce the economic and time costs of the trajectory design.