Optimization of Dogleg Severity in Directional Drilling Oil Wells Using Particle Swarm Algorithm (Short Communication)

Abstract

The dogleg severity is one of the most important parameters in directional drilling. Improvement of these indicators actually means choosing the best conditions for the directional drilling in order to reach the target point. Selection of high levels of the dogleg severity actually means minimizing well trajectory, but on the other hand, increases fatigue in drill string, increases torque and drag, particularly in the rotation mode. Therefore the aim is to define the index in an optimal range which meets both requirements. Particle swarm algorithm was used for optimization the dogleg severity. The final measured depth and directional well pattern were considered as an objective function and Build & Hold, respectively. Then the fatigue caused by the stresses exerted on the drill string, evaluated by modified Goodman equation simultaneously. The relationship between path parameters and the obligation to reach a target point in directional wells, converts the problem into a constrained optimization problem. Comparing the proposed directional drilling path in a drilled well in the Ahwaz oilfield with the responses obtained from the particle swarm algorithm indicated that the particle swarm algorithm is converged in finding the shortest path, and on the other hand, it decreases the time of using directional drilling equipment due to the selection of the proper dogleg severity. Note that it is likely to add other constraints to the optimization process which indicates the particle swarm algorithm efficiency in solving these problems.