A New Mathematical Method for Solving Cuttings Transport Problem of Horizontal Wells: Ant Colony Algorithm

• Published in: Mathematical problems in engineering 2017-01, Vol.2017 (2017), p.1-8
• Main Authors: Yan-feng, Bai, Heng-bin, Qiu, Yu-ming, Liu, Liu, Yongwang
• Format: Article
• Language: English
• Subjects: Algorithms; Ant colony optimization; Artificial intelligence; Drilling; Engineering; Genetic algorithms; Horizontal wells; Information science; Jacobi matrix method; Jacobian matrix; Mathematical problems; Matrix methods; Methods; Newton methods; Nonlinear equations; Outflow; Petroleum engineering; Petroleum production; Researchers; Searching; Transport; Variables
• ISSN: 1024-123X; 1563-5147
• DOI: 10.1155/2017/7898647

Cuttings transport problem has long been recognized as one of the key difficulties in drilling horizontal wells, and the models in cuttings transport research are usually formulated with highly nonlinear equations set. When using Newton methods to solve real engineering problems with nonlinear equations set, the problems of result dependence on initial values, Jacobian matrix singularity, and variable outflow of its definition domain in iterations are three of the often-encountered difficulties. In this paper, the ant colony algorithm is applied to solve the two-layer cuttings transport model with highly nonlinear equations set. The solution-searching process of solving nonlinear equations set is transformed into an optimization process of searching the minimum value of an objective function by applying ant colony algorithm. Analyzing the results of the example, it can be concluded that ant colony algorithm can be used to solve the highly nonlinear cuttings transport model with good solution accuracy; transforming the solution-searching process of solving nonlinear equations set into an optimization process of searching the minimum value of the objective function is necessary; the real engineering problem should be simplified as much as possible to decrease the number of unknown variables and facilitate the use of ant colony algorithm.