14 AUGUST 2021 WorldWide Drilling Resource® Ant Behavior Improves Horizontal Directional Drilling Design Compiled by the Editorial Staff of WorldWide Drilling Resource® Trenchless technology has increased in demand due to the need for utility conveyance under multiple obstacles, such as highways, waterways, railroads, and other manmade or natural structures, as well as quicker and less invasive methods of installation, one of which is horizontal directional drilling (HDD). Engineering a successful HDD project requires calculations, analysis, and evaluation of feasibility, design, budget, and risk. Because the subsurface is unseen, it poses a set of challenges to underground infrastructure design. Subsurface investigations are performed to determine the underlying geology, and this is dependent on the type of infrastructure to be installed. Thanks to the lowly ant, optimization of HDD has become a more informed, efficient process. Some of today’s optimization techniques to streamline the decision-making process during HDD design involve a look at ant colonies and how they “learn” the best pathway to a food source. Analysis of a colony of ants, a swarm population, reveals a remarkable collective intelligence. Developed as one of the artificial intelligence (AI) disciplines, swarm intelligence (SI) derived inspiration from studying the collective behavior of insects and animals. In Ant Colony Optimization (ACO), the ant searches for food in a random manner until the food source is found. Returning to the colony, the ant leaves a pheromone (a chemical ants produce) trail based on the quality and quantity of food. The colony eventually responds to the strongest presence of pheromones leading to the food, which is usually the shortest, most advantageous route. As English Writer Richard Jefferies said, “It would seem that the ant works its way tentatively, and observing where it fails, tries another place and succeeds.” ACO, a step-by-step computational procedure within SI, is a technique using probabilities for finding optimal paths based on collection information. In application, multiple operations considering several variables are run to determine the optimal drill path, similar to how ants communicate pheromone signals repetitively to the whole colony. Real data from HDD projects help implement ACO. Actions of expert HDD professionals, mechanics of drilling simulations, historical data from drilling logs, and simulation data are used to generate decisions resulting in efficient drilling. The drill path is optimized by continuous ACO implementations, setting the depth of the alignment and its entry and exit angles as the design parameters. This ensures a minimal, less costly drill path and avoids collapses or instability. As an advanced tool providing more rapid feasibility and analysis of HDD projects, ACO results in more rigorous designs and ultimately, lower cost and risk. DIR
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