Approach Uses Multidisciplinary Constraints for Automatic Well-Path Generation

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 215021, “A New Paradigm for Automatic Well-Path Generation Using Multidisciplinary Constraints,” by Eric Cayeux, SPE, Gilles Pelfrene, SPE, and Rodica G. Mihai, SPE, NORCE, et al. The paper has not been peer reviewed. _ Because of the multidisciplinary nature of the well-planning process, many iterations are necessary to generate a well path. This is a time-consuming process that finally leads to planned trajectories that may be suboptimal. Departing from the traditional incremental approach to well-path generation, the method proposed in the complete paper relies on the collection of experienced-based constraints from each discipline to generate possible alternatives to the well path. As a result of this new process, the multidisciplinary team can focus on the relevance of the constraints rather than on the details of the planned trajectory. Constraint-Based Well-Path Design To better capture the decisions during the well-path-planning process and their motivation, a constraint-based design is proposed. Typical constraints that can be included in such an approach are geological, geomechanical, operational, economic, time-related, technical, social, and environmental. The complete paper focuses on geometrical constraints and aspects related to their influence on well-path design. Target and Target-Group Constraints Definition. A target is a complex concept with no standard definition across the industry or within companies themselves. In the proposed approach, the target is defined as a set of geometrical constraints to be met, each referring to a target body. It differs from the standard approach in that it does not discriminate between target bodies to reach (such as points or target axes), which can be called attractors, and those to avoid (such as faults or vertical walls of lease lines), which can be called repellors. The distinction is particularly relevant when the well-path-generation algorithm can process the set of constraints globally, thus providing an ensemble of solutions that inherently meet all constraints.