Autonomous Directional Drilling Planning and Execution Using an Industry 4.0 Platform

Today, directional drilling is considered a mix between art and science only performed by experts in the field. In this paper, we present an autonomous directional drilling framework using an industry 4.0 platform that is built on intelligent planning and execution capabilities and is supported by surface and downhole automation technologies to achieve consistently performing directional drilling operations accessible for easy remote operations. Intelligent planning builds on standard planning activities that are needed for directional drilling applications and advances them with rich data pipelines that feed predictive and prescriptive machine-learning (ML) models; this enables more accurate BHA tendencies, operating parameters, and trajectory plans that ultimately reduce executional risk and uncertainty. Intelligent execution provides technologies that facilitate decision-making activities, whether they be from the wellsite or town, by leveraging the digital-drilling program that is generated from the intelligent planning activities. The program connects planning expectations, real-time execution data from the surface and downhole equipment, and generates insights from data analytics, physics-based simulations, and offset analysis to achieve consistent directional drilling performance that is transparent to all stakeholders. This new framework enables a self-steering BHA for directional drilling operations. The workflow involves an automated evaluation of the current bit position with respect to the initial plan, automated evaluation of the maximum dogleg capability of the BHA, and the capability to examine the health of the BHA tools and, if needed, an automated re-planning of an optimized working plan. This is accomplished on a system level with interdependencies on the different elements that make up the complete workflow. This new autonomous directional drilling framework will minimize operational risk and cost-per-foot drilled; maximize performance, procedural adherence, and establish consistent results across fields, rigs, and trajectories while enabling modern remote operations.

An Optimal Algorithm for Planning Shearer Trailing Drum Cutting Path

The intelligent adaptive cutting of the shearer is one of the key technologies to realize the intelligent working face. However, since the “memory cut” technology is the mainstream technology, which cannot actively adapt to the coal seam variations, the trailing drum usually cuts at a fixed height without a planned cutting path. This paper analyzes the shearer’s location characteristics before and after the advancement to propose a complete calculation method for the advancing path of the shearer, which simulates all of its possible advancing paths for subsequent n cuttings. The multitree and depth-first search algorithms are utilized to filter out the optimal advance path under different mining requirements. Simultaneously, this paper indicates that the vertical curvature of the armored face conveyor (AFC) should be considered in the calculation process of the optimal advancing path at different positions of the working face to obtain the shearer’s planned cutting path for subsequent n cuttings. The proposed algorithm in this paper has apparent advantages over the “memory cut” technology and provides a good solution for the intelligent planning of cutting and pitch steering of the shearers.