Agile AI Agentic Neural Based Framework for Complex Workflow Forming, with Multiple Implementations Upon Upstream and Downstream Scenarios

Abstract

Abstract The oil&gas sector increasingly depends on agile, reliable, and explainable workflow automation to support production and operations activities such as wellbore fault diagnostic, maintenance, and pipeline scheduling. However, existing genAI-based agents technologies are limited by complex workflow forming, lack of cross-scenario generalization, and insufficient continuous refinery mechanisms, which restrict their ability to support long procedure workflows or adapt to evolving operating conditions. This study proposes an Agile Agentic Neural (AAN) framework that autonomously forms, executes, and refines complex workflows. The approach is designed to address upstream wellbore workovers and downstream pipeline scheduling, enabling event sensing, task planning, tools coordination, and continuous improvement through dual human-machine feedforward mechanism. The objective is to establish an operationally robust and scalable approach that accelerates production decision-making while maintaining interpretability and safety across integrated asset operations. The AAN framework adopts a neural network inspired architecture with 3 layers. The input layer performs scene recognition, semantic understanding, and task decomposition from multimodal field data (e.g., SCADA, production logs), using distributed self supervised distributed agents to ensure quality-grounded fusion. The computational layer organizes sub-agents within dynamically selected topologies—mesh, tree, code or workflow structures based on operational context. A supervisor agent uses in-context refinement and historical success logs to optimize decisions such as diagnostics, workflow planning, and response actions examining. The output layer assembles executable workflows and employs a dual feedforward loop that integrates engineer feedback and machine-observed metrics, enabling digital-twin-like self-refinement without retraining. Validation includes 60 shale wellbore maintenance cases (pump leaks, rod failures, scaling, wax deposition) and pipeline scheduling scenarios within previous couples of years, involving equipment faults, weather anomalies, and supply-demand disturbances. Ablation studies compare four feedback mechanisms to assess robustness. Across wellbore maintenance tasks, the AAN framework achieved human and agent evaluation scores of 0.76-0.98, with standard deviations <0.1 in event sensing, task planning, agent coordination, and tool use. Pipeline scheduling cases showed comparable performance, with 0.74-0.93 accuracy and strong stability under multiphase flow and hydrat risk conditions. Cross-scenario generalization maintained 0.85-0.94 accuracy, though toolusage precision declined in highly heterogeneous cases. Ablation testing confirmed the superiority of the global dual-feedback design, achieving 0.78-0.98 accuracy with the lowest variance (0.02-0.19), highlighting its value for real-time production control and risk mitigation.