NeoSpatial: Agentic AI Driven Solution, Revolutionizing Oil and Gas Exploration Geospatial Workflows

Abstract The exploration and development of oil and gas resources increasingly depend on the rapid integration and analysis of diverse geospatial datasets. This paper introduces NeoSpatial, an agentic artificial intelligence (AI) platform that combines large language model (LLM) reasoning, autonomous GIS agents, and policy-driven guardrails to automate complex geospatial workflows. NeoSpatial represents a shift from rule-based automation toward autonomous geospatial intelligence, translating natural-language instructions into executable, policy-compliant workflows. By integrating LangChain and LangGraph for orchestration, the ArcGIS Python API for spatial computation, and ReAct agents embedded with various geospatial tools for adaptive execution, NeoSpatial currently enables the automation of key enterprise workflows such as well placement planning, infrastructure routing, regional screening and basin evaluation, and prospects delineation and lead generation. The agentic geoprocessing system has been enhanced with full autonomous mode which is capable of determining the ordered set of geoprocessing tasks required for automatic workflow generation hence minimizing the geospatial knowledge requirement for the end user. This is a powerful and flexible capability that incorporate human-in-the-loop mechanism to improve accuracy and provide human oversight feature within the system. Moreover, NeoSpatial has been integrated with various company's AI-powered intelligent information retrieval agents such as Lexica and Energy Large Language Model (ELLM) that helps professionals to access precise and contextually relevant information related to oil and gas operations. In pilot studies across few upstream case studies, NeoSpatial reduced manual geoprocessing time by median 62 % (p < 0.01) and improved reproducibility of spatial analyses. By unifying generative AI, natural language interfaces, and autonomous GIS, NeoSpatial constitutes a first‑of‑its‑kind, fully autonomous geospatial intelligence engine bridging the gap between human intent and machine-executed geospatial reasoning in the upstream energy domain. These results demonstrate NeoSpatial's potential to become a core enabler of reasoning-driven geospatial intelligence across oil and gas exploration domains.