Case Study: An Agentic AI Framework for Large-Scale Well Modeling of Offshore Field Developments

_ Well modeling is a foundational activity in production engineering for artificial lift systems. Engineers routinely construct physics-based models to match flowing gradient survey (FGS) data, generate inflow performance relationship (IPR) and vertical lift performance (VLP) relationships, analyze pressure-temperature (PT) profiles, and evaluate tubing-size and gas-lift sensitivities. These workflows directly support decisions related to production optimization, stimulation planning, and liquid-loading diagnostics. In practice, however, such modeling remains highly manual and time intensive. For a single well, constructing and calibrating a physics-based model in a commercial simulator often requires several hours of focused engineering effort. When the number of wells increases to hundreds, as is common in large offshore developments, the task becomes prohibitively slow. As a result, many studies are either deferred, restricted to limited subsets of wells, or simplified to meet time constraints. Each of these approaches may compromise decision quality. To address this scalability challenge, agentic artificial intelligence (AI) was deployed for India’s Oil and Natural Gas Corporation (ONGC). The agentic AI enabled an automation framework that could complete large-scale well modeling in a rapid and repeatable manner while under the supervision of engineering teams (Fig. 1). The framework integrates a domain-specific Python library with an AI-driven command-line agent operating on SLB’s Pipesim engine. The central objective is not to replace engineering judgment, but to remove repetitive manual steps so that engineers can focus on interpretation and decision-making rather than model construction. Agentic-AI Architecture The automation framework consists of three tightly integrated components. Well-Analysis Python Library A custom Python library, named well-analysis, was developed to encapsulate the most common well-modeling operations in Pipesim. The library provides compact and reusable functions to define tubular geometry and completion configuration, black-oil PVT (pressure-volume-temperature) properties, reservoir inputs and productivity models, artificial-lift settings, IPR generation, VLP correlation selection, PT profiling, nodal analysis, and FGS-based model calibration. Conventional Pipesim scripting for these tasks often exceeds 100 lines of code per modeling scenario. Using the library, the same operations can typically be expressed in fewer than five lines, which dramatically simplifies automation, maintenance, and reuse. Small Language Model Agent A lightweight AI agent powered by a small language model (SLM) orchestrates the modeling workflow. The agent communicates with the simulator through a model context protocol server that exposes the functions of the well-analysis library as actionable tools. Engineers provide high-level natural-language instructions such as building and calibrating models from a data file, generating IPR-VLP and PT plots, or executing tubing sensitivity studies. The agent translates these instructions into structured execution steps while preserving full traceability of inputs, actions, and outputs.