Optimizing Closed-Loop Geothermal Systems for Sustainable Energy: A Technoeconomic Framework

Abstract Closed‑loop geothermal systems (CLGS) are emerging as firm, low‑carbon resources, yet project viability depends on co‑optimizing subsurface design and surface conversion under geological and market uncertainty. This paper presents a physics‑informed, AI‑assisted technoeconomic framework that couples a transient conduction model for multilateral closed loops with an Organic Rankine Cycle (ORC) performance block and a discounted‑cash‑flow module. The subsurface model uses Ramey‑style wellbore/formation heat‑transfer relations to simulate injection–lateral–production temperatures; the surface block computes thermodynamic states and duties with high‑fidelity property calls; the economics module returns LCOE/LCOH, NPV, IRR, and payback over a 30‑year life. A multi‑objective genetic algorithm explores the design space (e.g., mass flow, depth, lateral length, branch count, ORC settings) under operational constraints including minimum boiler pinch. Local and global sensitivity analyses quantify how thermal properties, gradients, and drilling performance propagate to cost and value metrics. Results across representative scenarios indicate that CLGS can sustain stable heat extraction with late‑time logarithmic drawdown characteristic of conduction‑dominated regimes; Pareto fronts reveal actionable "knee" solutions that balance moderate depths and lateral lengths against net power and cost; and project economics are most sensitive to geothermal gradient, rock conductivity/diffusivity, mass flow, and drilling day‑rate/ROP. The framework provides decision‑grade guidance for early‑stage screening and design, is compatible with open‑source scientific computing tools, and can be extended to include pressure‑drop hydraulics and advanced ORC variants. Overall, the study demonstrates a practical pathway to optimize CLGS for sustainable, cost‑effective heat and power delivery.