Phase 2: Spatial Correlation (Supply-Demand) Using GIS, information layers were superimposed to determine logistical viability: ● Supply: Heat flow and gradient maps (Region 5, INGEMMET) identifying anomalies >50 MW. ● Demand: Georeferencing of large-scale mining in Arequipa, Moquegua, and Tacna. ● Optimization: Euclidean proximity analysis to define "energy clusters" minimizing transmission CAPEX (<100 km). Phase 3: Conceptual Development Model A technical framework was defined based on three engineering criteria to transform resources into reserves: ● A. Multi-Scalar Drilling: Regional and reservoir protocol, estimating well density for 100 MWe (productivity 5-10 MWe/well, reinjection 1:3). ● B. Thermodynamic Cycle: Double Flash Technology for High Enthalpy (>240 °C) and Binary Cycles (ORC) for Medium Enthalpy (180-230 °C). ● C. Staged Development: "Cluster Drilling" strategy and modular growth (50 → 100 → 300 MW) to mitigate capital risk. Multi-Criteria Decision Matrix Design (MCDA) A weighted matrix was designed to compare Geothermal (Achumani), Hydroelectricity (SEIN), and Solar+BESS, assigning weights according to base load requirements: Supply Security (35%), Economic Viability (25%), Logistical Synergy (15%), Sustainability/ESG (15%), and Social License (10%). Financial Model: Levelized Cost of Energy (LCOE) To determine competitiveness, the Capital Recovery Factor (CRF) method was used, annualizing investment via the following equations: = ( × )+ í Where: CAPEX: Total capital investment, OPEXannual: Operation and maintenance costs per year. Energyannual: Expected net electricity generation (MWh/year). = (1+ ) (1+ ) −1 r: Discount rate (WACC), n: Project useful life. 3.1. Geophysical Integration Approach and State of the Art Technology Characterization is based on multi-physics data fusion (MT and Gravimetry). Magnetotellurics (MT) discriminate reservoir geometry (cap rock < Ω m), while Gravimetry/Magnetometry delineates structural control. This approach aligns with current trends of attribute integration powered by Artificial Intelligence (Neural Networks) to reduce uncertainty in blind systems, validating drilling targets. 3.2. Conceptual Framework: Play Fairway Analysis (PFA) The Play Fairway Analysis (PFA) methodology was adopted to identify zones where heat, permeability, and fluids coexist. Following the evolution towards quantitative approaches (ePFA in Nevada/Texas), the analysis prioritizes structural "fairways" (Incapuquio and Cincha-Lluta fault systems) where data density suggests higher probability of commercial success (Figure 2 and Table 3). 131
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