Operating expense (OPEX) estimation is a critical determinant of project viability in overseas oil and gas developments. Domestic top-down approaches employed by Chinese petroleum enterprises, which rely on historical averages and fixed contingency percentages, often fail to capture project-specific drivers and risk dynamics. In contrast, International Oil Companies (IOCs) utilize a bottom-up framework that integrates activity-based costing (ABC), learning-curve, and Monte Carlo simulation to produce both rapid baseline estimates and refined probabilistic forecasts. An empirical study was conducted on a representative Australian gas project under uniform production and operational assumptions. The top-down method produced an estimate of 800 MM AUD by aggregating unit rates for variable and fixed costs and applying a contingency factor of 7–10% of maintenance expenditures. The IOC method constructed a detailed Work Breakdown Structure to link each operational activity to specific cost drivers. Learning curves were applied to repetitive tasks—such as well workovers and major equipment overhauls—resulting in approximately 40% lower cost projections in those categories. Risk was modeled across three layers—inherent, contingent, and unknown—using Monte Carlo simulation to derive P10, P50, and P90 intervals. The comparison revealed that the top-down model’s coarse granularity led to significant overestimation of workover and surface-maintenance costs, underestimation of labour requirements, and a static treatment of contingency. In contrast, the IOC approach delivered transparent, auditable, and probabilistic cost forecasts that better reflected operational efficiency gains and compound risk interactions. To address these shortcomings, three strategic pathways are recommended for Chinese enterprises: (1) adoption of a dual-layer adaptive model combining ABC with probabilistic scenario analysis; (2) acceleration of digital transformation through professional estimation platforms to enable data-driven, traceable modeling of cost drivers, learning effects, and interdependencies; and (3) development of localized cost databases and adjustment-factor systems that quantify host-country political, regulatory, and infrastructure risks. Implementation of these strategies is expected to enhance the precision and reliability of OPEX forecasting and support risk-informed decision-making in complex international projects.