Abstract
<jats:title>Abstract</jats:title> <jats:p>Production and economic forecasting are central to field development planning and asset management in the oil and gas industry. However, forecasting workflows frequently rely on deterministic assumptions, spreadsheet-based models, and manually managed scenarios that limit transparency, auditability, and decision robustness. This paper presents a constraint-based probabilistic forecasting methodology that integrates existing deterministic production forecasts with stochastic scenario simulation under explicit operational and commercial constraints.</jats:p> <jats:p>The methodology is applied to the evaluation of an oil and gas discovery tied back to a mature host asset with limited gas handling capacity. Key uncertainties, such as discovery production performance, gas-oil ratio, development costs, and host facility constraints, are modelled probabilistically. The resulting simulations enable rapid evaluation of production deferrals, host well shut-ins, facility upgrade options, and economic outcomes. Results demonstrate that constraint-driven probabilistic forecasting materially improves decision quality, speed, and cross-disciplinary alignment compared with conventional deterministic approaches.</jats:p>