Against the backdrop of the full advancement of China’s “Dual Carbon” strategy and the impending inclusion of the oil and gas industry in the national carbon emissions trading system (ETS), traditional optimization models for reservoir development, which mostly adopt a static carbon emission accounting mode for the net present value (NPV) maximization problem, cannot accurately capture the real-time impact of production strategy adjustments on carbon emissions, and thus struggle to address the dual challenges of economic benefits and emission reduction brought by carbon costs. This study develops a carbon-embedded NPV optimization model that establishes a coupling system between subsurface reservoir simulation and surface facility energy consumption simulation. An improved particle swarm optimization (PSO) algorithm with a constraint repair operator is adopted to solve the global optimal control strategy of the model with production well liquid rates as decision variables. Three carbon quota allocation mechanisms are designed for quantitative analysis: free allocation, mixed allocation, and paid auction. Numerical experiments demonstrate that, compared with the baseline scenario, the NPV improvements under the three mechanisms are 7.63%, 5.56%, and 3.86%, respectively, indicating that stricter carbon constraints lead to greater compression of profit margins. Furthermore, the paid auction mechanism exhibits the strongest water-cut control and the most significant emission reduction effects. The study verifies that the carbon-embedded NPV optimization model can achieve the synergistic optimization of production and emission reduction, and effectively transmit carbon price signals to development enterprises. Carbon quota mechanisms are ideal transitional policy tools for the low-carbon transformation of the oil and gas industry.