Abstract:

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.

Key words: carbon-embedded NPV, liquid production rate optimization, low-carbon industry, carbon quota mechanism, intelligent optimization algorithm

摘要：

在“双碳”战略全面推进、油气行业即将纳入全国碳排放权交易体系的行业背景下，传统方法的净现值(Net Present Value, NPV)最大化问题多采用静态碳排放核算模式建立优化模型，无法精准捕捉生产策略调整对碳排放的实时影响，难以应对碳成本带来的经济与减排的双重挑战。本研究构建碳内嵌的NPV优化模型，通过建立地下油藏数值模拟与地面设备能耗模拟的耦合系统，采用引入约束修复算子的改进粒子群优化算法求解模型全局最优控制策略，以生产井产液速率为决策变量，设计免费分配、混合分配以及有偿拍卖3种典型碳配额方式开展系统的量化分析。数值实验表明，与无碳约束的基准开发方案相比，3种配额机制下项目最优方案的NPV分别提升7.63%、5.56%、3.86%，表明碳约束强度越高，企业利润空间压缩越明显。同时，对比不同政策下的最优开发策略发现，有偿拍卖机制下控水力度最大，源头减排效果最显著。研究表明，碳内嵌NPV优化模型可实现生产与减排的协同优化，有效向开发企业传递碳价格信号，碳配额机制是油气行业低碳转型的理想过渡政策工具。

关键词: 碳内嵌NPV, 产液速率优化, 低碳工业, 碳配额机制, 智能优化算法

CLC Number: 

• TE322

• P618.13