In response to the objective of fully attaining carbon neutrality by 2060, people from all walks of life are pursuing low-carbon transformation. Due to the high water cut in the middle and late phases of development, the oilfield’s energy consumption will be quite high, and the rise in energy consumption will lead to an increase in carbon emission at the same time. As a result, the traditional energy model is incapable of meeting the energy consumption requirement of high water cut oilfields in their middle and later phases of development. The present wind hydrogen coupling energy system was researched and coupled with the classic dispersed oilfield energy system to produce energy for the oilfields in this study. This study compares four future energy system models to existing ones, computes the energy cost and net present value of an oilfield in Northwest China, and proposes a set of economic evaluation tools for oilfield energy systems. The study’s findings indicate that scenario four provides the most economic and environmental benefits. This scenario effectively addresses the issue of high energy consumption associated with aging oilfields at this point, significantly reduces carbon emissions, absorbs renewable energy locally, and reduces the burden on the power grid system. Finally, sensitivity analysis is utilized to determine the effect of wind speed, electricity cost, and oilfield gas output on the system’s economic performance. The results indicate that the system developed in this study can be applied to other oilfields.

Renewable energy source has developed rapidly and attracted considerable attention. The integration of renewable energy into the energy supply chain requires precise forecast of the output of energy supply chain, thereby reducing energy resource waste and greenhouse gas emissions. In this study, a coupled model system is developed to forecast energy supply chain for the design optimization of distributed energy system, which can be divided into two parts. In the first part, long short-term memory (LSTM) and particle swarm optimization algorithm (PSO) contribute to energy supply chain forecast considering time series, and particle swarm optimization is used to optimize the parameters of the long short-term memory model to improve the forecast accuracy. Results show that the mean absolute error and root mean squared error are 8.7 and 16.3 for the PSO-LSTM model, respectively. In the second part, the forecast results are used as input of the distributed energy system to further optimize the design and operation schemes, so as to achieve the coupling optimization of forecast and design. Finally, a case study is carried out to verify the effectiveness of the proposed method.

The global energy landscape is undergoing a transformative shift, driven by the urgent need to address climate change, enhance energy efficiency, and integrate renewable energy sources