Model and methods for comprehensive measurement of the low-carbon status of China's oil and gas enterprises

This paper establishes a model that would allow China＇s oil and gas enterprises to scientifically evaluate and measure their low-carbon level and status. It considers various characteristics of China＇s oil and gas enterprises and the implications of low-carbon development, and is based on an overall analysis of factors that influence the reduction of carbon emissions. In view of low-carbon economic theories and the general principles of an evaluation index system, a comprehensive system for measuring the low-carbon status of China＇s oil and gas enterprises has been developed. This measurement system is comprised of four main criteria （energy structure, energy utilization, carbon emissions and utilization, and low carbon management） as well as thirty indexes. By the Delphi method and the analytical hierarchy process （AHP）, the weight of the rules hierarchy and indexes hierarchy were determined. The standardized indexes were then integrated using a linear weighted sum formula, and a comprehensive formula for index measurement was established. Taking into account the status of low- carbon development in the petroleum and petrochemical industry at home and abroad, an evaluation criterion is proposed comprising four levels： ideal low-carbon, economical low-carbon, medium-carbon and high-carbon, whose values were organized within the settings of [0, 1].

Status quo and countermeasures of energy audit in oil and gas field enterprises

To develop energy audits in oil and gas field enterprises has important and realistic significance. Chinese energy audit in oil and gas field enterprises is still in the exploratory stage, the paper puts forward some countermeasures and proposals through the analysis on status quo to identify the shortcomings.

Forecasting and optimal probabilistic scheduling of surplus gas systems in iron and steel industry

To make full use of the gas resource, stabilize the pipe network pressure, and obtain higher economic benefits in the iron and steel industry, the surplus gas prediction and scheduling models were proposed. Before applying the forecasting techniques, a support vector classifier was first used to classify the data, and then the filtering was used to create separate trend and volatility sequences. After forecasting, the Markov chain transition probability matrix was introduced to adjust the residual. Simulation results using surplus gas data from an iron and steel enterprise demonstrate that the constructed SVC-HP-ENN-LSSVM-MC prediction model prediction is accurate, and that the classification accuracy is high under different conditions. Based on this, the scheduling model was constructed for surplus gas operating, and it has been used to investigate the comprehensive measures for managing the operational probabilistic risk and optimize the economic benefit at various working conditions and implementations. It has extended the concepts of traditional surplus gas dispatching systems, and provides a method for enterprises to determine optimal schedules.