关键词: 油气田, 操作成本, 预测, 成本定额, 主成份, 深层和超深层

Abstract: Accurate prediction and refined assessment of operating costs constitute a significant tool for petroleum enterprises to advance lean management and cost control. To this end, targeting deep and ultra-deep oil and gas fields in Western China, we propose two operating cost prediction methods: Cost Component-Based Operating Cost Prediction Method & Principal Component Model-Based Prediction Method. These approaches provide robust evaluation tools and decision-making foundations for development plan design, financial budgeting, operational optimization, and strategic formulation in oil and gas development. Studies confirm both methods exhibit strong practicability and reliability. Cost composition-based operating cost forecasting method starts from the components of operating costs, focuses on cost norms, and selects common modes, common alternative modes, or deep modes for operating cost forecasting according to specific situations. The benchmarking-based cost norm auxiliary decision-making method and the research on the regularity between operating costs and burial depth are two types of guarantee mechanisms to ensure the rationality of the cost composition-based operating cost forecasting results. The reasonable selection and accurate definition of norms are the key to the effective application of this method. When historical norms are involved, it is generally recommended to use the three-year historical average. The Principal Component Modelbased forecasting method for operating costs begins with techno-economic indicators—covering geological, developmental, and operational factors—that influence these costs. It conducts macro-level operating cost predictions through multi-factor dimensionality reduction and regression based on historical samples. The comprehensiveness and representativeness of historical operating cost samples are critical determinants of the model’s accuracy.

Key words: Oilfield, Operating, Cost Prediction, Cost Component, Principal Component, Deep and Ultra-deep