钻井投资是油气田开发工程投资的重要组成部分，钻井成本的准确合理估算和预测对海外油气项目经济、合理有效获取有重要影响。钻井成本是一种综合成本，不仅受管理、技术创新等微观因素影响，也与国际油价、资源国经济形势等宏观因素密切相关，且宏观因素直接影响着微观因素，是影响钻井成本的关键因素。本文从宏观角度，以计量经济学的协整理论为基础，对全球GDP、国际油价与某资源国钻井米成本两两之间以及三者之间的定量关系进行了实证研究，建立钻井成本预测模型。研究结果表明：1) 全球GDP对石油钻井米成本有长期单向影响；2) 国际原油价格对石油钻井米成本有长期单向影响；3) 全球GDP和国际油价与钻井成本之间存在协整关系，可用全球GDP和国际油价建立VAR模型。基于以上研究建立了以实证研究为基础的资源国钻井米成本预测模型，进行了Johansen协整检验、Granger因果关系检验。并用某资源国历史钻井成本数据进一步拟合验证，预测数据误差主要在10%~20%左右，证明预测模型能够在海外油气新项目评价中应用。
Drilling investment is an important
part of investment of oil and gas field development. Accurate and reasonable
estimation and forecast of drilling cost have an important influence on the economic,
reasonable and effective acquisition of the project. Drilling cost is the
embodiment of a combination of cost. It is affected by micro factors such as
management and technological innovation, also closely related to macro factors
such as international oil prices and the economic situation of world, and macro
factors directly affect micro factors and are the key factors that affect the
cost of drilling. Therefore, from the macro point of view, based on the theory
of econometrics, this paper makes a positive analysis on the quantitative
relationship between international crude oil price, globe GDP and cost of
drilling in this resource country, and among the three, in order to establish a
forecast model for drilling costs. Research results show that: 1) Global GDP
has a long-term one-way impact on the cost of oil drilling rice; 2)
International crude oil prices have a long-term one-way impact on the cost of
oil drilling rice; 3) There is an integration relationship among global GDP and
international oil prices and drilling costs. VAR model was established with
global GDP and international oil prices. Based on the above research, a
forecast model of the cost of drilling rice in resource countries based on empirical research was established, and carried out
Johansen co-integration test and Granger causality test. The historical
drilling cost data of resource countries were further fitted and verified, and
the error of forecast data is mainly about 10% - 20%, which proves that
the forecast model can be applied in the evaluation of new overseas oil and gas
projects.
