【摘要】 油气钻井成本是反映油田企业经济效益的重要指标.对钻井成本进行准确预测,有利于企业管理者和投资者进行科学的决策与评估.在对油气钻井成本影响因素进行分析的基础上,运用BP神经网络算法,建立了考虑成本因素之间相互关系的油气钻井成本神经网络预测模型,并结合中国石油某公司各区块钻井作业成本数据,将线性回归方法与神经网络方法进行对比,结果表明该模型具有较高的预测精度.更多还原

【Abstract】 Oil-gas drilling cost is an important index reflecting the economic benefit of an oilfield enterprise.The accurate forecast of the drilling cost can help enterprise directors and investors to carry out scientific decision and estimation.Based on analyzing the influential factors of oil-gas drilling cost,a drilling cost forecasting model is established using BP neural network,in which the relationship among the factors is considered.Taking the drilling work data of some oilfield as an example,it is proven that the method has higher forecast precision than linear regression method and BP neural network method.更多还原