A model for estimating the drilling and completion investment in offshore oilfields in West Africa and the Asia-Pacific region

Abstract

A BP neural network model for estimating the drilling and completion investment is built based on the BP neural network method with 86 representative offshore oilfields in West Africa and Asia-Pacific as samples. The model uses five factors, including oil price, water depth, well number, well depth and geologic condition, as the input parameters, and outputs the drilling and completion investment parameters. Comparison of the model with a regression analysis model shows that the established model is reasonable and valuable because the BP neural network is an active learning process, able to effectively describe the non-linear relationship between variables and solve complicated problems. The established BP neural network model has high fitting accuracy and the errors of most samples are within 30%, satisfying the requirements for engineering development, and are much smaller than that of regression analysis. Key words: West Africa, Asia-Pacific, offshore oilfields, drilling and completion investment estimate, BP neural network