@Article{ee.2023.027703,
AUTHOR = {Wenhua Xu, Yuming Zhu, Yingrong Wei, Ya Su, Yan Xu, Hui Ji, Dehua Liu},
TITLE = {Prediction Model of Drilling Costs for Ultra-Deep Wells Based on GA-BP Neural Network},
JOURNAL = {Energy Engineering},
VOLUME = {120},
YEAR = {2023},
NUMBER = {7},
PAGES = {1701--1715},
URL = {http://www.techscience.com/energy/v120n7/52719},
ISSN = {1546-0118},
ABSTRACT = {Drilling costs of ultra-deep well is the significant part of development investment, and accurate prediction of drilling
costs plays an important role in reasonable budgeting and overall control of development cost. In order to improve
the prediction accuracy of ultra-deep well drilling costs, the item and the dominant factors of drilling costs in Tarim
oilfield are analyzed. Then, those factors of drilling costs are separated into categorical variables and numerous
variables. Finally, a BP neural network model with drilling costs as the output is established, and hyper-parameters
(initial weights and bias) of the BP neural network is optimized by genetic algorithm (GA). Through training and
validation of the model, a reliable prediction model of ultra-deep well drilling costs is achieved. The average relative
error between prediction and actual values is 3.26%. Compared with other models, the root mean square error is
reduced by 25.38%. The prediction results of the proposed model are reliable, and the model is efficient, which can
provide supporting for the drilling costs control and budget planning of ultra-deep wells.},
DOI = {10.32604/ee.2023.027703}
}