Wenhua Xu^1,3, Yuming Zhu², Yingrong Wei², Ya Su², Yan Xu^1,3, Hui Ji¹, Dehua Liu^1,3,*

Energy Engineering, Vol.120, No.7, pp. 1701-1715, 2023, DOI:10.32604/ee.2023.027703

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… More >

Meng Li¹, Kanhua Su¹, Zijian Li², Dongjie Li³, Lifu Wan¹

CMES-Computer Modeling in Engineering & Sciences, Vol.113, No.4, pp. 475-495, 2017, DOI:10.3970/cmes.2017.113.475

Abstract With the consideration of the randomness of complex geologic parameters for ultra-deep wells, an uncertainty analysis method is presented for the extrusion load on casing in ultra-deep wells through complex formation at a certain confidence level. Based on the extrusion load model for casing in ultra-deep wells and the prerequisite of integrity of formation-cement ring-casing, the probability and statistics theory is introduced and the sensitivity analysis on the uncertainty of extrusion load on casing is conducted. The distribution types of each formation parameters are determined statistically. The distribution type and distribution function of the extrusion load on casing are derived.… More >