Ai-Guo Wang

Ai-Guo Wang


Research Focus & Metrics

Academic Career Length

3Years (2024-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

View All Publications
10 March 2024
Verified
Book Chapter
Closed access
PDF file
AWAi-Guo Wang
Ai-Guo Wang
FHFei Han
Fei Han
HWHong-Wei Wang
Hong-Wei Wang
HQHan Qiao
Han Qiao

13 May 2026 in Processes
Verified
Journal Article
Open access
PDF file
LWLifu Wan
Lifu Wan
HSHongchen Song
Hongchen Song
AWAi-Guo Wang
Ai-Guo Wang
MLMeng Li
Meng Li
ZZZhili Zhang
Zhili Zhang
GZGuohui Zhang
Guohui Zhang
Aiming at the engineering challenges of low Rate of Penetration (ROP), high rock-breaking energy consumption, and the difficulty in realizing collaborative optimization of efficiency improvement and consumption reduction through traditional drilling parameter regulation in deep interbedded sandstone and mudstone formations of the Southwest Oil and Gas Field, a study on multi-objective optimization of drilling parameters is carried out. First, the traditional Teale Mechanical Specific Energy (MSE) model is modified, with the introduction of the effective energy utilization coefficient of the drill bit and the downhole torque calculation method, to establish an improved MSE model that fits the actual field drilling conditions. Second, a hybrid TCN-LSTM model fused with an additive attention mechanism is constructed to realize high-precision dynamic prediction of ROP. Finally, taking the on-site real-time controllable Weight on Bit (WOB) and rotary speed as decision variables, the synergy–conflict boundary between the dual objectives of ROP maximization and MSE minimization is clarified, a dual-objective coupled optimization model is established, and the genetic algorithm is used to complete the global optimization. Case verification is carried out based on the actual drilling data of 12 deep wells in the study block, and the results show that the Coefficient of Determination R2 of the established ROP prediction model on the test set reaches 0.91, and the prediction accuracy is significantly better than that of BP, CNN and single LSTM models; after optimization, the ROP of the target well interval is increased by 13.1% compared with the on-site actual drilling value, and the improved MSE is reduced by 23.5%, which simultaneously realizes drilling efficiency improvement and rock-breaking energy saving within the safe operation boundary. The stability of the model and the effectiveness of each module are verified through five-fold inter-well cross-validation and module ablation experiments. The research results can provide a theoretical basis and technical support for the precise regulation of drilling parameters in deep formations of the target block.