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Field data analysis and risk assessment of shallow gas hazards based on neural networks during industrial deep-water drilling

Author

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  • Cao, Bohan
  • Yin, Qishuai
  • Guo, Yingying
  • Yang, Jin
  • Zhang, Laibin
  • Wang, Zhenquan
  • Tyagi, Mayank
  • Sun, Ting
  • Zhou, Xu

Abstract

The geological conditions of deep water in the South China Sea are complex. Shallow gas is often encountered during deep-water drilling, which is likely to cause serious accidents such as blowouts and fires. This paper studied the identification methods of shallow gas during deep-water drilling based on neural networks as follows. First, the identification criteria of shallow gas were obtained from the seismic characteristics of shallow gas. Three-Dimensional (3D) seismic data from the field was used to identify the shallow gas. A dataset of thirteen seismic attributes was collected, including seismic amplitude, frequency, and velocity. The features were refined to optimize the seismic attributes. Secondly, this study employed Back Propagation (BP) neural network, BP neural network optimized based on Particle Swarm Optimization (PSO-BP), probabilistic neural network (PNN), and fully connected Deep Neural Network (DNN) as the algorithms for shallow gas identification according to the characteristics of the dataset. Results showed that the fully connected DNN performed better than the other three neural network algorithms. The predicted shallow gas based on neural networks is consistent with its actual distribution estimated by seismic data, proving the feasibility and effectiveness of the shallow gas identification. Our study pointed out that the fully connected DNN had great advantages in fitting the highly nonlinear mapping relation of the multi-level multi-source dataset, which provided a reference for similar classification problems.

Suggested Citation

  • Cao, Bohan & Yin, Qishuai & Guo, Yingying & Yang, Jin & Zhang, Laibin & Wang, Zhenquan & Tyagi, Mayank & Sun, Ting & Zhou, Xu, 2023. "Field data analysis and risk assessment of shallow gas hazards based on neural networks during industrial deep-water drilling," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    • Handle: RePEc:eee:reensy:v:232:y:2023:i:c:s0951832022006949
    DOI: 10.1016/j.ress.2022.109079
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