# Causes of Multi-Mechanism Abnormal Formation Pressure in Offshore Oil and Gas Wells
## 5 Conclusions  
1. In the Rio Del Rey Basin in Cameroon, a meticulous data collection process was conducted on three wells, successfully extracting critical logging parameters such as well depth, acoustic velocity, density, resistivity, and natural gamma. These features are essential for discerning the mechanisms behind abnormal formation pressures and have been rigorously analyzed to ensure their utility in subsequent analytical models. 2. A pioneering analytical methodology was developed, combining hierarchical clustering with the powerful predictive capabilities of the LightGBM algorithm. This hybrid approach was further refined through Bayesian optimization, achieving an impressive model accuracy of 0.942. This advancement does not only enhances the accuracy of abnormal formation pressure cause identification but also underscores the potential of integrating sophisticated computational techniques with geophysical analysis. 3. The study delved into a quantitative analysis of the abnormal formation pressure causes in the #A, #B, and #C wells, revealing that the undercompaction mechanism predominates, with approximately 70%. Fluid expansion and shale diapirism were allocated 10% and 20%, respectively, offering a granular breakdown of the contributing factors to the overpressure phenomena within the basin. 4. The findings underscore the multifaceted nature of overpressure mechanisms in sedimentary basins, advocating for a comprehensive approach to their analysis. The methodology presented is readily adaptable to other geological contexts, suggesting avenues for future research that may include expanded data sets, additional machine learning algorithms, and broader stratigraphic evaluations to consolidate the model's forecasting robustness. 5. The research culminates in a significant contribution to geophysical analysis, particularly in identifying the causes of abnormal formation pressures with higher precision. The synergistic application of hierarchical clustering, machine learning, and Bayesian optimization has been demonstrated as an effective strategy for enhancing the accuracy of geological interpretations, providing valuable insights for hydrocarbon exploration and contributing to the strategic planning of production activities in analogous geological settings. Y.X. : formal analysis, investigation, writing--original draft; J.Y. : conceptualization, methodology, formal analysis, investigation, writing--review and editing, funding acquisition; Z.H. : methodology, formal analysis, investigation, funding acquisition; Q.Z. : methodology, formal analysis, investigation, supervision; L.L. : conceptualization, investigation data curation; Q.Y. : methodology, formal analysis, investigation, writing--review and editing, funding acquisition. All authors have read and agreed to the published version of the manuscript. The authors would like to acknowledge the support of the Sinopec's key scientific and technological research project (P24174, P22317) and National Key Research and Development Program of China (Grant No. 2022 YFC2806401). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Well Number** & **Stratum** & \begin{tabular}{c} **Undercompaction** \\ **Mechanism Weight** \\ \end{tabular} & \
\begin{tabular}{c} **Hydrothermal** \\ **Pressurization** \\ **Mechanism Weight** \\ \end{tabular} &
\begin{tabular}{c} **Shale** \\ **Diapirism Mechanism Weight** \\ \end{tabular} \\ \hline \multirow{6}{*}{\#A} & S1 & 79.25\% & 11.64\% & 9.12\% \\  & M2 & 69.52\% & 2.14\% & 28.34\% \\  & S2 & 72.05\% & 8.7\% & 19.25\% \\ \cline{1-1}  & M3 & 79.43\% & 10.64\% & 9.93\% \\ \cline{1-1}  & S3 & 75.92\% & 8.38\% & 15.71\% \\ \cline{1-1}  & S4 & 83.9\% & 4.57\% & 11.53\% \\ \cline{1-1}  & M5 & 70.67\% & 8.03\% & 21.3\% \\ \cline{1-1}  & S5 & 73.81\% & 10.95\% & 15.24\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Calculated weights of overpressure mechanisms for five drilled wells.