## 3 Characteristics of Sequences and Sedimentary Facies
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Microfacies** & **Logging Characteristics** & **Thickness (m)** &
\begin{tabular}{c} **Typical Picture** \\ **(GR SP AC LLD LLS)** \\ \end{tabular} \\ \hline Estuary bar & Primarily fine sandstone, the GR curve & & \\  & exhibits low-amplitude funnel-shaped & & \\  & morphology. Despite sandy & & \\  & composition, the SP curve retains & & \\  & moderate–high-amplitude negative & & \\  & anomalies. Rapid acoustic propagation & & \\  & produces moderate–high AC values, & & \\  & though morphology transitions to & & \\  & funnel-shaped. High resistivity & & \\  & generates moderate–high LLD and & & \\  & LLS values, matching the & & \\  & funnel-shaped AC curve. & & \\ \hline Inter-distributors & Clay-rich with minor siltstone and fine & & \\  & sand, the GR curve shows & & \\  & low-amplitude, high-value patterns. & & \\  & Poor permeability results in & & \\  & low-amplitude/no SP anomalies. Slow & & \\  & acoustic velocity leads to high-value, & & \\  & low-amplitude AC curves. Low & & \\  & resistivity yields low-amplitude LLD & & \\  & and LLS values. & & \\ \hline Natural levelee & Thin interbeds of fine sandstone, & & \\  & siltstone, and mudstone display GR & & \\  & curves with moderate-high amplitude & & \\  & and box/bell-shaped morphology. & & \\  & Good permeability causes & & \\  & moderate–high-amplitude negative SP & & \\  & anomalies. High acoustic velocity & & \\  & produces moderate–high AC values, & & \\  & consistent with GR morphology. & & \\  & Elevated resistivity generates & & \\  & moderate–high LLD and LLS values, & & \\  & mirroring GR/AC patterns. & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._  
Figure 2: Diagram of typical bedding types: (**a**) horizontal bedding; (**b**) parallel bedding; (**c**) inclined bedding; (**d**) cross-bedding; (**e**) massive bedding; (**f**) flaser bedding. Based on the determination of sedimentary facies types, core-log calibration of key cored wells (e.g., Well W1-1-J31) must be conducted to establish a log-derived micro facies model for the entire study area, thereby providing the foundation for sedimentary microfacies mapping and subsequent analyses (Figure 3). The classical sequence stratigraphy theory typically defines initial transgressive surfaces (ITSs) and maximum flooding surfaces (MFSs) as sequence boundaries, allowing for the identification of three major system tracts, the lowstand system tract (LST), transgressive system tract (TST), and highstand system tract (HST), as shown in Figure 4, a schematic diagram of the three major units of sequence stratigraphic domains. These tracts comprehensively document sea-level fluctuation cycles. The lowstand system tract develops at the basement of stratigraphic sequences, characterized by slow vertical aggradation and normal regression deposits [22; 23]. The transgressive system tract, situated between the ITS and MFS, exhibits regressive stacking patterns due to backstepping depositional processes. In contrast, the highstand system tract overlies the TST and is dominated by progradational str  
Figure 3: Lithology-Log Relationship Diagram. This study employs well logging curve image datasets combined with sequence system tract classifications to identify morphological characteristics of well logging facies, thereby providing critical diagnostic criteria for sedimentary microfacies recognition. For example, Figure 5 illustrates a sedimentary microfacies pattern derived from well logging facies analysis. The diagram utilizes natural gamma ray (GR), spontaneous potential (SP), acoustic (AC), latorolog deep resistivity (LLD), and latorolog shallow resistivity (LLS) curves. By integrating their morphological features with lithological data obtained from stratigraphic exploration at relative depths, a comprehensive dataset for sedimentary microfacies identification is constructed.