# High-Resolution Mapping of Subsurface Sedimentary Facies and Reservoirs Using Seismic Sedimentology
## 3 Methodology and Workflow
### Step 5: Machine Learning for Ultrahigh-Resolution Interpretation (Optional)  
Machine learning (ML) is becoming increasingly important in various geoscience fields today. It can effectively learn detailed information about core and wireline-log characteristics--specifically the vertical and lateral variations in facies and lithology--by using a quality training data set to predict those characteristics in areas between wells. This process involves a complex nonlinear regrouping of seismic attributes guided by local stratigraphic and sedimentologic models. Model testing has demonstrated the potential of this approach (see Figure 9). For instance, in an interbedded formation (Figure 9a), the band-limited seismic responses (Figure 9b) display rounded bed boundaries and often miss variations in thickness. While ML can utilize single-well training data to predict some thickness variations, the boundaries remain mostly blurred. This trade-off suggests that, although there is improved resolution, some limitations still exist (Figure 9c).  
In contrast, a model based on 500 wells conveys much of the high-resolution information in the predicted section, resulting in sharp boundaries (Figure 9d). These two cases illustrate the range of improvements expected in practical AI applications. While more studies are needed to uncover the detailed mechanisms behind these processes, ML is poised to significantly enhance below-seismic-resolution thin-bed mapping using seismic data, creating numerous opportunities for exploring and developing conventional and unconventional resources.  
However, we have to agree that the application of machine learning is still in its early stages. Many recent case studies, including this example, are overly simplistic. There are many challenges remaining. For example, the field data, both seismic and well data, are not noise free, and the rock-physics model in the real world is not ideal. Without knowledge of the true impedance model, we could perform only a blind-well test to evaluate the quality  
Figure 8: Thin progradational clinoforms show a discontinuous–chaotic vertical pattern but a clear parallel horizontal pattern. (**a**) A 3D impedance model of an outcrop-based, 50 ms (150 m) thick, progradational sequence. (**b**) A vertical synthetic seismic section of a 28 Hz wavelet illustrates mostly discontinuous to chaotic reflections. (**c**) A horizontal amplitude slice showing a parallel pattern perpendicular to the progradation direction. Modified from [PERSON] et al. [23].  
of prediction and its practical value. Further research is necessary to build confidence in this technology.  
In summary, this five-step workflow is designed for thin beds (thickness \(<\) seismic resolvable limit, typically meters to low tens of meters) where the interpretability of a seismic reflection event is governed by the seismic tuning effect. It does not necessarily improve the interpretation of thick reservoirs (high tens of meters or more) when the top and base of the reservoirs can be picked out directly on reflection seismic events. When such thick reservoir beds are involved, conventional approaches (e.g., seismic stratigraphy, instantaneous seismic attributes, etc.) are still effective.