Application of Machine Learning for Lithofacies Prediction and Cluster Analysis Approach to Identify Rock Type

Abstract

Nowadays, there are significant issues in the classification of lithofacies and the identification of rock types in particular. Zamzama gas field demonstrates the complex nature of lithofacies due to the heterogeneous nature of the reservoir formation, while it is quite challenging to identify the lithofacies. Using our machine learning approach and cluster analysis, we can not only resolve these difficulties, but also minimize their time-consuming aspects and provide an accurate result even when the user is inexperienced. To constrain accurate reservoir models, rock type identification is a critical step in reservoir characterization. Many empirical and statistical methodologies have been established based on the effect of rock type on reservoir performance. Only well-logged data are provided, and no cores are sampled. Given these circumstances, and the fact that traditional methods such as regression are intractable, we have chosen to apply three strategies: (1) using a self-organizing map (SOM) to arrange depth intervals with similar facies into clusters; (2) clustering to split various facies into specific zones; and (3) the cluster analysis technique is used to identify rock type. In the Zamzama gas field, SOM and cluster analysis techniques discovered four group of facies, each of which was internally comparable in petrophysical properties but distinct from the others. Gamma Ray (GR), Effective Porosity(eff), Permeability (Perm) and Water Saturation (Sw) are used to generate these results. The findings and behavior of four facies shows that facies-01 and facies-02 have good characteristics for acting as gas-bearing sediments, whereas facies-03 and facies-04 are non-reservoir sediments. The outcomes of this study stated that facies-01 is an excellent rock-type zone in the reservoir of the Zamzama gas field.

Scenario Clustering for Long-term Hydropower-Thermal Scheduling using Shape Feature Extraction

“…This impact has been discussed in the literature previously, i.e. [23,33], and it is due to the fact that this methodology suffered from the inappropriate initial centroids selection.…”

Scenario Reduction for Long-term Hydropower Scheduling using Shape-based Block Decomposition

“…However, k-means has a high dependency on the initialization of the centroid of each block, which means it could result in inappropriate clustering selection [22,23]. Nevertheless, in this paper, the k-means method is implemented to compare with other methods.…”

Analyzing the seismic attributes, structural and petrophysical analyses of the Lower Goru Formation: A case study from Middle Indus Basin Pakistan

“…At the present time, geological structure interpretation has been improved with the advancement of seismic exploration and interpretation techniques as well as advanced tools that are utilized for seismic and well logs interpretation such as seismic attributes, seismic inversion, lithofacies prediction, cluster analysis approach, reservoir quality prediction, multi-attributes analysis, deep learning, and machine learning. So, multiscale seismic dip constraint geological structure interpretation copes up the issue which integrates time-frequency decomposition of seismic data and geological structure interpretation (Ali et al, 2022;Anees et al, 2022;Hussain et al, 2022;Zhang et al, 2022).…”