Determination of geomechanical zones based on evaluation of Unsupervised Machine Learning algorithm methods

Machine Learning algorithms have widely been adopted to group well log measurements into distinguished lithological groupings, known as Facies/Geomechanical units. This procedure can be achieved using either unsupervised learning or supervised learning algorithms. Supervised learning is the most common and practical of machine learning tasks and it is designed to learn from the example using input data that has been mapped to the correct output. In this research, we can run the modeling using Unsupervised Learning, where we authorize the algorithms to recognize underlying patterns within the data that may not be easily visible during data exploration. Therefore, an unsupervised learning method has been used to determine geomechanical zones. In this method, we give one's consent/assent to algorithms to identify subsurface patterns using data that may not be easily visible during data exploration. First, the application of practical methods of machine learning algorithms, including the K-mean model, Based Spatial Clustering of Applications with Noise (DBSCAN), Hierarchical Agglomerative Clustering (HAC), and Gaussian mixed model, will be explained, And then in this research, the best method for predicting petrophysical layers will be presented and compared the results with an established Lithofacies curve. The required programming is done in a Python environment. In this regard, after well processing, The XGBoost and Multi-Layer Perceptron Neural Network Algorithms have been used to predict the missing data. The optimal number of clusters is obtained using an ‘elbow’, In this article, as the title suggests, Four methods are used in cluster analysis unsupervised machine learning algorithms, but in petrophysical, geological, and geomechanical realities, data seldom conform to good circle patterns. Whereas if the data clusters are circular, K-Means clustering and Hierarchical Agglomerative Clustering( HAC) work great. Therefore, it is better to use the Gaussian mixed models (GMM) method.