# Committee Machine Learning for Electrofacies-Guided Well Placement and Oil Recovery Optimization
## 1 Introduction  
Machine learning (ML) methodologies have gained significant traction in the field of geophysics as a practical tool for addressing intricate and non-linear challenges. Machine learning uses algorithms and statistical models to analyze and draw inferences from patterns in data [1]. Data-driven discovery, the ability to extract new information from data,is one of the most powerful capabilities of machine learning for scientific applications [2]. Machine learning equips researchers with a variety of methodologies to uncover novel patterns, structures, and relationships within scientific datasets that traditional techniques may not readily reveal. General categories of machine learning include supervised, unsupervised, and semi-supervised machine learning. Supervised learning is a powerful set of tools that have successfully been used in applications spanning the themes of automation, modeling and inversion, and discovery, using a collection of examples to decipher patterns and build models that are predictive. Unsupervised machine learning algorithms find structure in a dataset that contains only input [3]. Unsupervised machine learning techniques can be categorized into six categories: hierarchical learning, data clustering, latent variable models, dimensionality reduction, and outlier detection [4]. Clustering is a form of unsupervised learning aimed at identifying hidden structures within unlabeled datasets by organizing them into distinct groups [5]. This method entails the systematic categorization of data into coherent natural clusters based on the similarities observed among various attributes, thereby aiding in the comprehension of the underlying framework. The clustering process is defined by a high level of similarity within individual clusters and a low level of similarity across different clusters. This approach is widely utilized in numerous fields, including machine learning, data mining, and pattern recognition. In the context of reservoir characterization, clustering has become a vital instrument, enabling the segmentation of reservoirs into unique electrofacies to enhance field development strategies based on the characteristics of different rock units. The proliferation of various clustering methodologies and machine learning techniques has significantly improved the process of reservoir clustering, minimizing interpretational biases and addressing the challenges associated with accurately delineating the reservoir into distinct zones [6]. The application of data science and artificial intelligence (AI) in the fields of Earth sciences, reservoir engineering, and the energy industry has significantly expanded in recent years. Current energy industry practices lead to the generation of large amounts of data that can be better understood and explored using artificial intelligence, increasing the efficiency of reservoir engineers through readily available data analysis tools [7]. AI techniques have found many applications in Earth sciences and petroleum engineering. For instance, machine-learning techniques have been applied to study earthquakes [8], predict petrophysical properties [9; 10; 11], reservoir simulation [12; 13], and various aspects of oil recovery and optimization [14; 15]. Machine learning, especially deep learning, can also aid in the analysis of inverse problems. Deep neural networks, with architectures informed by the inverse problem itself, can learn an inverse map for critical speedups over traditional reconstructions, and the analysis of the generalization of ML models can provide insights into the ill-posedness of an inverse problem [16] and structural health monitoring [17; 18]. Well placement and distribution and assessing high porosity and permeability areas is a complex problem that can be addressed using machine learning. Several studies have improved on well placement by traditional methods using machine learning methods, specifically individual tree-based machine learning methods [19; 20; 21; 22].