... on a large amount of original geological, engineering, and dynamic data acquired from 373 hydraulically fractured horizontal wells, the flowback characteristics were systematically studied based on machine learning. Based on the data analysis and random forest forecasting, a new indicator, single-cluster...

... discrete element method (DEM) approach to investigate the velocity and solid residence time distributions in the moving bed. In this work, the flow patterns under different operating and structural parameters are studied and optimized via machine learning methods. The random Forest regression model...

... to natural gas (NG) spark ignition (SI), based on a limited number of experiments. As the best ML technique cannot be chosen a priori, the applicability of different ML algorithms for such an engine application was evaluated. Specifically, the performance of two widely used ML algorithms, the random forest...

... of compressional and shear slowness (ΔT c and ΔT s ) are considered costly and time-consuming operations. The target of this paper is to propose machine learning models for predicting the sonic logs from the drilling data in real-time. Decision tree (DT) and random forest (RF) were employed as train-based...

... work or empirical correlation from logging data are time consuming and highly cost. To overcome these drawbacks, this paper utilized the help of artificial intelligence (AI) to predict (in a real-time) the rock strength from the drilling parameters using two AI tools. Random forest (RF) based...

... the structural relationships existing between the inputs and target variables; these techniques were recently successfully applied to estimate the ROP in different wellbore shapes and through various formation lithologies. This study is aimed to introduce a random forest (RF) regression model for ROP prediction...

...-in. horizontal section until 1 day prior to the stuck pipe incident, were used to train and test three models: random forest, artificial neural network, and functional network, with an 80/20 training-to-testing data ratio, to predict the surface drilling torque. The independent variables for the model...

... is that it requires less formal statistical training and its ability to implicitly detect complex non-linear relationships between dependent and independent variables. 2 In this work, a few machine learning algorithms were compared in order to find the best model for failure prediction: Random Forest, Gradient...