..., data imputation was conducted by incorporating geographic information and physics guided feature engineering, through which the imputation is more accurate compared to simple imputers. For machine learning algorithms, polynomial regression was first evaluated as the baseline model and various machine...

... witnessed impressive growth since the ML technology is sufficiently precise, quick, reliable, and cost-effective to model various linear and non-linear problems. In this study, three robust ML algorithms comprising the Decision Tree Regression (DTR), Random Forest Regression (RFR), and Extra Tree...

... estimation allowing mitigation of tubular extraction risk during operations planning. regression calculation shear pressure novel shear ram well control equipment linear equation novel linear equation requirement offshore technology conference drilling equipment shear ram shear force blade...

... performance comparison milling algorithm offshore technology conference rate of penetration test data mape machine learning section milling process data-driven performance optimization regression bha type model performance algorithm train error section milling Introduction...

..., such as regularized regression, polynomial regression, and neural networks. The optimized integrative model is deployed on a platform at rig for use at real-time drilling operations. algorithm rheology drilling fluid management & disposal variance score ensemble method shirangi neural network...

... of the soil parameters is based on deepwater CPTu data from Brazilian offshore basins. Three analytical approaches are used in this study (linear and non-linear regressions: second and third degrees). Moreover, Artificial Neural Networks (ANN) (dense, convolutional and recurrent networks) are also employed...

... ). Electrofacies characterization and permeability predictions in carbonate reservoirs: role of multivariate analysis and nonparametric regression . In: SPE annual technical conference and exhibition ( 3–6 October ), Houston, Texas . https://doi.org/10.2118/56658-MS Lee , S. H. , Kharghoria...

... and calculated data continuously being stored into the system, using big data technologies for handling the large volume data. machine learning physical model operation digital twin architecture model calculation multi-physic analysis torque upstream oil & gas regression artificial...

..., Artificial Neural Networks, Support Vector Regression, Ridge Regression, and Gradient Boosting Machine and the appropriate hyperparameters were selected. A successful model was chosen based a minimized deviation from planned trajectory, minimized tortuosity, and maximized ROP. A MAE of 10% was achieved...

..., could be applied to solve high-level, non-linear problems, such as evaluating gas hydrate risk based on measurable process parameters. In this work, several machine learning techniques, such as regression, classification, feature learning with an algorithm/framework like support vector machine (SVM...

... machine learning performance measure completion regression upstream oil & gas dataset drillstem/well testing production monitoring calculation Abdi , Hervé. 2010 . " Partial Least Squares Regression and Projection on Latent Structure Regression (PLS Regression) ." Wiley...

... allocation and sustainable field development. A major hurdle faced in subsurface characterization of assets is the degree of dependency between attributes and, the often non-linear behavior of these attributes. One way of overcoming these limitations is regression analysis; however, even in a high accuracy...

... for seven Facies classification algorithms, which are either supervised learning or regression based. The algorithm are Nave Bayes Classifier (NBC), Logistic Boosting Regression (LogitBoost), Linear Discriminant Analysis (LDA), Multinomial Logistic Regression (MLR), Tree-Based Classification Models (RPART...

... data into permeability modeling is the Multiple Linear Regression, which considers the least-square equation to estimate the coefficient of parameters in the linear modeling. However, there are many other algorithms that can be used for more accurate modeling and prediction of formation permeability...

... Abstract This paper describes a new procedure of model selection in linear regression analysis to efficiently model and predict the formation permeability in non-cored intervals. The simplest way of model selection in regression model is to adopt stepwise elimination that depends...

... areas. Secondly, long term distributions of maximum wave height ( H max ) crest elevation ( C max ) are derived following the storm event based approach. Thirdly, short term distribution of wave period conditional on the large individual wave height in a sea state is given by multiple regressions...

... packer formation evaluation wellbore feet bml valve eni petroleum regression log analysis salt canopy drilling operation reservoir 2 well logging displacement otc 18298 upstream oil & gas completion installation and operations pressure regression fluid loss perforation...

... characterization cape town exploration sediment orange river deposit regression erosion continental shelf interpretation metals & mining structural geology west coast diamond exploration upstream oil & gas inner shelf diamond diamondiferous deposit bedrock middle shelf river concession...

... data are utilized. Mul- tiple linear regression analysis is applied for the calculation of the mean value functions and all related statistics. The assump- Reference and illustrations at end of paper. tion of the lognormal distribu- tion of both variables is reason- ably well verified. For this purpose...