{2} } } \right)\)andMw. The ML predictor with the variablesD,H,Mw,D1400, andVs30learned the residual between the observation and GMPE prediction as the training data. For the ML algorithm, we used the Extra Trees Regressor in the scikit-learn Python programming package40. Table2...
This integration allows easy handling for the potential users, biological researchers that often do not have experience with programming. By integrating the CNN as a segmentation algorithm into the image label app the tool does not only allow for an easy prediction of the mask using the CNN ...
Gene expression programming GP: Gene programming MLR: Multiple linear regression OG: Orthogneiss PC: Principal component PCA: Principal component analysis SG: Sillimanite and garnet-bearing biotite gneiss D : Bulk density, g/cm3 FD: Fracture density, m−1 GR: Gamma ray, API K ...
G.M. Hamada Reservoir fluids identification using Vp/Vs ratio? Oil Gas. Sci. Technol., 59 (2004), pp. 649-654 CrossrefView in ScopusGoogle Scholar Hoffmann and Nelles, 2001 F. Hoffmann, O. Nelles Genetic programming for model selection of TSK-fuzzy systems Inf. Sci. (Ny), 136 (2001...
Fluid loss to subsurface formations is a challenging aspect during drilling operations inpetroleum industry. Several other drilling issues such as fluid influx and pipe sticking can be triggered in such scenarios, posturing a significant risk to rig personnel, environment, and economical drilling. Theref...
Centerlines and surface reconstruction. Regarding the 3D reconstruction of vessel centerlines, various deformable models10–12 and dynamic programming methods8,13,14 are suggested to avoid the epipolar geometry problem when a curve's point from one view intersects the corresponding centerline on ...
Our dataset can be used to analyze the factors influencing the productivity of NT (or CA) vs. CT. It is possible to train machine learning models to predict the probability of yield increase with NT (or CA) system (e.g. Supplementary Figs.1and2) or the range of yield changes resulting...
Alternatively, as in many other fields in the last years, the use of Machine learning (ML) techniques has become very popular for image analysis. In particular, deep learning-based s olutions9, such as CNN now dominate the field due to the superiority of their results10. Considering...
The least square support vector machine (LSSVM) is an improved SVM model that transforms the quadratic programming problem of SVM into a linear equation by establishing a fresh quadratic loss function; accordingly, it becomes capable of improving accuracy and reducing the computational burden of SVR...
Support Vector Machine UKF Unscented Kalman Filter WOB Weight on Bit WITSML Wellsite Information Transfer Standard Markup Language YP Yield Point 1. Introduction Drilling of oil and gas wells in petroleum industry is a challenging operation for hydrocarbon extraction. These challenges increase due to un...