... deformation fines migration production control permeability blockage-removing mechanism particle efficiency uplift wellbore injection machine learning simulation permeability coefficient mechanism bitumen migration operation Abstract Fine migration is considered to be the primary...

... upstream oil & gas drilling operation bit design artificial intelligence machine learning neural network calgary bit selection strength reservoir geomechanics efficiency predictorname trainregressionmodel hardness compressive strength variablename correlation predictfcn...

... intelligence reservoir characterization gamma ray geophysics lithology neural network deep learning log analysis prediction sequence upstream oil & gas well log prediction deep sequence learning well logging neutron porosity machine learning application information dts journal...

... Abstract The objective of this paper is to highlight a new machine learning application to predict a synthetic caliper log using other available Logging While Drilling (LWD) well logs. LWD logs and wireline (WL) mechanical caliper logs for 17 tight gas wells, with more than 120,000 depth data...

... artificial intelligence perforation well 3 re-fracturing reservoir petroleum exploration hydraulic fracturing potentiality selection saturation shale reservoir matrix engineering north america well 5 figure application wang fracture machine learning development wellbore well 2...

... gradient drilling operation neural network upstream oil & gas decision tree learning wellbore design well logging machine learning abdulraheem estimation arabian journal artificial intelligence reservoir characterization petroleum engineer society johnson wellbore integrity...

... upstream oil & gas artificial intelligence directional drilling drilling operation well logging public aape log analysis machine learning dtsm real-time prediction predicted sonic log surface drilling parameter rmse saudi aramco reservoir characterization slowness algorithm...

... characteristics can lead to higher efficiency of tunnelling projects and prevent any cost or time overruns (Yan-lin et al. 2011; Hossin and Sulaiman 2015; Zhang and Zhu 2018). optimization problem machine learning lithology borehole prediction decision tree learning reservoir characterization tunnel...

... algorithm machine learning upstream oil & gas sand control prediction completion installation and operations sand/solids control random forest hyperparameter sand control completion technique k-nearest neighbor nait amar supervised ml algorithm artificial intelligence selection...

.... In A guided tour of artificial intelligence research. ed. P. Marquis, O. Papini and H. Prade, 447 484. 24. Wagstaff, K., C. Cardie, S. Rogers and S. Schrödl. 2001. Constrained k-means clustering with background knowledge. In Proceedings of the Eighteenth International Conference on Machine Learning, San...

... neural network model challenging to express the spatial correlation of logging data with in-situ stresses (Zhang et al., 2018b). Recently, with the widespread application of machine learning in the fields of science and engineering, many researchers suggested using data-driven methods to solve geological...

... using machine learning tools. The study proposes a new approach for utilizing the random forest technique for developing a sonic prediction model for real-time deployment in drilling operation. The model was developed using drilling and sonic data for composite drilled formations with different...

...ARMA/DGS/SEG International Geomechanics Symposium 21 95 Prediction of shear wave velocity using machine learning technique, multiple regression and well logs Lin Shi, Jiachen Zhang* 1. State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum (Beijing), Beijing...

...ARMA/DGS/SEG International Geomechanics Symposium 21 9 Gas Injection Optimization Under Uncertainty in Subsurface Reservoirs: An Integrated Machine Learning-Assisted Workflow He, X., Qiao, T., Santoso, R. and Hoteit, H. King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi...

... then be used to estimate formation Breakdown Pressures (Fatahi et al., 2016). In this work, we will investigate the efficiency of a machine learning algorithm to predict formation breakdown pressures using Neural Networks. The novelty of the method investigated here comes from the fact that the training set...

... machine learning hydraulic fracturing correlation artificial intelligence upstream oil & gas flow in porous media fluid dynamics reservoir characterization complex reservoir oil production reservoir pressure porosity prediction effective stress stress-dependent permeability...

... this approach, artificial neural network (ANN) machine learning algorithm was used. ANN is very common artificial intelligence method that could be used in regression or classification problems. 2288 data points were used to construct and test the model, while another 1667 data points were hidden from...

... mud properties is required when drilling these units in future wells. machine learning reservoir geomechanics reservoir characterization log analysis neural network pore pressure artificial intelligence well logging upstream oil & gas mc2 unit structural geology reservoir unit...

... 1.8%). This work shows the reliability of the proposed models to forecast the pressure gradient from both mechanical and hydraulic drilling data while drilling. machine learning reservoir characterization reservoir geomechanics artificial intelligence abdulraheem prediction well logging...

... failures (e.g., breakouts). ARMA/DGS/SEG International Geomechanics Symposium 20 49 Integrating Monte Carlo Simulation, Machine Learning and Physics-Based Solutions to Estimate In-Situ Stresses Murtadha J. AlTammar and Khalid M. Alruwaili Saudi Aramco, Dhahran, Kingdom of Saudi Arabia Copyright 2020 ARMA...