... challenging. Hence, the aim of this paper is to revisit some existing studies and propose practical RDP charts based on basic machine learning algorithms such as artificial neural network classifier. Historical records of rockburst compiled from Australian mines were employed for this purpose. Overall...

...; these will be presented in the literature review, with their pros and cons. The focus of this paper is to apply machine learning algorithms to synthesize the shear slowness log. Our models are trained and tested with log data from 27 wells drilled in the Bakken petroleum system, Williston Basin. Logging data include...

... prediction cnn dropout well log generation yian wong upstream oil & gas well logging dataset exploring advanced neural network architecture neural network log analysis bi-lstm algorithm gradient descent probability convolution mc dropout complexity ARMA 22 0261 Exploring Advanced...

... amanbek gradient recovery kazakhstan us rock mechanic geomechanics symposium wavelet transformation algorithm lightgbm debit accuracy artificial intelligence dataset simulation maintenance optimization ARMA 22-415 Time-series event prediction for the uranium production wells using machine...

... wang journal algorithm evaluation ARMA 22 375 Enhanced Rate of Penetration Prediction with Rock Drillability Constraints: A Machine Learning Approach Fan, Y.D.1,2 ,Jin, Y.1,2,3*, Pang, H.W.2,4, Wang, S.G.2,3 1. Beijing, College of Artificial Intelligence, China University of Petroleum, China 2...

..., and 28 days. The chemical-mineralogical synthesis of the cement and fineness factor significantly affects the cement strength for the well’s life cycle to avoid unwanted fluid leakage. This study aims to develop two Artificial Intelligence algorithms: Artificial Neural Networks (ANN) and Support Vector...

... ABSTRACT: The fractal dimension theory is applied to the fracture network that impacts the basement and the Paleozoic formation in the northern part of the Hoggar area. The first technique used is the center distance algorithm, which considers only the fault centers distribution as a fractal...

... and is difficult to obtain the global optimal solution. In this paper, an optimization method of hydraulic fracturing design for horizontal shale gas well based on artificial neural network (ANN) and genetic algorithm (GA) is proposed. On the basis of collecting geology, engineering, and production data...

... the number of layers, the number of neurons, activation function and learning rate are explored via the Bayesian optimization algorithm. The field experiment shows that the proposed Vs prediction model outperforms the traditional conversion method and can be used as a promising tool for accurate Vs...

... computed tomography scanning data of open and closed joints of rock cores retrieved from a 4.2-km-deep well at the Pohang EGS site. We developed an algorithm to update the orientations of joint contacts automatically and overcome the mismatch between joint roughness and contact direction especially...

... experiment sandstone mechanism lvd fracture upstream oil & gas reservoir characterization gerya algorithm nucleation sensor berea sandstone simulation acoustic emission solid earth geophysical research clvd laboratory experiment equation ARMA 22-0621 A Study of Progressive Failure...

... wellbore integrity machine learning boualam drilling conception drillstem testing wellbore design well logging algorithm log analysis reservoir characterization mechanical earth model rasouli zoback application symposium estimation new perspective volve field variation ARMA 22 441...

... resolution probability fracture intensity connectivity r-dis-frag morphology table 1 implementation algorithm grid orientation figure 2 polyhedral ARMA 22 0556 Rock-Discontinuity-Fragmentation (R-Dis-Frag): a computer package for the characterization of large-scale fractured rock masses...

... into a 3D point cloud for various purposes (e.g. 3D map construction, quality inspection for huge parts). upstream oil & gas machine learning barton terrestrial laser scanner roughness estimation dataset algorithm artificial intelligence noise specimen joint roughness neural network...

... ways in engineering rock fragmentation, drilling and blasting have been widely utilized in rock engineering for centuries. With the rapid development of the hardware and the numerical algorithms, numerical modeling is either economical or efficient in predicting and evaluating the results of blasting...

... ABSTRACT: This study investigates the performance of yielding bolt in a weak rock mass tunnel with high in-situ stress conditions. The genetic algorithm and the Komamura-Huang rheological model are combined to perform an inversion analysis of the mechanical parameters of the weak surrounding...

... and in-situ stress conditions. The ML techniques are combined with the results obtained from the commercially available finite difference code FLAC, widely used in geotechnical modeling. The ML algorithm with maximum accuracy is then used to determine the input parameters using the synthetic monitoring data...

..., so that a particular input, according to a learning algorithm, leads to a specific target output [8, 9] . upstream oil & gas network artificial intelligence machine learning neural network probabilistic neural network deep feed-forward neural network s-wave esmaeilpour dfnn...

... magnitude mechanism geothermal reservoir microseismicity geophysical research reservoir geomechanics fracture machine learning anisotropy algorithm orientation inversion geyser constrained time-lapsed windowed microseismic imaging california solid earth evolution seismicity dynamic...

... parameter and formation lithology was analyzed by correlation analysis algorithm, and 13 drilling parameters were selected as model inputs. And then random forest (RF) and XGBoost algorithms are used to develop lithology identification models respectively. The results show that the XGBoost model has...