... ABSTRACT: Being able to predict the dimensions of excavation damage zones (EDZs) is crucial for the design of permeability-sensitive structures. This study focuses on predicting excavation-induced damage depth using a machine learning method called multi-layer perceptron (MLP). The inputs...

... reservoir geomechanics artificial intelligence geological subdiscipline structural geology reservoir characterization frequency sensor machine learning mechanism magnitude mining inversion mendecki objective potency excavation deformation information waveform malovichko limitation...

... learning methods (Lolon et al., 2016). Machine learning models can overcome the limitations of traditional analytical methods in correlating high-dimensional data due to their powerful nonlinear fitting capabilities (Pankaj et al., 2018). Various machine learning models have been used by researchers...

... subdiscipline mechanical earth model calibration statistical concept ray breakout classification prediction probability machine learning mem brier score accuracy scenario statistics wikipedia regime drilling probabilistic forecasting ARMA 24 439 Improve Mechanical Earth Model Calibration...

... et al, 2023a). geologist artificial intelligence fluid dynamics neural network flow in porous media geology prediction geological subdiscipline gnn mapper algorithm reservoir geomechanics machine learning dataset microstructure reservoir characterization mukerji cnn algorithm...

... combined to present the most holistic overview of the rockfall study and analyses. geologist rockfall wyllie geology battulwar kliche machine learning restitution trajectory plane rockfall trajectory experiment university software artificial intelligence algorithm coefficient...

... hydraulic fracturing geology mudrock mudstone discriminant matrix fiber injection intensity zhang artificial intelligence geological subdiscipline intensity proportion optical fiber shale ga exploration evaluation reservoir geomechanics machine learning shale ga sichuan basin quantitative...

... are given by Parson and Dahl (1971), Maleki and White (1997), Maleki and Owen (2001), Maleki et al. (2001), Maleki et al. (2023), and Maleki (2023). geology reservoir geomechanics geologist panel 1 maleki machine learning organic-rich rock artificial intelligence geological subdiscipline...

... rock structural geology rock type log analysis prediction dataset geological subdiscipline neural network sequence application information clastic rock well logging machine learning geologist correlation exponential artificial intelligence reservoir characterization normalization...

... into the application of machine learning for well kick classification, critically addressing the challenge of model generalization. It proposes innovative solutions, including a novel PCA-based method, to overcome these hurdles. This study thoroughly examines the potential and obstacles in training models to achieve...

... faults with near-vertical dips can be challenging to detect using conventional seismic imaging methods due to poor incident angles. In this study, sponsored by the U.S. Department of Energy's SMART initiative, we developed a machine learning workflow using Long Short Term Memory (LSTM) recurrent neural...

... distribution, but there are also many non-Gaussian situations that these methods cannot handle (Zhou et al., 2014). geologist deep learning neural network characterization geology artificial intelligence reservoir characterization enhanced geothermal site characterization machine learning...

... as its practical importance. classifier database geology rockfall artificial intelligence classification geologist machine learning cloud accuracy algorithm jaboyedoff remote sensing journal clutter point filter rockfall database application verification dbscan abellan walton...

...). geologist artificial intelligence machine learning cement chemistry stiffness cement property interface geology reservoir geomechanics strength steel triangle fly 0 preparation engineering slurry enhanced cement ambient consistency casing and cementing cement formulation geological...

... remains a challenging issue. sedimentary rock clastic rock complex reservoir artificial intelligence mudrock geologist mudstone optimization problem particle geology rock type reservoir characterization geological subdiscipline inversion accuracy machine learning particle filter...

... in the recovery rate of shale gas wells(Zhao et al., 2023). clastic rock geologist drilling operation wellbore design rock type reservoir geomechanics mudrock geological subdiscipline artificial intelligence machine learning sedimentary rock injection complex reservoir shale gas reservoir...

..., the temporary plugging ball is subjected to stronger drag force in the horizontal direction, and the steering tendency is slightly slower than that of small displacement. Based on SVM and RF two machine learning models to predict the blocking results, analyze and predict different temporary plugging ball...

..., searching for optimal well control schemes for the WAG flooding process will maximize development benefits and the amount of CO 2 storage into the reservoir. structural geology co 2 geological subdiscipline injection carbon storage objective geologist artificial intelligence machine learning...

... machine learning software technology torque neuron classification accuracy drilling operating condition geology identification accuracy lstm recurrent neural network application wob vibration experiment real-time identification efficiency ARMA 24 666 LSTM Recurrent Neural Network Based...

... ABSTRACT: This paper presents a comprehensive analysis of the correlation between static and dynamic mechanical properties of sedimentary rocks from Australian basins. By leveraging both empirical and machine learning techniques, we provide valuable insights into the predictive capabilities...