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Keywords: machine learning
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Proceedings Papers

Paper presented at the 55th U.S. Rock Mechanics/Geomechanics Symposium, June 18–25, 2021
Paper Number: ARMA-2021-1446
... is needed. artificial intelligence segmentation grout part machine learning upstream oil & gas nakashima deep learning specimen cement grout fracture fusionnet automatic segmentation deep learning algorithm neural network x-ray ct imaging ct image x-ray ct ct value histogram...
Proceedings Papers

Paper presented at the 55th U.S. Rock Mechanics/Geomechanics Symposium, June 18–25, 2021
Paper Number: ARMA-2021-2164
... opposite side slip surface lem analysis slope stability analysis three-dimensional slope stability analysis particle swarm machine learning upstream oil & gas fs 1 failure surface mmo approach fem analysis multi modal failure mechanism failure mechanism ARMA 21 2164 Multi Modal failure...
Proceedings Papers

Paper presented at the 55th U.S. Rock Mechanics/Geomechanics Symposium, June 18–25, 2021
Paper Number: ARMA-2021-1025
.... With the influence of irregular shape and the wall effect, the settling behavior of particles in fracture is complex to be described accurately by the traditional data fitting methods. Artificial neural network (ANN) is a biological inspired machine learning method, which has significant advantages in solving...
Proceedings Papers

Paper presented at the 55th U.S. Rock Mechanics/Geomechanics Symposium, June 18–25, 2021
Paper Number: ARMA-2021-1113
... to select rock-breaking regions of similar components to avoid errors caused by strong heterogeneity. reservoir characterization artificial intelligence bit selection rock drillability wellbore design drilling parameter heterogeneity experimental study machine learning specimen well logging...
Proceedings Papers

Paper presented at the 55th U.S. Rock Mechanics/Geomechanics Symposium, June 18–25, 2021
Paper Number: ARMA-2021-1649
.... shale gas history matching machine learning complex reservoir natural fracture optimization problem upstream oil & gas shale gas reservoir artificial intelligence reservoir simulation shale reservoir edfm well spacing cumulative gas production probabilistic well spacing optimization...
Proceedings Papers

Paper presented at the 55th U.S. Rock Mechanics/Geomechanics Symposium, June 18–25, 2021
Paper Number: ARMA-2021-1654
... influences of frac-hit problem. artificial intelligence fracture characterization machine learning hydraulic fracturing shale gas upstream oil & gas natural fracture non-intrusive edfm method fracture sepehrnoori scenario resource technology conference complex fracture inter well...
Proceedings Papers

Paper presented at the 55th U.S. Rock Mechanics/Geomechanics Symposium, June 18–25, 2021
Paper Number: ARMA-2021-1668
..., and completion and production strategies development (Onalo et al., 2018; Oloruntobi and Butt, 2018; Yusuf et al., 2019). reservoir characterization artificial intelligence accuracy prediction log analysis upstream oil & gas elkatatny proceedings bulk density machine learning well logging...
Proceedings Papers

Paper presented at the 55th U.S. Rock Mechanics/Geomechanics Symposium, June 18–25, 2021
Paper Number: ARMA-2021-1669
.... reservoir characterization artificial intelligence log analysis complex reservoir machine learning well logging wellbore design rock property driven in-situ sonic log synthesis dataset mechanics geomechanics symposium drilling data upstream oil & gas algorithm nygaard prediction ann...
Proceedings Papers

Paper presented at the 55th U.S. Rock Mechanics/Geomechanics Symposium, June 18–25, 2021
Paper Number: ARMA-2021-1746
... ABSTRACT: Elemental analysis of the mining exploration data is important for many geochemical applications. The objective of this work is to conduct a comparison analysis of machine learning algorithms in predicting recovery of copper (Cu) from the Kazakhstan field. The data of cores...
Proceedings Papers

Paper presented at the 55th U.S. Rock Mechanics/Geomechanics Symposium, June 18–25, 2021
Paper Number: ARMA-2021-1864
... al., 2020). Researchers agree that for rock bursts to occur, a high stress and high energy environment is necessary, which is often found in deep mining conditions (Cai, 2016; Zhu et al., 2018). reservoir characterization metals & mining machine learning reservoir geomechanics...
Proceedings Papers

Paper presented at the 55th U.S. Rock Mechanics/Geomechanics Symposium, June 18–25, 2021
Paper Number: ARMA-2021-1888
.... The validated 1D MEMs provided 3D distribution of rock elastic and strength behavior along with pore pressure and stress distribution profiles for the target unconventional formation. These inputs were then subsequently employed for 3D sweet spot volume generation using innovative machine learning modelling...
Proceedings Papers

Paper presented at the 55th U.S. Rock Mechanics/Geomechanics Symposium, June 18–25, 2021
Paper Number: ARMA-2021-1907
... logs (Barree et al., 2009). reservoir characterization structural geology machine learning neural network artificial intelligence artificial neural networks-based equation empirical equation estimation mahmoud dyn elkatatny ann model dataset abdulraheem correlation empirical...
Proceedings Papers

Paper presented at the 55th U.S. Rock Mechanics/Geomechanics Symposium, June 18–25, 2021
Paper Number: ARMA-2021-1226
... and controlling stope dilution. Problems related to dilution have been reported by many mines (Diakite, 1998, Wang, 2004, Mouhabbis, 2013, Zarshenas and Saeedi, 2016). neural network design line artificial intelligence stope design machine learning prediction procceding graph metals & mining...
Proceedings Papers

Paper presented at the 55th U.S. Rock Mechanics/Geomechanics Symposium, June 18–25, 2021
Paper Number: ARMA-2021-1237
... of different depths; a physics-based machine-learning enabled flexural dispersion extraction algorithm that does not require zone-by-zone tuning of mud slowness, borehole size, and frequency filters; and a new inversion algorithm that jointly inverts the three Thomsen anisotropic parameters as well as mud...
Proceedings Papers

Paper presented at the 55th U.S. Rock Mechanics/Geomechanics Symposium, June 18–25, 2021
Paper Number: ARMA-2021-1243
... conditions (e.g., injection temperature) suitable for energy storage? 2) How long until the battery becomes fully charged initially? 3) For a fully charged battery, how long can it function to generate electricity continuously? reservoir characterization artificial intelligence machine learning...
Proceedings Papers

Paper presented at the 55th U.S. Rock Mechanics/Geomechanics Symposium, June 18–25, 2021
Paper Number: ARMA-2021-1302
... to the micro-scale is crucial in the engineering applications. Thus, it's necessary to conduct detailed laboratory experiments down to the submicron scale to obtain the mechanical characteristics of shale. reservoir geomechanics mineral phase machine learning upstream oil & gas artificial...
Proceedings Papers

Paper presented at the 55th U.S. Rock Mechanics/Geomechanics Symposium, June 18–25, 2021
Paper Number: ARMA-2021-1764
... machine learning algorithms (ML) to predict abnormal pressure zones in a study area. For this purpose, input features or parameters were obtained from classified log data commonly used in pore pressure prediction, namely gamma-ray, bulk density, and deep-resistivity. Other estimated parameters...
Proceedings Papers

Paper presented at the 55th U.S. Rock Mechanics/Geomechanics Symposium, June 18–25, 2021
Paper Number: ARMA-2021-1769
.... A machine learning framework based on Gaussian process regression (GPR) was chosen in the development of this procedure because it can assess uncertainty of the cement bond through estimation of error and confidence interval. GPR also require less training samples than conventional machine learning...
Proceedings Papers

Paper presented at the 55th U.S. Rock Mechanics/Geomechanics Symposium, June 18–25, 2021
Paper Number: ARMA-2021-1170
... fracture characterization reservoir geomechanics upstream oil & gas artificial intelligence tilt response dtfm exhibition tilt data tiltmeter fracture height modeling result machine learning hydraulic fracturing dimension microseismic event modeled tilt response microseismicity data...
Proceedings Papers

Paper presented at the 55th U.S. Rock Mechanics/Geomechanics Symposium, June 18–25, 2021
Paper Number: ARMA-2021-1108
... relation utilizing linear machine learning reservoir characterization wellbore design yagiz upstream oil & gas disc cutter artificial intelligence machine test colorado school linear rock sample lcm test rock block earth mechanics institute cutter coefficient brittleness university...

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