... 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...

... 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...

... 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...

... 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...

... 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...

.... 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...

... 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...

..., and induced seismicity for both technologies. reservoir characterization machine learning drilling measurement enhanced recovery risk management well logging annular pressure drilling hydraulic fracturing drilling data acquisition artificial intelligence wellbore design reservoir...

... ABSTRACT: Machine learning techniques are supposed to be a great ally in binary classification tasks for the inventory of landslides caused by the seismic action of earthquakes. These seismic-induced landslides can be used in the creation of landslide hazard maps, from the cartography...

... presents the case studies about Blowout Preventer (BOP) and Surface Controlled Subsurface Safety Valve, (SCSSV) reliability used in the pre-salt environment. machine learning reservoir characterization structural geology concentration artificial intelligence upstream oil & gas...

... ABSTRACT: Concerns about seismic hazards associated with fluid injection necessitate close monitoring of geothermal reservoirs to anticipate and minimize the threat of induced earthquakes. Here, we apply machine learning to a series of datasets from laboratory-scale friction experiments...

... distribution of the individual microearthquakes from the minimization of the residuals. Supervised machine learning (ML) algorithms were trained and tested to the extracted features from the waveforms registered by the closest station to estimate the locations of the source events. The results have shown...

... for the distribution of dips. For a joint with no overall inclination, the value of A 0 is anticipated to be approximately 0.5, as half of the asperities would face one way while the other half would be facing the other way. artificial intelligence sawcut specimen machine learning complex reservoir...

... time and cost (Tingay et al., 2009; Zoback, 2007). artificial intelligence fuzzy logic abdulraheem machine learning neural network pore pressure gradient transfer function reservoir characterization upstream oil & gas elkatatny formation pressure prediction prediction petroleum...

... provided by transportation authorities and other governing bodies have been analyzed (McCauley et al., 1985; Delonca et al., 2014). us government rockfall activity artificial intelligence calculation classification rock slope machine learning rockfall bedrock floyd hill point cloud...

... 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...

... 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...

... to calibration, limiting their applicability. Therefore, the accurate prediction of ROP is still a problem to be solved. artificial intelligence rop prediction variation machine learning rate of penetration deep learning model prediction rop neural network neural network model prediction...

... in unconventional reservoirs, the fracture networks simulation techniques include local grid refinement (LGR), discrete fracture model (DFM) and embedded discrete fracture model (EDFM) (Xu et al., 2019; Sepehrnoori et al., 2020). machine learning complex reservoir modeling & simulation lgr result...

... the elastic behavior of a rock sample during a dynamic test at high confining pressures. mud logging / surface measurements drilling data acquisition reservoir characterization machine learning wellbore integrity artificial intelligence upstream oil & gas indentation modulus complex...