... With the increasing application of deep learning in geoscience, In this paper, a new method of fault-karst reservoir identification based on ResU-Next model is proposed on the basis of previous research. Traditional learning and prediction of karst caves and faults are conducted separately...

... three-dimensional seismic dataset shows that the method can improve the accuracy of seismic records and seismic attribute identification for fractures, and the comparison with the existing methods proves that the post-stack fracture enhancement technique based on phase decomposition has a great...

... geophysicist seismic inversion reservoir characterization offshore mexico impedance identification matrix characterization elastic inversion s-impedance Fracture and matrix characterization using wide-azimuth and multi-component seismic data: a case study from offshore Mexico Romain Baillet*, Nawal...

... data studies building on the work of La Marca and Bedle (2022). The goal is ambitious: to improve the identification of intricate channel features in depositional environments. By focusing on the careful selection of attributes and applying dimensional reduction (DR) techniques, we aim to significantly...

... Strike-slip fault identification is an important work in the oil and gas exploration. When it associates many karst caves, the accuracy of traditional methods such as coherence, curvature will be greatly affected. Although the deep learning method can identify some complex faults...

... with high resolution and coverage is developed to characterize the reservoir space structure and identify fluid types in the near wellbore regions. On the one hand, the maximum connected path algorithm is utilized to realize the automatic identification of fractures, pores and vugs, then respectively mark...

... It is difficult to identify small faults in structural interpretation, especially in the area where the formation occurrence changes greatly and the signal-to-noise ratio of seismic data is low. The common fault identification method is mainly coherent attribute, and the influence of formation...

... through rapid detection of outliers, efficient identification, curation of missing logs data, and the effective normalization of lithofacies to construct regionally consistent subsalt earth models. These constructed earth models are geologically compliant and generate seismic volumes properly tied...

... property predictions at new identified areas. The up dip entrapment was found to be controlled by faults and low porosity zones. The entire effort culminated in identification of lead areas with better understanding of favorable petroleum systems elements i.e. better reservoir sections, seal properties...

... continental environment rock type depositional environment north america government structural geology identification artificial intelligence geologist sedimentology geological subdiscipline exploration geophysicist quantitative identification machine learning energy 10 professor permeability...

... artificial intelligence geologist neural network reservoir characterization machine learning american association exploration geophysicist accuracy confusion matrix classification international geology vehicle identification applied geoscience application large-sized vehicle dnn energy 10...

... When identifying fluids in anisotropic reservoirs, the impact of medium anisotropy on fluid identification needs to be considered. At the same time, during seismic wave propagation, pore fluids can experience wave-induced fluid flow, resulting in squirt flow. In order to address these issues...

... ultra-deep fracture-cavity carbonate reservoirs with complicated fault-karst system. geologist deep learning upstream oil & gas neural network reservoir characterization seismic model geology china government impedance identification mechanism international reservoir identification...

... This paper presents a novel machine learning workflow for automated fracture and baffle identification in completion design optimization for unconventional reservoirs, engineered geothermal systems, and CO 2 storage sites. The current manual interpretation process of wellbore microimaging logs...

.... However, the low resolution of fault probability and identification of false-positive faults creates uncertainty locating the true faults. In this paper, we first performed a structure-oriented filter on the synthetic seismic data to improve the quality of the training data and applied spectral...

... A machine learning (ML) method has been developed for frac-hit identification. It utilizes Long Short-Term Memory (LSTM) and Multilayer Perceptron (MLP) neural networks to innovatively identify the frac hits due to hydraulic communications between fracking child wells and producing parent wells...

... containing more than 19000 3D synthetic seismic volumes and the corresponding channel labels, which are shown to be sufficient to train a good channel identification network. After training with only the synthetic dataset, the network automatically learns useful features that are important for channel...

... modeling. upstream oil & gas fracture characterization fracture china government deep learning algorithm reservoir characterization neural network machine learning asia government artificial intelligence identification international prediction small-scale fracture exploration...

... structural geology machine learning artificial intelligence information soulution enhance carbonate reservoir reservoir characteristic identification upstream oil & gas reservoir characterization geology-geophysics-data mining exploration geophysicist american association...

... stratigraphic facies classification offshore brazil topology identification society We present results of a multi-attribute, machine learning study over a pre-salt carbonate field in the Santos Basin, offshore Brazil. These results test the accuracy and potential of Self-organizing maps (SOM...