... and identified those that performed best in segmentation. machine learning upstream oil & gas achimov formation deep neural network deep learning intensity distribution mineral composition feature map sem image neural network convolution information multi-mineral segmentation artificial...

... empirical parameter artificial neural network machine learning neural network training data ann characteristic reservoir characterization upstream oil & gas optimization pore pressure curve eaton model analytical method loss function petroleum engineer pore pressure prediction pp...

... (taking into account the limitations of the material balance method). machine learning production control production monitoring flow in porous media artificial intelligence reservoir surveillance drillstem testing asset and portfolio management reservoir simulation neural network drillstem...

... enhanced recovery hydraulic fracturing reservoir simulation fluid dynamics experiment simulator machine learning intervención de pozos petroleros artificial intelligence well intervention upstream oil & gas viscosity technical conference spe russian petroleum technology conference acid...

... the hydrocarbon recovery. In addition, the models can be helpful to perform well-testing avoiding costly shut-in operations. production control drillstem testing machine learning artificial intelligence production monitoring neural network reservoir surveillance reservoir simulation activation...

... Abstract The main topic of an article is machine learning and classification (neural net) use for prognostic lithological model creation. Moreover, research preceding stages such as attribute analysis, seismic inversion, seismogeological modeling and briefly the results of lithological...

... compared with five correlations of multiphase flow, the most suitable correlations were determined and modified, including using machine learning methods, which helped to significantly improve the convergence of calculated and actual bottomhole pressures. On the basis of the newly modified correlation...

.... It is shown that the optimal solution found by the neural network differs from the solution found by hydrodynamic modeling by 5%. At the same time, a significant gain in calculation time was achieved. production monitoring machine learning neural network production control artificial intelligence...

... field development machine learning reservoir surveillance drillstem/well testing two-phase filtration reservoir characterization artificial intelligence compressibility reservoir pressure pressure drop coefficient skin factor boundary element method production monitoring upstream oil...

... data and several dozen oil wells. The problems of seismic interpretation and its application for geological modeling are considered. We also propose several ways to overcome them. machine learning upstream oil & gas structural geology reservoir characterization seismic data correspond...

.... climate change production control reservoir surveillance flow in porous media machine learning enhanced recovery production logging production monitoring modeling & simulation chemical flooding methods artificial intelligence streamline simulation optimization problem waterflooding gas...

... policy air emission upstream oil & gas machine learning environmental law calculation simulator algorithm platform assessment module climate change optimization problem infrastructure inverse problem ontological model operation database hydrocarbon report generator ontology...

...: Kulginskoye, Shirotnoye, Yuzhno-Tambaevskoye, etc. (Tomsk Geophysical Trust, 1997-2002); Dvurechenskoe, Zapadno-Moiseevskoe, Talovoe, Krapivinskoe, Ontonigayskoe, etc. (TomskNIPIneft, 2002–2013). machine learning dependence equation forecast map bezkhodarnov artificial intelligence effective...

... and yield-stress hydraulic fracturing fluid rheology on cumulative production. It allows to develop a design for the well start-up and fracture cleanup in terms of dynamics of wellheadchoke opening. machine learning production control reservoir characterization fracturing materials hydraulic...

... for a geologist, which helps him to identify cases where our model should be applied. neural network pixel generator artificial intelligence seg technical program expanded abstract reservoir characterization upstream oil & gas dimension machine learning deep learning seismic data training...

... is compared to other known machine learning methods, such as linear regression, ensemble-based model, and recurrent neural network. In this work, the application of Latent ordinary differential equations for the problem of multiphase flow rate estimation is introduced. The considered example refers...

... Abstract The ultimate objective of this paper is to describe the experience of using a machine learning model prepared by the ensemble method to prevent stuck pipe events during well construction process on extended reach wells. The tasks performed include collecting, analyzing and cleaning...

... Abstract Under the present conditions of oil and gas production, which are characterized by mature production fields and the focus shifted towards digitalization of production processes and use of machine learning (ML) models, the issues related to the improvement of accuracy and consistency...

... measuring and current electrodes, between which there is a well's multiphase flow. Imaginary and real components of the impedance quantitatively describe the component composition of the studied oil and gas-water mixtures. In this process, machine learning methods and developed algorithms for features...

... filtration, material balance, Darcy's law and machine learning models. After calculations by each model, their forecasts are combined into a single ensemble forecast. The hybrid approach is based on the Monte Carlo method on Markov chains as a separate probabilistic model using Bayes’ formula. In this case...