... and modeling various physical processes on the obtained models. Our work proposes using deep learning methods for mineral and pore space segmentation instead of classical methods such as threshold image processing. Deep neural networks have long been able to show their advantages in many areas of computer...

... volumes of injection wells without performing full-scale or sector hydrodynamic simulation. To predict production, we use machine learning methods (based on decision trees and neural networks) and methods for optimizing the target functions. As a result of this research, a unified algorithm for data...

... for assessing the pore pressure distribution across the field, based on the usage of neural network technology. This approach potentially eliminates both of the above disadvantages from the pore pressure model building. artificial intelligence deviation reservoir geomechanics pore pressure measurement...

... (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...

... Abstract We propose a novel approach to data-driven modeling of a transient production of oil wells. We apply the transformer-based neural networks trained on the multivariate time series composed of various parameters of oil wells measured during their exploitation. By tuning the machine...

... Abstract The article discusses the results of long-term forecasting of non-stationary technological modes of production wells using neural network modeling methods. The main difficulty in predicting unsteady modes is to reproduce the response of producing wells to a sharp change in the mode...

... 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...

... evolution properties of a dynamical system and the ability of neural networks to quantitively describe poorly understood multiphase phenomena and can be considered as a hybrid solution between data-driven and mechanistic approaches. The continuous latent ordinary differential equation (Latent ODE) approach...

... classification based on neural networks and regression modeling implemented using machine learning are employed. It was found that the flow rates of liquid and gas do not affect the results of measuring the water cut due to the high frequency of the impedance measurements - up to 100 thousand measurements per...

... Summary This paper presents the efficiency of using artificial neural networks for solving problems of processing and interpreting geophysical data obtained by scanning magnetic introscopy. Neural networks of various architectures have been implemented to solve the problems of processing...

... systems. We demonstrate that if the forward model is a neural network, gradient backpropagation becomes naturally involved both in model training and adaptation. In our research we compare 3 adaptation strategies: variation of reservoir model variables, neural network adaptation and latent space...

... the possibility of knowledge transfer across different fields and the construction of the ranking model allowing fast expertise conduction of proposed intervals and evaluation of the proposed method on mature assesses. The proposed approach is based on deep learning and artificial neural networks architectures...

... of the saturation type for potential reservoirs and also the prediction of porosity and permeability was chosen. The article discusses the application of three machine-learning algorithms: the support vector machine method, the neural network and the random forest algorithm. The existing problems of the studied...

... of the located events, which provide information about the magnitudes, modes and orientations of the fractures, are obtained through moment tensor inversion of the recorded waveforms. In this paper, we propose a deep neural network approach to solve the above challenges, in real-time, and increase the efficiency...

... characterized and a special algorithm for wells assignments to an existing cluster was developed, that is done by: Wells clustering depending on their petrophysical properties derived from well logs interpretation via k-means algorithm. Wells classification with a use of neural network...

... algorithm in which the set of PVT correlations and the segment model are utilized. The distinguishing feature of the utilized approach is a data-driven segment model required for calculating the pressure drop along the chosen part of the pipe. This model is based on Artificial Neural Networks (ANNs), which...

... wave velocities, Thomsen parameters) cannot be retrieved without rigorous assumptions or additional data (e.g. from deviated borehole). In the present work, we perform a sonic data inversion by using a machine learning approach, more specifically, the convolutional neural network. The main advantage...

... specialist isochron map software package horizontal well boundary algorithm geological support time domain wave field october 2020 methodological approach seismogeological model drilling horizontal well neural network society of petroleum engineers In the process of geological support...

... for FBHP. Therefore, artificial neural network (ANN), functional networks (FNN), and long-short term memory (LSTM) models were utilized in this paper. The designed models have been validated over a diverse range of data sets. More than 30,000 data points were collected from various wells with wide range...

... is that it allows considering a complex set of time dependencies, taking into account their mutual influence. In order to account for the dependencies between physical quantities and time, a model using probabilistic neural networks has been developed allowing for retrospective filtering and data filtering...