...-driven methods by constraining the models to adhere to the general production trends. In this paper, we develop a physics-constrained data-driven model by embedding physical flow functions into neural network models. Since the performance of the physics-constrained model depends on the relevance...

... is the possibility of negative transfer, that is, transferring incorrect or irrelevant knowledge to the target data. In particular, the black-box nature of most data-driven models, e.g., neural networks, support vector machines, and random forest, makes it difficult to completely interpret the contribution...

... phenomenon—permeability jail. 1 7 2022 20 9 2022 14 9 2022 24 10 2022 12 4 2023 Copyright © 2023 Society of Petroleum Engineers neural network machine learning flow in porous media permeability upstream oil & gas pore fluid dynamics artificial intelligence...

... neural network (ANN) is proposed here. MLEU uses a fast and accurate ANN proxy model to predict the anisotropic shear strength of heterogeneous oil sands with interbedded shales. The R 2 values of the trained ANN models exceed 0.94 for estimating shear strengths in horizontal and vertical directions...

... & gas deep learning asia government source domain drilling operation machine learning representation artificial intelligence target sample target domain international conference united states government neural network domain adaptation wd-muda data distribution drilling data domain...

... for a convolutional neural network (CNN) layer. Eventually, data are trained utilizing you only look once (YOLO)—a one-stage object detector, hyperparameters are tuned, and model performance is evaluated using mean average precision (mAP). The collected data from fields in Alaska and North Dakota represent oil wells...

... in addition to the state of the opening control valve to train a deep neural network with a convolutional layer to output each fluid’s volume rate. The proposed method is computationally simpler than recurrent neural networks and provides similar results. However, it still requires data to train the neural...

... stability performance in crude oil was evaluated using six machine learning (ML) techniques, namely decision tree (DT), Naïve Bayes (NB), support vector machine (SVM), artificial neural networks (ANN), random forest (RF), and k-nearest neighbor (KNN). A large stability data containing 186 crude oil samples...

... condition diagnosis of rod pump through convolutional neural network (CNN). The classification results of the validation set indicate that without the mini-batch method, the recall of generated categories for pump hitting down and leakage has increased by 12 and 5.3%, respectively; in contrast...

.... reservoir simulation upstream oil & gas realization artificial intelligence elevation europe government workflow reservoir characterization algorithm united kingdom government neural network machine learning fwl fathom fault sandstone probability visualization statistics prospect...

... framework based on a bidirectional gated recurrent unit (BiGRU) and multitask learning (MTL) combined neural network (BiGRU-MTL), which can improve prediction performance by sharing task-dependent representations among tasks of multiphase production prediction. The forecasting strategies and evaluation...

... artificial intelligence deep learning reservoir characterization spallation technology application experiment drilling technology conversion efficiency neural network machine learning granite paper thermal spallation morphology electrothermal conversion efficiency debris diameter spe...

... are investigated. An artificial neural network (ANN) model is developed to predict proppant distribution in a cluster. The results identify that proppant distribution among perforations is generally toe-biased in a horizontal wellbore due to a high pumping rate. Proppants with large inertia easily miss the heel...

... (linear regression) all the way to artificial neural network (ANN) and hybrid ML models. This is the first study that comprehensively benchmarks polymer rheology models and proposes a simple, least number of coefficients, and tunable polymer-rheology model. We provide a predictive bulk rheology model...

... Engineers asia government artificial intelligence deep learning upstream oil & gas neural network machine learning united states government china government noise separation impact frequency telemetry dimension dprnn frequency separation architecture explored em telemetry snr...

... this information to predict the oil and gas distribution accurately. Adaptive mutation particle swarm optimization (AMPSO) was used to train the ML models [artificial neural network (ANN) and least-squares support vector machine (LSSVM)] and obtain intelligent prediction models (AMPSO-ANN and AMPSO-LSSVM...

... fluid dynamics waterflooding upstream oil & gas deep learning project valuation saudi arabia government reinforcement learning constraint robust optimization reservoir simulation flow in porous media artificial intelligence reservoir characterization durlofsky iteration neural network...

.... The method models a two-layer structure. The first layer consists of multiple models that were trained by different learning algorithms, such as k -nearest neighbor ( k NN), decision tree (DT), neural network (NN), and support vector machine (SVM). While the second layer was used to relearn the output...

... synthetic images was first created with the cutters appropriately identified and labeled. Using this data set, a convolutional neural network (CNN) along with other image processing techniques was applied to first identify the individual cutters and their positions on the bit and then to quantify the damage...

...Junjie Yu; Atefeh Jahandideh; Behnam Jafarpour Summary This paper presents a neural network architecture for prediction of production performance under different operating conditions by integration of domain insight and simulated production response data. The neural network topology...