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Keywords: neural network
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Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 20–22, 2022
Paper Number: URTEC-3702980-MS
... for prediction accuracy. After several rounds of model refinement we then selected a deep-learning neural network architecture as the model that offered the best combination of predictive accuracy, prediction speed, training efficiency, and model portability. The accuracy of the selected machine learning model...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 20–22, 2022
Paper Number: URTEC-3707202-MS
... learning and deep neural network to estimate and predict the geomechanical properties of the Permian Basin. The log-derived prediction algorithm includes (a) Single-Well prediction, 75% of log data of a single well is used as a specimen for training the Bi-LSTM, and the rest 25% of data of the same well...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 20–22, 2022
Paper Number: URTEC-3702606-MS
... extrapolation ability and require sufficient training data, where training an under-determined neural network predictive model with limited data can result in overfitting and poor prediction performance. Unlike statistical models, physics-based models impose causal relations that can provide reliable...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 20–22, 2022
Paper Number: URTEC-3703284-MS
... Multilayer Perceptrons (MLP), Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), Convolutional Neural Network (CNN), Long-term Recurrent Convolutional Network (LRCN), and Gated Recurrent Unit (GRU) with two statistical methods (Exponential Smoothing, and Seasonal Autoregressive Integrated Moving...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 20–22, 2022
Paper Number: URTEC-3701806-MS
... Abstract Objectives/Scope This study will demonstrate an automated machine learning approach for fault detection in a 3D seismic volume. The result combines Deep Learning Convolution Neural Networks (CNN) with a conventional data pre-processing step and an image processing-based post...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 20–22, 2022
Paper Number: URTEC-3722179-MS
... that integrates innovative and revolutionary machine learning techniques, which embed recurrent neural networks and Seq2Seq architectures commonly used for language processing in translation tasks for estimating in this paper future oil rates. The method uses a type of recurrent neural network known as LSTM...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 20–22, 2022
Paper Number: URTEC-3721904-MS
.... Additionally, this study serves as a guide to help improve the DL applications in production forecasting. production forecasting artificial intelligence modeling & simulation neural network upstream oil & gas rnn application algorithm long term dependency deep learning proceedings...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 20–22, 2022
Paper Number: URTEC-3719366-MS
... to develop a predictive machine learning model (comprising a multiple linear regression model and a simple neural network). This model has been successfully implemented in field developments to optimize child well placement and has produced improvements in performance predictions and net present value (NPV...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 20–22, 2022
Paper Number: URTEC-3723045-MS
... using artificial neural networks (ANN) and adaptive neuro fuzzy inference system (ANFIS) separately, which achieved good predicting performance in highly deviated well sections. The feasibility of using machine learning in T&D modeling were proved. However, the "independent and identically distributed...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 20–22, 2022
Paper Number: URTEC-3723466-MS
... system to update the ML model and adjust the resulting insights based on the most recent system status and newest data (Figure 2, top). The ML model combines neural network, decision tree, and advanced deep learning systems that aggregate the available datasets and applications to provide a complete...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 20–22, 2022
Paper Number: URTEC-3722944-MS
... is to develop a methodology for predicting the reservoir pressure gradient trend for the Potash Area using Artificial Neural Network (ANN) and Multilinear Regression machine learning models. The decision on what model should be used was guided by the efficiency and accuracy of the models. The study utilized...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 20–22, 2022
Paper Number: URTEC-3723688-MS
... Abstract Objective In this paper we present recent work leveraging the advances in deep learning to propose a novel deep convolutional neural network Focal Mechanism Network (FMNet) for estimating the location, magnitude and source focal mechanisms of earthquakes rapidly, using full...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 20–22, 2022
Paper Number: URTEC-3723682-MS
... production control machine learning production forecasting production monitoring hydraulic fracturing deep learning artificial intelligence neural network reservoir surveillance complex reservoir water rate history input forecaster completion characteristic architecture history...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 20–22, 2022
Paper Number: URTEC-3723793-MS
.... In this study, we showcase an efficient workflow composed of the following key components to overcome current challenges: • Develop a physics-guided deep convolutional neural network (CNN) trained based on past simulation results. The CNN predicts enhanced permeability fields post stimulation, which represent...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 20–22, 2022
Paper Number: URTEC-3723603-MS
.... It can be utilized to efficiently optimize development strategy solely based on a 3D earth model without utilizing a fracture or reservoir simulator. neural network simulation machine learning deep learning training dataset artificial intelligence upstream oil & gas optimization alf...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 20–22, 2022
Paper Number: URTEC-3723965-MS
... Abstract Developing a reliable deep learning model for new unconventional reservoirs, is often constrained by the limited number of wells available. Transfer learning is a useful approach to alleviate data needs in training a neural network. This involves storing knowledge gained while solving...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 20–22, 2022
Paper Number: URTEC-3725863-MS
... of porosity and permeability. Different machine learning algorithms have been developed including Linear Regression (LR), Artificial Neural Network (ANN), Random Forest Regressor (RFR), Extreme Gradient Boosting (XGBoost), Adaptive Booster Regressor (AdaBoost), and Support Vector Regression (SVR), to predict...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 26–28, 2021
Paper Number: URTEC-2021-5418-MS
... to explore the possibility of using this technology in the petroleum industry to automate production forecasting to save time and cost where traditional methods may fail. In this paper, a deep Bidirectional Long Short Term Memory (Bi-LSTM) neural network was used to increase the accuracy of future production...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 26–28, 2021
Paper Number: URTEC-2021-5562-MS
... classification and segmentation, especially with the use of convolutional neural networks (CNN) and their variants. Although computationally expensive and time- consuming, there are several promising applications to classify or segment SEM images, images of core and thin sections. But we have not really...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 26–28, 2021
Paper Number: URTEC-2021-5398-MS
.... geochemical characterization energy economics artificial intelligence shale gas neural network pvt measurement machine learning equation of state structural geology reservoir characterization unconventional resource economics production gor composition dataset conocophillips geochemistry api...

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