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Keywords: neural network
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Journal Articles
Journal: SPE Journal
SPE J. (2024)
Paper Number: SPE-221497-PA
Published: 12 July 2024
... speed (revolutions per minute, RPM), pump flow rate, pump pressure, weight on bit (WOB), torque, and rate of penetration (ROP), were logged in the meantime. Given the acoustic signal as input, we built 1D convolutional neural network (1D-CNN) models for drilling parameter prediction. The prediction...
Journal Articles
Journal Articles
Journal: SPE Journal
SPE J. 29 (07): 3440–3448.
Paper Number: SPE-219746-PA
Published: 10 July 2024
... neural network geological subdiscipline transformer prediction forge pilot well machine learning forecast horizon deep learning reservoir geomechanics rop efficiency rop prediction adjustment drilling performance transformer model accuracy encoder drilling efficiency rop forecaster...
Journal Articles
Journal: SPE Journal
SPE J. 29 (07): 3449–3458.
Paper Number: SPE-219747-PA
Published: 10 July 2024
... geologist neural network rock type reservoir geomechanics screenout geology proppant artificial intelligence machine learning geological subdiscipline clastic rock complex reservoir hydraulic fracturing mudstone supplementary material proppant injection fracture operation screenout...
Includes: Supplementary Content
Journal Articles
Journal Articles
Journal: SPE Journal
SPE J. 29 (07): 3651–3672.
Paper Number: SPE-219749-PA
Published: 10 July 2024
... Copyright © 2024 Society of Petroleum Engineers geologist sedimentary rock deep learning coal seam gas shale gas reservoir surveillance reservoir characterization neural network rock type coal bed methane reservoir geomechanics geological subdiscipline reservoir simulation coalbed methane...
Journal Articles
Journal: SPE Journal
SPE J. 29 (07): 3791–3800.
Paper Number: SPE-212975-PA
Published: 10 July 2024
... realization reynolds ensemble neural network artificial intelligence model calibration spe journal co 2 workflow geophysics-derived spatial data history-matched co 2 Data assimilation is also called history matching or inverse modeling in different communities. It has been widely applied...
Journal Articles
Journal: SPE Journal
SPE J. (2024)
Paper Number: SPE-221476-PA
Published: 09 July 2024
... technique is proposed in this study to optimize the trajectory of wells under geological uncertainty. The proposed model is a deep neural network with ConvLSTM layers to extract the most salient features from highly channelized and layered reservoirs efficiently. ConvLSTM layers are used because they can...
Includes: Supplementary Content
Journal Articles
Journal: SPE Journal
SPE J. (2024)
Paper Number: SPE-219767-PA
Published: 27 June 2024
... proxies into the multiobjective HM process in the literature. 1 12 2023 16 4 2024 5 4 2024 27 6 2024 Copyright © 2024 Society of Petroleum Engineers history matching geologist neural network reservoir simulation optimization problem geology proxy application pso...
Journal Articles
Journal: SPE Journal
SPE J. (2024)
Paper Number: SPE-219773-PA
Published: 13 June 2024
..., such as injection rate and pressure data, as inputs and multiphase production rates as outputs. We combine reduced physics models into a neural network architecture by utilizing two different approaches. In the first approach, the reduced physics model is used for preprocessing to obtain approximate solutions...
Journal Articles
Journal Articles
Journal: SPE Journal
SPE J. 29 (06): 2772–2792.
Paper Number: SPE-219731-PA
Published: 12 June 2024
... reservoir considering both hydrocarbon recovery and CO 2 storage efficacies. To make the predictive model more robust, two distinct proxy models—multilayered neural network (MLNN) models coupled with particle swarm optimization (PSO) and genetic algorithms (GAs)—were trained and optimized to forecast...
Includes: Supplementary Content
Journal Articles
Journal Articles
Journal Articles
Journal Articles
Journal: SPE Journal
SPE J. 29 (05): 2257–2274.
Paper Number: SPE-217977-PA
Published: 15 May 2024
... show that the effectiveness of the long short-term memory (LSTM) neural network method was found to be superior to support vector regression (SVR), backpropagation (BP) neural network, deep belief neural network (DBN), and convolutional neural network (CNN) methods. For data validity, the best input...
Includes: Supplementary Content
Journal Articles
Journal Articles
Journal: SPE Journal
SPE J. 29 (05): 2165–2180.
Paper Number: SPE-219452-PA
Published: 15 May 2024
.... 30 10 2023 11 1 2024 21 12 2023 5 2 2024 15 5 2024 Copyright © 2024 Society of Petroleum Engineers sedimentary rock clastic rock geologist rock type neural network deep learning geology artificial intelligence prediction scenario experiment denoising...
Includes: Supplementary Content
Journal Articles
Journal Articles
Journal: SPE Journal
SPE J. 29 (05): 2432–2444.
Paper Number: SPE-214832-PA
Published: 15 May 2024
...Miao Jin; Hamid Emami-Meybodi; Mohammad Ahmadi Summary We present artificial neural network (ANN) models for predicting the flowing bottomhole pressure (FBHP) of unconventional oil wells under gas lift operations. Well parameters, fluid properties, production/injection data, and bottomhole gauge...

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