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Keywords: deep learning
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Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 17–19, 2024
Paper Number: URTEC-4042557-MS
... especially challenging for operators to scale to many wells and in a fast cadence to accommodate the rapid speed in shale and tight asset development. In this paper, we present a workflow that combines probabilistic modeling and deep learning models trained on an ensemble of physics models to improve...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 17–19, 2024
Paper Number: URTEC-4043583-MS
... that shale well production does not adhere to the traditional decline curve models established by Arps (1945), which assume a relatively stable production decline after an initial peak. clastic rock rock type unconventional resource economics complex reservoir geologist deep learning...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 17–19, 2024
Paper Number: URTEC-4040968-MS
.... geology interpretation geologist complex reservoir neural network log analysis geological subdiscipline information availability well logging deep learning rock type machine learning reservoir geomechanics urtec workflow neutron porosity prediction abubakar mb000 subset ub000 tf400...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 17–19, 2024
Paper Number: URTEC-4043196-MS
... often struggle to capture the intricate relationships between various drilling parameters, leading to inaccurate predictions. complex reservoir neural network deep learning artificial intelligence machine learning urtec depth coiled-tubing drilling dynamic architecture transformer...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 17–19, 2024
Paper Number: URTEC-4044067-MS
... geology deep learning asset and portfolio management natural language permian basin application rock type artificial intelligence geological subdiscipline algorithm operator tca production forecasting urtec large language model information efficient field development decision driven...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 17–19, 2024
Paper Number: URTEC-4044012-MS
... production of each drilling unit as targets; and 6) evaluating optimization with real data and creating what-if forecasting scenarios. neural network geologist complex reservoir geology geometry urtec machine learning geological subdiscipline deep learning artificial intelligence dataset...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 17–19, 2024
Paper Number: URTEC-4044635-MS
... of these models through new workflows to accommodate the distinct characteristics of unconventional reservoirs. complex reservoir deep learning geology geologist artificial intelligence machine learning transformer unconventional reservoir forecasting time sery transformer architecture...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 17–19, 2024
Paper Number: URTEC-4054687-MS
... and pattern recognition. structural geology pvt measurement geologist neural network pressure transient testing asset and portfolio management complex reservoir reservoir simulation modeling & simulation equation of state drillstem/well testing deep learning drillstem testing risk...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 17–19, 2024
Paper Number: URTEC-4055265-MS
... deep learning rock type modeling & simulation machine learning realization misra URTeC: 4055265 Effects of Early-Time Production Data on Machine-Learning-Assisted Long-Term Production Forecasting Mohammad H. Elkady*1, Siddharth Misra1, Veena T. Kumar1, Uchenna Odi2, Andrew Silver2 1. Texas...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 17–19, 2024
Paper Number: URTEC-4046687-MS
... Abstract This research addresses Industry 4.0's predictive maintenance challenges by applying Deep Learning (DL) algorithms that include LSTM, RNN, GRU and ensemble methods like Random Forest, XGBoost and Gradient Boosted Tree to predict the Remaining Useful Life (RUL) of downhole drilling...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 17–19, 2024
Paper Number: URTEC-4053769-MS
... recovery. geologist geology sagd steam-assisted gravity drainage deep learning workflow reservoir geomechanics thermal method huff enhanced recovery information modeling & simulation geological subdiscipline engineering geomechanics recovery optimization workflow integrating...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 13–15, 2023
Paper Number: URTEC-3863926-MS
... the nonlinearity as well as the impact of manual operations. Production history was obtained from 3 wells of the Eagle Ford shale (Frio, La Salle, and Zavala County) where a single well data was split into a 75:25 ratio for training and testing. Deep learning (DL) algorithms, long short-term memory (LSTM) and Bi...

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