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1-20 of 57
Keywords: deep learning
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Proceedings Papers
Towards Universal Production Forecasting via Adversarial Transfer Learning and Transformer with Application in the Shengli Oilfield, China
Available to Purchase
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 17–19, 2024
Paper Number: URTEC-4032318-MS
... Abstract This study aims to leverage deep learning (DL) to forecast oil production dynamics, focusing on the transition from conventional to unconventional reservoirs in the Shengli Oilfield, China. Due to the discrepancy in geological characteristics and development methods, production data...
Proceedings Papers
Physics Informed Deep Learning Models for Improving Shale and Tight Forecast Scalability and Reliability
Available to PurchaseKainan Wang, Lichi Deng, Yuzhe Cai, Guido Di Federico, Keith Ramsaran, Mun Hong Hui, Hussein Alboudwarej, Christian Hager, Yuguang Chen, Xian-Huan Wen
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
Transforming Early-Stage Oil and Gas Production Forecasting with Generative AI
Available to Purchase
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
A Deep Learning Workflow for Integrated Geological, Petrophysical, and Geomechanical Interpretation
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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
Predicting Coiled-Tubing Drilling Dynamics Using Transformers
Available to Purchase
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
Efficient Field Development Decisions Driven by Artificial Intelligence: A Permian Basin Example
Available to PurchaseHui Zhou, Johan A. Daal, Tom Williford, Qin Lu, Michael Burkard, Lee McAuliffe, M. D. Rincones, Rafael Paz Lopez
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
An Innovative Approach to Capture Depletion Impact in Unconventional Reservoir Production Prediction Using Machine Learning and a Time-Dependent Depletion Function
Available to Purchase
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 17–19, 2024
Paper Number: URTEC-4044069-MS
... of deep-learning neural network models were trained on Bakken well data: 70% for training, 20% for validation, and 10% for holdout to predict monthly oil, water, and gas volumes. The best ten models were first selected for low validation errors, then advanced for reservoir-engineering benchmarking...
Proceedings Papers
Convolutional Neural Networks Forecasting for Unconventional Drilling Units for US Land
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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
Innovating Oil and Gas Forecasting: Developing a Trailblazing Generative AI Model
Available to Purchase
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
Evaluation of Empirical Correlations and Time Series Models for the Prediction and Forecast of Unconventional Wells Production in Wolfcamp A Formation
Available to PurchaseAimen Laalam, Houdaifa Khalifa, Habib Ouadi, Mouna Keltoum Benabid, Olusegun Stanley Tomomewo, Mouad Al Krmagi
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 17–19, 2024
Paper Number: URTEC-4043738-MS
... and, consequently, production behavior (Waite, 2021). sedimentary rock clastic rock unconventional resource economics fluid dynamics unconventional play deep learning reservoir simulation mudrock flow in porous media reservoir surveillance neural network rock type petroleum play type shale gas...
Proceedings Papers
Artificial Intelligence Integration for Optimal Reservoir Data Analysis and Pattern Recognition
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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
Statistical Analysis of Estimated Ultimate Recovery: Comparing Machine Learning and Traditional DCA Methods in Eagle Ford and Bakken
Available to Purchase
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 17–19, 2024
Paper Number: URTEC-4049496-MS
... eagle ford deep learning artificial intelligence machine learning ultimate recovery information normalized root mean squared error lstm nrmse unconventional reservoir eur prediction production control prediction accuracy distribution URTeC: 4049496 Statistical Analysis of Estimated...
Proceedings Papers
Cluster Efficiency-Based Stimulation: Real-Time Quantified Screenout Monitoring and Diversion Evaluation Through a Data Model Approach
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Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 17–19, 2024
Paper Number: URTEC-4056167-MS
... domain. This paper applies high-fidelity measurements and deep-learning techniques to address some of the key challenges faced by operators today in the fracturing domain. Fiber optic data have been used to evaluate the cluster efficiency. A novel parameter, referred to as nondimensional slurry...
Proceedings Papers
Effects of Early-Time Production Data on Machine-Learning-Assisted Long-Term Production Forecasting
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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
A Deep Learning Approach to Predicting Remaining Useful Life for Downhole Drilling Sensors Using Synthetic Data Generation
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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
A Hybrid Machine Learning Workflow for CO2 Huff-n-Puff
Available to PurchaseGurpreet Singh, Anuj Gupta, Uchenna Odi, Davud Davudov, Birol Dindoruk, Ashwin Venkatraman, Rabah Mesdour
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
Predicting Hydrocarbon Production Behavior in Heterogeneous Reservoir Utilizing Deep Learning Models
Available to PurchaseFatick Nath, Sarker Asish, Happy R. Debi, Mohammed Omar S. Chowdhury, Zackary J. Zamora, Sergio Muñoz
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...
Proceedings Papers
Optimization Method for Fracture-Network Design Under Transient and Pseudosteady Condition Using UFD Technique and Deep Learning Approach
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Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 13–15, 2023
Paper Number: URTEC-3853784-MS
...) condition is presented. A nested optimization based on the PI-performance model is provided. In the-inner shell, the optimum fracture parameters considering time dependency are achieved by searching the maximum PI based on unified fracture design (UFD). Coupling with deep learning and UFD practices...
Proceedings Papers
Predicting Facies, Rock, and Geomechanical Properties Using Convolutional Neural Networks: A Case Study from an Unconventional Shale Reservoir
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Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 13–15, 2023
Paper Number: URTEC-3862247-MS
.... Introduction The objectives of this study is to accurately predict unconventional reservoir properties from seismic and well data using convolutional neural networks (CNNs). There is a great interest in the use of Deep Learning (Goodfellow et al.,2016) to automate complex workflows and reduce project times...
Proceedings Papers
Mechanistic Understanding and Data-Driven Prediction of Liquid Loading in Long-Lateral Oil Wells in Unconventional Reservoirs
Available to PurchaseXiao Zhang, Amit Kumar, Tyler Nahhas, Willy Manfoumbi, Christopher Frazier, Gunta Chomchalerm, Yang Chen, Isara Tanwattana, Tamas Toth, Huafei Sun, Aaron Shinn, Peng Xu
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 13–15, 2023
Paper Number: URTEC-3860898-MS
... neural network europe government united states government production monitoring deep learning drillstem testing artificial intelligence gas well deliquification reservoir surveillance upstream oil & gas complex reservoir liquid loading production control artificial lift system machine...
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