... 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...

... 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...

... 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...

.... 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...

... 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...

... 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...

... 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...

... 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...

... 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...

... 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...

... 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...

... 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...

... 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...

... 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...

... 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...

... 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...

... 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...

...) 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...

.... 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...

... 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...