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Keywords: machine learning
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Proceedings Papers
A Machine-Learning-Based Gas Lift Optimization Workflow for Unconventional Fields
Available to PurchaseSha (Sasha) Miao, Alexandra Vendetti, Logan Smart, Gunta Chomchalerm, Yang Chen, Christopher Frazier, Dustin Haralson, Jeremy Sorenson, Xiao Ma, Huafei Sun, Aaron Shinn, Haining Zheng, Xiao-Hui Wu, Peng Xu
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 17–19, 2024
Paper Number: URTEC-4033553-MS
... Abstract In this paper, we present an automated data-driven workflow using Machine Learning (ML) for gas lift optimization in unconventional fields. This workflow integrates a ML model that accurately forecasts the Gas Lift Performance Curve, and a Bayesian Optimization Framework to solve...
Proceedings Papers
Practical Models for Computing CO 2 Solubility in Brines with Complex Ions for Carbon Geo-Sequestration Applications
Available to Purchase
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 17–19, 2024
Paper Number: URTEC-4033482-MS
... 2 in brine formation, the mean squared error is 5 × 10 −5 and the R-squared is 0.96. in general, the Gradient Boosting Model ranks as the most accurate machine learning algorithm among the algorithms studied in this study. Introduction Reactive gases, like ozone and nitrous substances...
Proceedings Papers
Resolving Multiphase Fractions in Horizontal Wells Using Speed of Sound Measured with Distributed Acoustic Sensors and a PVT Database
Available to Purchase
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 17–19, 2024
Paper Number: URTEC-4017241-MS
... recovery multiphase flow sos log analysis complex reservoir flow in porous media reservoir surveillance production monitoring pvt measurement equation of state production logging well logging production control machine learning fraction frequency accuracy resolution wavenumber...
Proceedings Papers
A Fast and Robust Method for Estimating Inter-Well Interference in Shale and Tight Reservoirs
Available to PurchaseEsmail Eltahan, Ali Moinfar, Zhenzhen Wang, Matthieu Rousset, Haishan Luo, Pierre Muron, Xianhuan Wen
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 17–19, 2024
Paper Number: URTEC-4031992-MS
... testing modeling & simulation drillstem/well testing geology rock type reservoir geomechanics mudstone scenario configuration urtec unconventional resource technology conference application inter-well interference unconventional reservoir geometry machine learning dtof seg...
Proceedings Papers
Smart Control: Advancing the Optimization and Control of Artificial Lift Systems
Available to Purchase
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 17–19, 2024
Paper Number: URTEC-4031362-MS
... 1. Discovery: Review current control and surveillance technologies to identify opportunities for AI-based enhancement. 2. Assessment: Gather key data for model training and evaluate integration compatibility with existing systems. 3. Development: Develop and calibrate AI / machine learning (ML...
Proceedings Papers
Field Implementation and Surveillance of Gas Injection Enhanced Oil Recovery in the Bakken
Available to PurchaseJin Zhao, Lu Jin, Xue Yu, Nicholas A. Azzolina, Xincheng Wan, Steven A. Smith, Nicholas W. Bosshart, James A. Sorensen
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 17–19, 2024
Paper Number: URTEC-4031314-MS
... testing, were collected from these tests to generate time-series plots or analytics that can inform operators of downhole conditions. Predictive modeling based on reservoir simulation and machine learning was then conducted to rapidly forecast future performance for operators to compare against observed...
Proceedings Papers
Machine Learning-Based Sweet Spot Prediction Method for Canada Tight Sandstone Gas Reservoir
Available to Purchase
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 17–19, 2024
Paper Number: URTEC-4025206-MS
... Abstract A prediction method for the EUR (Estimated Ultimate Recovery) of tight sandstone gas reservoirs based on a composite machine learning approach was proposed and applied in the M tight sandstone reservoir in Canada. This method extrapolated geological parameters such as porosity...
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
... geology complex reservoir reservoir surveillance forecasting urtec unconventional resource technology conference adversarial transfer learning deep learning production forecasting production monitoring unconventional reservoir technology conference discrepancy machine learning conference...
Proceedings Papers
Understanding the Impacts and Fate of Lost Drilling Fluid by Oil Geochemistry
Available to Purchase
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 17–19, 2024
Paper Number: URTEC-4027808-MS
... drilling fluid formulation drilling fluid property geochemistry geological subdiscipline artificial intelligence well control geochemical characterization drilling fluids and materials drilling fluid selection and formulation machine learning geologist complex reservoir drilling fluid...
Proceedings Papers
A Comprehensive Approach to Water Volume Estimation in Unconventional Shale Reservoirs Through Physics-Informed Statistical Modeling
Available to Purchase
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 17–19, 2024
Paper Number: URTEC-4026260-MS
... gas oil shale rock type complex reservoir shale oil geology mudrock log analysis water-filled porosity conductivity physics-informed statistical modeling mudstone saturation comprehensive approach accuracy well logging artificial intelligence machine learning urtec unconventional...
Proceedings Papers
Mapping Oil-Prone Facies in 3D for Field Development and Optimizing Production: A Midland Basin Case Study
Available to Purchase
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 17–19, 2024
Paper Number: URTEC-4020437-MS
... in the vertical wells and its implications for future use in deciding frac stage placement. geologist mudrock sedimentary rock complex reservoir artificial intelligence machine learning urtec field development structural geology petroleum geology clastic rock reservoir characterization rock...
Proceedings Papers
A Machine-Learning-Based Workflow for Drilling Risk Prediction of Wellbore Instability and Trajectory Optimization in Ultra-Deep Formation
Available to PurchaseHanqing Wang, Ruxin Zhang, Ziyan Deng, Jin Meng, Han Wang, Yujie Zhou, Fan Yang, Ji Chang, Yitian Xiao, Huiwen Pang
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 17–19, 2024
Paper Number: URTEC-4034008-MS
... to uncertainties when utilizing seismic and adjacent well data to anticipate drilling risks and optimize well trajectories. This study aims to mitigate these uncertainties by calibrating drilling risks using borehole-side seismic data. Additionally, a machine learning model is used to predict the 3D spatial...
Proceedings Papers
Application of a Sparse Hybrid Data-Driven and Physics Model in Unconventional Reservoirs for Production Forecasting
Available to Purchase
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 17–19, 2024
Paper Number: URTEC-4033921-MS
... for multiphase flow—Hybrid Sparse Nonlinear Regression (Hybrid SNR). This innovative model integrates recent advancements in machine learning and sparsity techniques. Unlike conventional methods, Hybrid SNR is suited to discern the intricate governing equations that dictate flow of phases in unconventional...
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
... the scalability and reliability for shale and tight reservoir forecasting. We construct generic reservoir models that can capture key first principles of unconventional well production mechanisms. PVT and pressure machine learning models are developed and incorporated into the reservoir models so...
Proceedings Papers
Forecasting Production Loss for Delayed Secondary Bench Development in the Midland Basin
Available to Purchase
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 17–19, 2024
Paper Number: URTEC-4037004-MS
... Abstract While it is generally understood that existing well depletion affects future well developments negatively, estimating the production impacts remains a challenge, particularly when newer developments occur in a different formation. This study seeks to use machine learning (ML) models...
Proceedings Papers
Estimation of Stimulated Reservoir Region for Hydraulic Fracturing in Shale Gas Well Based on Ensemble Learning Algorithm
Available to PurchaseYang Luo, Bo Kang, Yan Feng, Hehua Wang, Zhongrong Mi, Yi Cheng, Yong Xiao, Xing Zhao, Jianchun Guo, Cong Lu
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 17–19, 2024
Paper Number: URTEC-4018039-MS
... of microseismic data, there are multiple solutions for the inversion of fracture network, making it difficult to obtain the accurate distribution of hydraulic fractures. With the rapid advancement of artificial intelligence, machine learning methods have been applied in the oil and gas development, including...
Proceedings Papers
Integrating Experiments and Well Logs to Predict Caney Shale Static Mechanical Properties During Production with Supervised Machine Learning
Available to Purchase
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 17–19, 2024
Paper Number: URTEC-4038060-MS
... caney shale correlation complex reservoir shale gas log analysis machine learning static young oklahoma structural geology mudrock well logging rhob ductile phin dyn av mudstone young poisson xgboost caney shale static mechanical property random forest URTeC: 4038060 Integrating...
Proceedings Papers
Production Optimization of CO 2 Huff-n-Puff Process in Unconventional Oil Reservoirs with Effects of Gas Relative Permeability Hysteresis, Geomechanics, and Capillary Pressure
Available to Purchase
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 17–19, 2024
Paper Number: URTEC-4037410-MS
... reservoir geomechanics thermal method hysteresis proxy complex reservoir flow in porous media machine learning co 2 constraint geologist sagd enhanced recovery economic geology injection conference simulation coefficient optimization problem artificial intelligence geological...
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
... sedimentary rock geology natural language generative ai history gas production forecasting modeling & simulation energy economics time sery transformer application houston production forecasting machine learning urtec unconventional resource technology conference permian basin artificial...
Proceedings Papers
Physics-Informed Machine Learning Approach for Closed-Loop Reservoir Management Using RGNet
Available to Purchase
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 17–19, 2024
Paper Number: URTEC-4036015-MS
... connectivity analysis, resource volume estimation, forecasting, and flood optimization. However, the model's accuracy and predictability can be further enhanced with data-driven approaches. Here, our aim is to improve the quality of the RGNet model using machine learning (ML). We propose to use an ML-enhanced...
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