Robust Uncertainty Quantifications of Fracture Geometries Through Automatic History Matching with Application in Real Shale Gas

Yuchen Xiao, Chuxi Liu, Wei Yu, Kamy Sepehrnoori, Corwin Zigler

Unconventional Resources Technology Conference (URTEC)

.... Efficient Field-Scale Simulation of Black Oil in a Naturally Fractured Reservoir Through Discrete Fracture Networks and Homogenized Media. SPE Res...

An Integrated Approach to Building a 3D Discrete Fracture Network Model of an Unconventional Naturally Fractured Reservoir, Kuwait

Mariela D. Pichardi Hernandez, Raphael Altman, Girija S. Padhy, Arup Sadhu, Pratik Sangani, Alaa Mohammad, Tahani Al Rashidi, Priyavrat Shukla

Unconventional Resources Technology Conference (URTEC)

... that drilling and completion operations could have on natural fracture networks. Some wells completed in what appears to be high intensity natural...

Ascertain the Presence of Open Fractures in Tight Carbonate Reservoirs with Integrated Solution

Kim Long Nguyen, Mahmoud F. Shehab El Dein, Nami Al-Mutairi, Rasha Al-Morakhi, Mohammed Dasma, Lulwa Al-Mijrin, Meshal Abdul Hameed Al-Wadi, Karim Ousididene, Ahmed Moustafa, Mohab El-Masry, Sachindra Sharma, Nawal Kerrouche, Ramdane Bouchou, Mohamed Said

Unconventional Resources Technology Conference (URTEC)

... carbonate reservoirs. A processing and advanced interpretation are carried out and integrated to have a comprehensive evaluation of fracture type, networks...

Machine Learning Identification of TOC–Rich Zones in the Eagle Ford Shale

Adewale Amosu, Mohamed Imsalem, Yuefeng Sun

GCAGS Transactions

.... H. Fard, H. Talebi, and Z. Shirband, 2011, Estimating total organic carbon content and source rock evaluation, applying ΔlogR and neural network...

Data Connectivity Inference and Physics-AI Models For Field Optimization

Hector Klie, Arturo Klie, Bichen Yang

Unconventional Resources Technology Conference (URTEC)

... (DCA) models, neural networks, ensemble methods). During the past few years, the latter is receiving more attention than the former, as they are faster...

Investigation of Lateral Fracture Complexity to Mitigate Frac Hits During Interwell Fracturing

Yanli Pei, Jiacheng Wang, Wei Yu, Kamy Sepehrnoori

Unconventional Resources Technology Conference (URTEC)

...-nearest Neighbors and Neural Networks as Proxy Model. Fuel 262, 116563. https://doi.org/10.1016/j.fuel.2019.116563. Wang, J., Lee, H. P., Li...

Developing a High-efficiency Method for Field-scale Simulation of a Tight and Naturally Fractured Reservoir in the Williston Basin

Xincheng Wan, Lu Jin, Todd Jiang, Nicholas Bosshart, James Sorensen, Chenyu Wu, Ahmed Merzoug

Unconventional Resources Technology Conference (URTEC)

... complex URTeC 3868150 11 fracture networks. Employing the EDGS® Suite, Figure 12 shows the simulation model of the studied reservoir with wells...

Carbonate rocks: Matrix permeability estimation

Alejandro Cardona, and J. Carlos Santamarina

AAPG Bulletin

..., Relationships between permeability, porosity and pore throat size in carbonate rocks using regression analysis and neural networks: Journal of Geophysics...

Controls on fracture network characteristics of the middle member of the Bakken Formation, Elm Coulee field, Williston Basin, United States

S. Khatri, and C. M. Burberry

AAPG Bulletin

..., multilayer neural networks as well as the genetic algorithm are combined to provide a robust and straightforward seismic inversion. The time slice from...

Porosity distribution prediction of untapped Gumai Formation by applying multi-attribute analysis: A case study in South Sumatra Basin

Mohammad Risyad, Muhammad Fahmi Yahya Faizan

Petroleum Exploration Society of Australia (PESA)

... logs. Multi-attribute analysis, neural properties, density, gamma rays, and porosity are utilized to discriminate sand, thin sand and shale. Neural...

AAPG Annual Convention Shreveport, May 17-20, 1998, Salt Lake City, Utah, - Abstracts, #90937 (1998).

Search and Discovery.com

Unlocking Lithium Potential from Oilfield Brines: A Deep Learning-Driven Resource Assessment

Rajkanwar Singh, Saaksshi Jilhewar, Audrey Der, Ryan Mercer, Vikram Jayaram

Unconventional Resources Technology Conference (URTEC)

... introduces a deep learning framework for predicting lithium concentrations in produced waters, leveraging advanced neural network architectures...

Interactive channel interpretation using deep learning

Hao Zhang, Peimin Zhu, Zhiying Liao, Zewei Li, Dianyong Ruan

International Meeting for Applied Geoscience and Energy (IMAGE)

..., it is difficult to extract channels completely. With the development of machine learning technology, convolutional neural network (CNN) is widely...

Buried-hill multiscale fracture prediction using wide-azimuth OBN seismic data in the South China Sea

Xumin Liu, Zhenbo Zhang, Leyi Xu, Renjie Chen, Donghui Bian

International Meeting for Applied Geoscience and Energy (IMAGE)

... prediction results ranging from post-stack seismic, VVAz (Velocity Versus Azimuth) and AVAz, the neural net fracture prediction is carried out...

Reservoir Characterization of the Gabus-1 Reservoir in North Belut Field: An Integration of Core, Well Logs and Seismic, Natuna Sea Basin, Indonesia

Yan Darmadi, Edo Hartadi, Bowo Pangarso, Irene Sihombing, Retno Wijayanti

Indonesian Petroleum Association

.... Analysis of Lambda-Rho seismic inversion data provided controls for the lateral extent of the Gabus 1 sand. Further investigation suggests that neural...

Technical Resource Potential Estimation Using Machine Learning and Optimization for the Delaware Basin

Hardikkumar Zalavadia, Yuxing Ben, Raquel Gordillo, Steven Lauver

Unconventional Resources Technology Conference (URTEC)

... then reviewed some of the recent works on using Machine Learning to predict production and EUR for new wells. Neural networks were used to predict two...

The Vienna Basin

Gerhard Arzmller, tpn Buchta, Eduard Ralbovsk, Godfrid Wessely

AAPG Special Volumes

... methods like 3-D seismic and areal petrophysical evaluation (based on neural networks). The study came up with a new, detailed sequence-stratigraphic...

Maximizing Efficiency of Deep-reinforcement Learning Agents in Autonomous Directional Drilling With Hyperparameter Optimization

Vivek Kesireddy, Georgy Kompantsev, Sheelabhadra Dey, Eduardo Gildin, Enrique Z. Losoya, Narendra Vishnumolakala

Unconventional Resources Technology Conference (URTEC)

...., & Abbeel, P. (2017). Domain randomization for transferring deep neural networks from simulation to the real world. Paper presented at the 2017 IEEE...

Trace-Element and Major-Element Stratigraphy in Quaternary Sediments from the Arctic Ocean and Implications for Glacial Termination

A. Aldahan , G. Possnert , R. Scherer , N. Shi , J. Backman , K. Bostrom

Journal of Sedimentary Research (SEPM)

...., 1998, A neural networks approach to predict sedimentation and climate changes in arctic sediment covering last 350 ka (abstract): 15th International...

Production Optimization Using Machine Learning in Bakken Shale

Guofan Luo, Yao Tian, Mariia Bychina, Christine Ehlig-Economides

Unconventional Resources Technology Conference (URTEC)

... built the neural network model to identify the relationship between the first-year oil production with the important features. We separated the data...

GEO 2018; - Abstracts, #90319 (2018).

Search and Discovery.com

Three-Dimensional Seismic Volume Visualization of Carbonate Reservoirs and Structures

Jose Luis Masaferro, Ruth Bourne, Jean Claude Jauffred

AAPG Special Volumes

... texture mapping using neural networks allows the grouping of regions of data into different classes based on a combination of 3-D attributes (Figure 4A...

Application of horizontal wells in three-dimensional shale reservoir modeling: A case study of Longmaxi–Wufeng shale in Fuling gas field, Sichuan Basin

Guochang Wang, Shengxiang Long, Yiwen Ju, Cheng Huang, and Yongmin Peng

AAPG Bulletin

... machine and neural networks in total organic carbon content prediction in organic shale with wire-line logs: Journal of Natural Gas Science...

Predicting Microseismicity from the Geomechanical Modeling of Multiple Hydraulic Fractures Interacting with Natural Fractures - Application to the Marcellus and Eagle Ford

Yamina E. Aimene, John A. Nairn, Adel Boudjema

Unconventional Resources Technology Conference (URTEC)

... regression and neural networks, if provided with the appropriate input, could capture the general physics but not the details. This appropriate input...

Artificial Intelligence for Production Optimization in Unconventional Reservoirs

Oscar Molina, Camilo Mejia, Jerry Webb, Rebecca Nye

Unconventional Resources Technology Conference (URTEC)

... production forecasts. Similarly, we observe that the introduction of artificial neural networks (ANN) as a means for ROP optimization, using offset wells...