Insights into fluid movement and production discrepancies from PCA clustering on 4D seismic data

Evan Jowers, Heather Bedle

International Meeting for Applied Geoscience and Energy (IMAGE)

... facies discrepancies, fluid movement, and possible reservoir baffles. With the inclusion of production data, clusters produced from machine learning...

Incorporating Artificial Intelligence into Traditional Exploration Workflows in the Cooper-Eromanga Basin, South Australia

H. M. Garcia, W. G. "Woody" Leel Jr., M. Riehle, P. Szafian

International Meeting for Applied Geoscience and Energy (IMAGE)

.... For the classification, we used the color blend from the frequency decomposition calculated using the Far volume. The tool uses several machine learning...

Integrated carbonate reservoir types modeling based on the PRT deep learning and multi-parameters seismic inversion and its application

Chen Xin, Song Jiawen, Liu Qing, Sun Qian, Zhao Min, Qi Qunli, Weixiang Zhong, Dengyi Xiao, Tang Zichang, Fuli An, Wang Bo, Fan Hanzhou, Li Xiaoliang, Huang Kongzhi, Liu Qiang

International Meeting for Applied Geoscience and Energy (IMAGE)

... on the deep learning and seismic inversion was proposed. By integrated lithofacies classification, it can enhance the accuracy and reliability...

Enhancing Karstified Carbonate Characterization Through Focused Seismic Reprocessing and Machine Learning Utilization in Ubadari Field

M.R Husni Sahidu, Ilham Panggeleng, Sarah Putri, Scott Miller, Xiaobo Li

Indonesian Petroleum Association

... mapping of top Faumai Carbonates c. Fault interpretation supported by edge attributes and Machine Learning (ML) toolkit d. Using bandlimited impedance...

AAPG International Conference and Exhibition; - Abstracts, #91209 (2025).

Search and Discovery.com

Deep adversarial multiview clustering network for unsupervised seismic facies analysis

Hanpeng Cai, Xiuyi Zou, Yuting Zhao, Sheng Zhang, Tengyu Wang

International Meeting for Applied Geoscience and Energy (IMAGE)

... and Development, Tarim Oilfield Company, PetroChina. Summary Unsupervised seismic facies analysis using pattern recognition algorithms provides an effective way...

Comparison of Clustering Techniques to Define Chemofacies in Mississippian Rocks in The STACK Play, Oklahoma; #42523 (2020)

David Duarte, Rafael Pires de Lima, Roger Slatt, Kurt Marfurt

Search and Discovery.com

... 9890 9890.00 honors the geology embedded in the lithofacies. Figure 1. Location, facies classification (legend in Figure 2), and gamma ray response...

Permeability Log Calculation by Integrating Lithology, Well Logs and DST Data … An Application to Sandstone Reservoir in Abadi Field Eastern Indonesia

Takahiro Zushi, Masato Okuno, Koichi Kihara, Tatsuya Konishi, Toru Ito

Indonesian Petroleum Association

... into a supervised learning method. The authors previously calculated permeability log curves for a sandstone reservoir of Abadi field by using parametric...

Automated Well Top Picking and Reservoir Property Analysis of the Belly River Formation of the Western Canada Sedimentary Basin

Baosen Zhang, Tianrui Ye, Yitian Xiao, Dongmei Li, Guoping Wang, Cong Su, Tongyun Yao

Unconventional Resources Technology Conference (URTEC)

... which the water saturation was corrected using the machine learning approach listed in the right lower corner. Using the cut offs of the reservoir...

OpenFWI 2.0: Benchmark Datasets for Elastic Full-waveform Inversion

Shihang Feng, Hanchen Wang, Chengyuan Deng, Yinan Feng, Min Zhu, Peng Jin, Yinpeng Chen, Youzuo Lin

International Meeting for Applied Geoscience and Energy (IMAGE)

... acoustic singleparameter counterpart. The recent advancements in machine learning have spurred researchers to investigate data-driven approaches...

A Comparison of Popular Neural Network Facies Classification Schemes*

Christopher P. Ross, David M. Cole

GCAGS Transactions

...) There are two learning networks commonly used for seismic facies classification: unsupervised and supervised neural networks. While unsupervised...

LTRO Workflow for Fast Turnaround Field-Optimisation Studies and Efficient Development Decisions, #42190 (2018).

Cristian Masini, Sergey Ryzhov, Dmitry Kuzmichev, Rina Bouy, Saeed Majidaie, Denis Malakhov

Search and Discovery.com

... MACHINE LEARNING QUANTIFICATION FOR DEVELOPMENT SCENARIOS (1000+ well in total) MARMUL GNR • Accelerated Mature Field Further Development assessment...

Joint Identification of Lithology and Lithofacies in Core Images Based on Deep Learning

Han Wang, Feifei Gou, Hanqing Wang, Shengjuan Cai

Unconventional Resources Technology Conference (URTEC)

... the accuracy and efficiency of lithology/facies classification in heterogeneous tight reservoirs; and (3) bridging the gap between advanced AI...

Enhancing seismic delineation of obscured geologic architectural elements in a deepwater channel complex through multi-attribute analysis: A study from the Taranaki Basin

April Moreno-Ward, Heather Bedle, Alexandro Vera-Arroyo

International Meeting for Applied Geoscience and Energy (IMAGE)

... and Exposition on Petroleum Geophysics, Hyderabad, India. Van der Maaten, L., and G. Hinton, 2008, Visualizing data using t-SNE: Journal of Machine Learning...

Machine-Learning-Assisted Induced Seismicity Characterization and Forecasting of the Ellenburger Formation in Northern Midland Basin

Niven Shumaker, Kaustubh Shrivastava, Mohamed Afia

Unconventional Resources Technology Conference (URTEC)

...Machine-Learning-Assisted Induced Seismicity Characterization and Forecasting of the Ellenburger Formation in Northern Midland Basin Niven Shumaker...

Petroleum Systems Through Economic Assessment of the Permian Basin, Texas Aided By a Cognitive System for Geosciences

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Table of Contents: GEOGULF TRANSACTIONS 70th Annual GCAGS Convention and 67th Annual GCSSEPM Meeting

James J. Willis, Norman C. Rosen, Jill C. Willis, Kate Kipper

GCAGS Transactions

... Machine Learning Identification of TOC-Rich Zones in the Eagle Ford Shale 3 Adewale Amosu, Mohamed Imsalem, and Yuefeng Sun Advanced Facies...

Depositional-process controls on chemofacies in mixed-lithology submarine lobe deposits: a high-resolution core study from the Permian Wolfcamp XY Formation, Delaware Basin, Texas, U.S.A.

Shaskia Herida Putri, Zane Jobe, Jesse Melick, Lesli Wood, Marsha French

Journal of Sedimentary Research (SEPM)

... geomechanical and well-log data from the Wolfcamp XY interval, this study demonstrates that chemofacies derived using unsupervised machine learning...

Lithofacies identification in cores using deep learning segmentation and the role of geoscientists: Turbidite deposits (Gulf of Mexico and North Sea)

Oriol Falivene, Neal C. Auchter, Rafael Pires de Lima, Luuk Kleipool, John G. Solum, Pedram Zarian, Rachel W. Clark, and Irene Espejo

AAPG Bulletin

... Using Deep Learning and Geosciences Applications Machine learning, and more specifically deep-learning convolutional neural networks (CNNs) have emerged...

Logging while drilling characterization of North Slope highly laminated Brookian formation

Tunde Akindipe, Patrick Perfetta, Chandramani Shrivastava, John Seitz, Vi Tuong, Arindam Bhattacharya, Vincent Osara

International Meeting for Applied Geoscience and Energy (IMAGE)

... downhole embedded machine learning utility to automatically detect drilling related features (breakout and induced fractures, Figure 2). Figure-2...

Optimizing Field Development Across Northern Delaware Basin for the Wolfcamp C

M. D. Rincones, I. Perez, J. Dark, A. Dutta, A. Wilkinson, Y. Wang, S. Bey, J. Hanzel, B. Biurchieva, P. Hoang

Unconventional Resources Technology Conference (URTEC)

... rock and fluid properties using “binary” cut-offs or risk bins. The Integrated Machine Learning (ML) Approach uses supervised ML algorithms...

Identification and distribution of hydraulic flow units of heterogeneous reservoir in Obaiyed gas field, Western Desert, Egypt: A case study

Mohamed A. Kassab, Ahmed Elgibaly, Ali Abbas, and Ibrahim Mabrouk

AAPG Bulletin

..., or transpose. The resulted PCs were used as the input for the model learning with using the hierarchical clustering (Ward method) for classification by using...

Deep convolutional neural networks for generating grain-size logs from core photographs

Thomas T. Tran, Tobias H. D. Payenberg, Feng X. Jian, Scott Cole, and Ishtar Barranco

AAPG Bulletin

... on all core (Switzer, 2013). As a quick and inexpensive alternative, this paper describes the process of using machine learning (ML) to generate grain...

A Coupled Laboratory Measurement - Machine Learning Workflow to Predict Elastic Anisotropy by Lithotype in Shale

Abhijit Mitra, James Kessler, Sudarshan Govindarajan, Deepak Gokaraju, Akshay Thombare, Andreina Guedez, Munir Aldin

Unconventional Resources Technology Conference (URTEC)

... using machine learning techniques like principal component and clustering algorithms. We then apply the predictive models to estimate anisotropy for each...

Applications of Artificial Intelligence in Log Analysis: Chapter 7

John H. Doveton

AAPG Special Volumes

.... The number chosen was arbitrary, but other than this constraint, the network was free to locate facies associations from the input logs. The learning...