Abstract: Seismic Attributes „ A Promising Aid for Hydrocarbon Prediction in Deep Water of the Ceará Basin, Brazilian Equatorial Margin;

Karen Leopoldino Oliveira, Karelia La Marca, Heather Bedle

Search and Discovery.com

... and an unsupervised machine-learning technique called self-organizing maps were applied focusing on this Cenomanian-Turonian interval. These techniques...

Integrated Multi-Scale Reservoir Characterization: Wolfcamp Formation … Midland Basin

Joel Walls, Mark Ver Hoeve, Anyela Morcote, Michael Foster

Unconventional Resources Technology Conference (URTEC)

.... Then based on a multidimensional rock typing classification using machine learning (ML) techniques, plug locations were selected. SEM analysis was performed...

Abstract: Seismic Analysis with Wavelets and Deep Learning;

Samvith Rao, Akhilesh Mishra

Search and Discovery.com

... results have demonstrated that wavelets combined with deep learning can distinguish among different facies helping the interpreter to process new...

Integrated reservoir characterization of low resistivity thin beds using three-dimensional modeling for natural gas exploration

Lim Yen Jun, Lo Shyh Zung

Geological Society of Malaysia (GSM)

.... Facies classification using machine learning. The 98 Leading Edge, (October), 906–909. Herron, D.A., 2011. First Steps in Seismic Interpretation...

Locate the Remaining Oil (LTRO) and Predictive Analytics: Application for Development Decisions on Marmul GNR Field, The Sultanate of Oman, #42191 (2018).

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

Search and Discovery.com

... analytics history machine learning Machine learning full field forecast Development scenarios forecast LOCAT -THE-R MAINI G-OL(LT O)ANDPREDICTIVEANALV...

Abstract: Geological Modelling and Reservoir Simulation of a Petroleum Field in Malaysia

D. N. H. Lee, D. Baxendale, Edmund Huang

Geological Society of Malaysia (GSM)

... on all the available core data. Having determined the core lithofacies classification, the next step was to identify the different facies in the non-cored...

Enhanced Reservoir Characterization for Optimizing Completion Decisions in the Permian Basin Using a Novel Field-Scale Workflow Including Wells with Missing Data

Artur Posenato Garcia, Laura M Hernandez, Archana Jagadisan, Zoya Heidari, Brian Casey, Rick Williams

Unconventional Resources Technology Conference (URTEC)

... geostatistical and machine learning methods to reliably reconstruct missing PEF logs with a confidence interval through a rock-type-based approach which...

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...

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...

Using Production Quality Flow AI Based Facies With Integrated Multidisciplinary Workflow For Best Potential Targets Evaluation in Delaware Basin

Mauricio Vinassa, Dave Handwerger, Tom Tracey, Javed Iqbal, Hyder Jatoi

Unconventional Resources Technology Conference (URTEC)

... for the Permian Basin LA URTeC 3967521 2 A series of property-specific machine learning based facies models were created using a set of training wells...

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...

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...

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...

ABSTRACT: Facies Modeling of Tight Gas Reservoir Using Neural Network: Case Study of NIKANASSIN Formation in Canadian Foothills; #90108 (2010)

Jack H. Deng

Search and Discovery.com

...ABSTRACT: Facies Modeling of Tight Gas Reservoir Using Neural Network: Case Study of NIKANASSIN Formation in Canadian Foothills; #90108 (2010) Jack H...

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...

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...

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...

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

Search and Discovery.com

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...

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...

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...

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...