Interpolated fast and computational-efficient multidimensional singular spectrum analysis (I-FMSSA) for compressive simultaneous-source data processing

Rongzhi Lin, Yi Guo, Fernanda Carozzi, Mauricio D. Sacchi

International Meeting for Applied Geoscience and Energy (IMAGE)

...-based methods. The filteringbased methods treat deblending as a noise filtering problem that operates on a particular domain, i.e., common receiver...

Machine learning and seismic attributes for prospect identification and risking: An example from offshore Australia

Mohammed Farfour, Douglas Foster

International Meeting for Applied Geoscience and Energy (IMAGE)

... and convert them to Gas chimney probability cube, and to Gamma Ray cube. Next, pre-trained Convolutional Neural Network (CNN) is trained using...

Deep Learning Models for Methane Emissions Identification and Quantification

Ismot Jahan, Mohamed Mehana, Bulbul Ahmmed, Javier E. Santos, Dan O’Malley, Hari Viswanathan

Unconventional Resources Technology Conference (URTEC)

... to prepare the data for the machine learning model. In this section, we will outline the preprocessing and Convolutional Neural Network (CNN) model...

Diagenesis and pore pressure induced dim spots „ Advances on AVO analysis of high-impedance reservoirs

Antonio Pessoa, Mark Chapman, Giorgos Papageorgiou

International Meeting for Applied Geoscience and Energy (IMAGE)

... stress scenario. Pressure-dependent AVO Analysis Seismic and Well Data Interpretation To conduct this AVO analysis, we assume a convolutional model...

Estimating CO2 saturation and porosity using the double difference approach based invertible neural network

Arnab Dhara, Mrinal K. Sen, Sohini Dasgupta

International Meeting for Applied Geoscience and Energy (IMAGE)

... posterior pdfs of model parameters to those obtained using Markov Chain Monte Carlo methods at significantly less computational time. We use two...

Counterfactual uncertainty for high dimensional tabular dataset

Prithwijit Chowdhury, Ahmad Mustafa, Mohit Prabhushankar, Ghassan AlRegib

International Meeting for Applied Geoscience and Energy (IMAGE)

... reveals valuable insights into model responses, enhancing decision-making, fairness analysis, and understanding of influencing factors. Our paper...

Application of Deep Learning for Methane Emissions Quantification and Uncertainty Reduction from Spectrometer Images

Ismot Jahan, Mohamed Mehana, Hari Viswanathan

Unconventional Resources Technology Conference (URTEC)

... oil and gas fields in the fields of Texas, California and New Mexico. Methods: We trained a convolutional neural network (CNN) using Large Eddy...

Application of Artificial Intelligence Tools for Fault Imaging in an Unconventional Reservoir: A Case Study from the Permian Basin

H. Garcia, L. Plant

Unconventional Resources Technology Conference (URTEC)

... and applied several of the more established techniques: data conditioning and frequency decomposition to push the resolution of the seismic data. Noise...

Application of Artificial Intelligence for Depositional Facies Recognition - Permian Basin

Randall Miller, Skip Rhodes, Deepak Khosla, Fernando Nino

Unconventional Resources Technology Conference (URTEC)

... in the Permian Basin. Training sets of core facies were selected by a sedimentologist. A model was built using a convolutional neural network...

Accelerate Well Correlation with Deep Learning; #42429 (2019)

Bo Zhang, Yuming Liu, Xinmao Zhou, Zhaohui Xu

Search and Discovery.com

... patterns (such as upward fining and coarsening) in neighboring wells and links them using a conscious or subconscious stratigraphic sequence model...

Interactive 3D fault prediction using a weighted 2D-CNN and multidirectional 3D-CNN

Jesse Lomask, Samuel Chambers

International Meeting for Applied Geoscience and Energy (IMAGE)

... using a weighted 2D-CNN and multi-directional 3D-CNN Jesse Lomask* and Samuel Chambers, S&P Global Summary We present an interactive 2D Convolutional...

Abstract: Predicting the Distribution of Subsurface Sedimentary Facies Using Deep Convolutional Progressive Generative Adversarial Network (Progressive GAN);

Suihong Song, Tapan Mukerji, Jiagen Hou

Search and Discovery.com

...Abstract: Predicting the Distribution of Subsurface Sedimentary Facies Using Deep Convolutional Progressive Generative Adversarial Network...

Deep learning-based joint inversion of time-lapse surface gravity and seismic data for monitoring of 3D CO2 plumes

Adrian Celaya, Mauricio Araya-Polo

International Meeting for Applied Geoscience and Energy (IMAGE)

... that measures the difference between the forward response of a given subsurface model and the observed data when the subsurface is directly stimulated...

An Introduction to Deep Learning: Part II

Lasse Amundsen, Hongbo Zhou, Martin Landrø

GEO ExPro Magazine

... often the model fails to predict the correct answer in their top five guesses (the top-5 error rate), in descending order of confidence. ILSVRC 2012...

Abstracts: The Reflectivity Response of Multiple Fractures and its Implications for Azimuthal AVO Inversion; #90173 (2015)

Olivia Collet, Benjamin Roure, Jon Downton

Search and Discovery.com

... studying various rock physics models in order to model the impact of multiple fractures on the elastic parameters of an isotropic medium. Then, we...

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 lithology identification model. Two lithofacies recognition models are trained for different lithology types. For a core image, the lithology is first...

Boulder prediction for offshore windfarm site evaluation using an interactive 2D CNN and a unique weighting scheme on unmigrated seismic

Samuel Chambers, Jesse Lomask

International Meeting for Applied Geoscience and Energy (IMAGE)

.... This gives the model a basic understanding of what to look for, and how to create the segmented output. The basic convolutional synthetic data was created...

2023 Middle East Oil, Gas and Geosciences Show ; - Abstracts, #91204 (2023).

Search and Discovery.com

Probabilistic seismic interpolation with the implicit prior of a deep denoiser

Matteo Ravasi

International Meeting for Applied Geoscience and Energy (IMAGE)

... velocity model that mimics the Volve field (see Ravasi et al. (2022) for more details on the data creation process). Second, we consider the Volve field...

Fracture Diagnostics in Naturally Fractured Formations: An Efficient Geomechanical Microseismic Inversion Model

Meng Cao, Mukul M. Sharma

Unconventional Resources Technology Conference (URTEC)

...Fracture Diagnostics in Naturally Fractured Formations: An Efficient Geomechanical Microseismic Inversion Model Meng Cao, Mukul M. Sharma URTeC...

Deep learning-based raster digitization engine

Atul Laxman Katole, Purnaprajna Mangsuli, Omkar Gune, Mohd Saood Shakeel, Abhiman Neelakanteswara, Aria Abubakar

International Meeting for Applied Geoscience and Energy (IMAGE)

... model encountered by the scanned raster images during the digitization process. A semantic segmentation model based on cGAN (Isola et al., 2017...

2019 AAPG Annual Convention and Exhibition

Search and Discovery.com

Evaluation of Empirical Correlations and Time Series Models for the Prediction and Forecast of Unconventional Wells Production in Wolfcamp A Formation

Aimen Laalam, Houdaifa Khalifa, Habib Ouadi, Mouna Keltoum Benabid, Olusegun Stanley Tomomewo, Mouad Al Krmagi

Unconventional Resources Technology Conference (URTEC)

.... Li et al. (2022) developed a hybrid production prediction model combining convolutional neural networks (CNN) and long short-term memory (LSTM...

Reservoir pressure monitoring via surface deformation inversion integrating numerical modelling with evolutionary optimisation

Reza Abdollahi, Abbas Movassagh, Dane Kasperczyk, Manouchehr Haghighi

Australian Energy Producers Journal

... Pressure Based on Surface Displacement Using Image-To-Image Convolutional Neural Network Model. Frontiers in Earth Science 9, 712681. doi...

Multi-Modal Neural Network for Porosity and Permeability Estimation in Tight Gas Reservoirs: A Case Study in the Ordos Basin, China

Shengjuan Cai, Yitian Xiao, Han Wang, Feifei Gou, Hanqing Wang, Yujie Zhou, Tianrui Ye

Unconventional Resources Technology Conference (URTEC)

... capture vertical and lateral variations across the reservoir. The network is designed to handle these multimodal inputs, with convolutional layers...