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Keywords: classification
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Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 17–19, 2024
Paper Number: URTEC-4044665-MS
... engineering company rrc data-driven risking geologist artificial intelligence dataset information permian basin likelihood optimal threshold classification classifier exxonmobil upstream company URTeC: 4044665 Data-Driven Risking for Induced Seismicity Mariana Rodriguez-Buno*1, Yang Chen1...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 17–19, 2024
Paper Number: URTEC-4020437-MS
... saturation facies optimizing production midland basin case study fasken oil geologist mudrock facies geological subdiscipline toc ikon science inc workflow economic geology mapping oil-prone facies classification wolfcamp phit porosity URTeC: 4020437 Mapping Oil-Prone Facies in 3D...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 13–15, 2023
Paper Number: URTEC-3863378-MS
... states government accuracy artificial intelligence china information real-time warning method classification hydraulic fracturing technology conference unconventional resource technology conference southwest china multistage horizontal well fracturing directional drilling shale gas presented...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 13–15, 2023
Paper Number: URTEC-3865660-MS
... and characterization to fill the gap. This study covers a total of 4 classes (quartz, feldspar, calcite, and matrix) of 993 representative images in a clastic sedimentary rock, Mancos shale. The proposed architecture for automated classification is divided into the following stages: (1) input microscopic thin-section...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 13–15, 2023
Paper Number: URTEC-3870990-MS
... classification using supervised machine learning as described by Lei et al. (2022). This workflow aims to provide a more natural method of anisotropy classification than a criteria-based algorithm thanks to the rich physical information provided by the training datasets. The soft flags (soft because...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 20–22, 2022
Paper Number: URTEC-3723099-MS
... lithological logs and geomechanical logs, can be interpreted to identify areas of high shale anisotropy and heterogeneity. C-Means Clustering will group measured data to other ‘like’ sets of data along the wellbore to create a predefined number of classifications, or clusters. C-Means is a fuzzy clustering...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 20–22, 2022
Paper Number: URTEC-3719323-MS
... of fractures and cavities may lead to an increase in permeability, which may impact the production. Therefore, the dissolution of source rocks by fracturing fluid should be considered as a factor in completion design. upstream oil & gas mechanism imbibition classification brine imbibition...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 26–28, 2021
Paper Number: URTEC-2021-5024-MS
... and geomechanics reservoir problems. A few examples are highlighted to demonstrate the toolkit and applicable geologic problems. Classification: General Use URTeC 5024 Introduction Understanding the geomechanical response of reservoir discontinuities is important for predicting permeability enhancement...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 26–28, 2021
Paper Number: URTEC-2021-5375-MS
... artificial intelligence classification neural network accuracy deep learning core image optimized model wavy bedding sedimentary structure identification model machine learning upstream oil & gas information original model parallel bedding transfer learning misidentified image...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 20–22, 2020
Paper Number: URTEC-2020-2885-MS
... classification based on machine learning and natural language processing techniques provides new solutions to automatic anomaly detection in daily reports. According to the characteristics of the drilling & completion activity recording texts, an automatic text classification method was proposed based...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 20–22, 2020
Paper Number: URTEC-2020-2900-MS
...) by statistical methods or as augmented training and validation samples for deep neural classification and prediction. Some examples from Teapot Dome and Niobrara Shale are included to support the use of well-known QA techniques to improve supervised deep learning performance using 3-D seismic data. The core...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 20–22, 2020
Paper Number: URTEC-2020-3191-MS
... to the changes in elastic properties. A good relationship was established between TOC, vclay and porosity to elastic logs using both conventional and unconventional RPMs. A class-IV AVO response was observed in the Avalon formation. Finally, our analysis showed that a depth trend based 1D Bayesian classification...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 22–24, 2019
Paper Number: URTEC-2019-638-MS
... Intelligence translation machine learning classification neural network Upstream Oil & Gas accuracy image classification SEM image image segmentation Eagle Ford Woodford pixel unconventional reservoir microstructural analysis dataset model 5 alexnet probability Microstructure URTeC...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 22–24, 2019
Paper Number: URTEC-2019-897-MS
... a machine learning approach to predict the elastic moduli. We utilized an ensemble of data mining techniques and a database that include both the mineralogy and pore characteristics. Our results indicate that K-Means clustering yields best performance on data classification than all other tested methods...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 22–24, 2019
Paper Number: URTEC-2019-1002-MS
... Abstract A workflow is presented which places far greater emphasis on formation lithology than is usually employed during pore pressure and geomechanical studies. Advanced classification techniques are linked with conventional pore pressure prediction and geomechanical modelling methods...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 22–24, 2019
Paper Number: URTEC-2019-1090-MS
... or remote wells. Methodology In this study, we utilized state-of-art deep learning to develop our production prediction model. The deep learning is inspired by biological neural networks, which are widely used in tasks such as image classification, self-driving car and natural language processing. The basic...

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