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Keywords: Artificial Intelligence
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Proceedings Papers

Paper presented at the SPWLA 29th Formation Evaluation Symposium of Japan, September 12–13, 2024
Paper Number: SPWLA-JFES-2024-N
... symposium grayscale intensity geological subdiscipline intensity 0 spatial arrangement mineralogy texture prediction rock type mudstone arrangement textural loocv glcm matrix deep learning artificial intelligence reservoir characterization classification pixel young information japan...
Proceedings Papers

Paper presented at the SPWLA 29th Formation Evaluation Symposium of Japan, September 12–13, 2024
Paper Number: SPWLA-JFES-2024-A
... Formation Evaluation Society (JFES) and the submitting authors. This paper was prepared for the JFES 29th Annual Symposium held from September 12-13, 2024. ABSTRACT Machine learning (ML) which is a subset of artificial intelligence is being used in the field of upstream oil and gas industry to enhance its...
Proceedings Papers

Paper presented at the SPWLA 29th Formation Evaluation Symposium of Japan, September 12–13, 2024
Paper Number: SPWLA-JFES-2024-G
.... To overcome these deficiencies, this study focused on applying deep learning, a powerful tool in the field of Artificial Intelligence (AI), to upscaling of both the singlephase property(absolute permeability) and the multi-phase property (relative permeability). First, a large dataset was generated...
Proceedings Papers

Paper presented at the SPWLA 29th Formation Evaluation Symposium of Japan, September 12–13, 2024
Paper Number: SPWLA-JFES-2024-F
... of Mines. He has worked in different job responsibilities during his professional journey in Halliburton. His primary areas of interest are data analytics, velocity modeling, pre-stack seismic analysis, artificial intelligence and machine learning in G&G aspects. He has conducted numerous training...
Proceedings Papers

Paper presented at the SPWLA 29th Formation Evaluation Symposium of Japan, September 12–13, 2024
Paper Number: SPWLA-JFES-2024-C
... for downhole fluid identification sensor design, formation pressure test and sampling modeling, and automation of wireline formation testers using artificial intelligence and machine learning techniques. Dai received a PhD degree in analytical chemistry, with a specialization in chemometrics and NIR...
Proceedings Papers

Paper presented at the SPWLA 28th Formation Evaluation Symposium of Japan, September 13–14, 2023
Paper Number: SPWLA-JFES-2023-U
.... CONCLUSIONS Artificial intelligence and machine learning are emerging as powerful tools to aid in reservoir analysis and characterization. This paper has presented the application of CbML for normalizing data in a field study based on core data available in key wells. Quality assurance and QC indicated...
Proceedings Papers

Paper presented at the SPWLA 28th Formation Evaluation Symposium of Japan, September 13–14, 2023
Paper Number: SPWLA-JFES-2023-S
.... upstream oil & gas solver core analysis geology artificial intelligence machine learning japan government mineral log analysis 28th formation evaluation symposium numerical solver multi-salinity analysis synthetic data equation ffri measurement geologist asia government well logging...
Proceedings Papers

Paper presented at the SPWLA 28th Formation Evaluation Symposium of Japan, September 13–14, 2023
Paper Number: SPWLA-JFES-2023-J
... analysis in this study. japan government geology upstream oil & gas artificial intelligence asia government reservoir surveillance geologist borehole imaging rock type sedimentary rock log analysis oman government production monitoring silicate borehole image reservoir...
Proceedings Papers

Paper presented at the SPWLA 27th Formation Evaluation Symposium of Japan, September 14–15, 2022
Paper Number: SPWLA-JFES-2022-K
... artificial intelligence coast formation evaluation symposium roughness kyushu university department university outcrop correlation nogita coast spatial resolution change japan engineering resource engineering The 27th Formation Evaluation Symposium of Japan, September 14-15, 2022 QUANTIFYING...
Proceedings Papers

Paper presented at the SPWLA 27th Formation Evaluation Symposium of Japan, September 14–15, 2022
Paper Number: SPWLA-JFES-2022-M
... the distribution of effective tight sandstones. permeability log analysis reservoir porosity upstream oil & gas machine learning chang 8 well logging mercury injection saturation characterization artificial intelligence formation evaluation symposium ansai region development university...
Proceedings Papers

Paper presented at the SPWLA 26th Formation Evaluation Symposium of Japan, September 30–October 7, 2021
Paper Number: SPWLA-JFES-2021-E
... permeability model has resulted in enhanced completion decisions for well-work operations (additional perforation and re-perforation campaigns). reservoir characterization log analysis drilling operation flow in porous media machine learning artificial intelligence well logging upstream oil...
Proceedings Papers

Paper presented at the SPWLA 26th Formation Evaluation Symposium of Japan, September 30–October 7, 2021
Paper Number: SPWLA-JFES-2021-D
...-2 to draw the multiple images of facies distribution constrained by well data using Generative Adversarial Network (GAN). flow in porous media machine learning artificial intelligence upstream oil & gas well data soft data th october 2021 reservoir characterization deep learning...
Proceedings Papers

Paper presented at the SPWLA 25th Formation Evaluation Symposium of Japan, September 25–26, 2019
Paper Number: SPWLA-JFES-2019-Q
...) for the formation evaluation in an exploration well. The result was used not only to optimize the drill stem test, but also it showed the good match with DST, and provided the general practice in this field for later well correlations. Artificial Intelligence flow in porous media permeability evaluation...

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