Well logging super-resolution based on fractal interpolation enhanced by BiLSTM-AMPSO
Abstract Enhancing the level of geological characterization and analysis has always been a challenging task in the exploration and development of unconventional oil and gas reservoirs. In order to address these challenges, geophysical logging is one of the most important data for characterizing targ...
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| Format: | Article |
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Springer
2025-05-01
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| Series: | Geomechanics and Geophysics for Geo-Energy and Geo-Resources |
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| Online Access: | https://doi.org/10.1007/s40948-025-00969-9 |
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| author | Jian Han Yu Deng Bing Zheng Zhimin Cao |
| author_facet | Jian Han Yu Deng Bing Zheng Zhimin Cao |
| author_sort | Jian Han |
| collection | DOAJ |
| description | Abstract Enhancing the level of geological characterization and analysis has always been a challenging task in the exploration and development of unconventional oil and gas reservoirs. In order to address these challenges, geophysical logging is one of the most important data for characterizing target reservoir model. However, the vertical resolution of conventional well logging data is often insufficient to handle the fine-scale characterization tasks of complex unconventional reservoirs. Therefore, several vertical resolution enhancement techniques have been devised to attain as fine as possible reservoir characterization. Nevertheless, local sharp structure with sufficient high frequency information cannot always been well recovered. In this paper, to improve the vertical resolution of well-logging data, a novel fractal interpolation based well logging super-resolution method was proposed by employing bidirectional long short-term memory (BiLSTM) and adaptive mutation particle swarm optimization (AMPSO). Specifically, mutation factors are introduced into the particle swarm optimization (PSO) algorithm to enhance search accuracy. The AMPSO is utilized to obtain optimal solutions for the vertical scaling factors in the fractal interpolation iterated function system (IFS), ensuring these factors adhere to fractal theory constraints. This approach constructs an IFS that aligns closely with the fractal characteristics of the logging curves, thereby enabling more effective extraction of structural information and finer-scale information recovery. Experimental results indicate that, compared to well logging super-resolution methods such as bicubic interpolation, convolutional neural networks, and random forests, the proposed method not only effectively preserves the overall contours and detailed information of the well logging curves but also exhibits good stability across different regions and various well logging curves. |
| format | Article |
| id | doaj-art-5d2df9c6a7e041429eeba25b1ecf85db |
| institution | DOAJ |
| issn | 2363-8419 2363-8427 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Springer |
| record_format | Article |
| series | Geomechanics and Geophysics for Geo-Energy and Geo-Resources |
| spelling | doaj-art-5d2df9c6a7e041429eeba25b1ecf85db2025-08-20T03:16:47ZengSpringerGeomechanics and Geophysics for Geo-Energy and Geo-Resources2363-84192363-84272025-05-0111112610.1007/s40948-025-00969-9Well logging super-resolution based on fractal interpolation enhanced by BiLSTM-AMPSOJian Han0Yu Deng1Bing Zheng2Zhimin Cao3NEPU Sanya Offshore Oil & Gas Research InstituteNEPU Sanya Offshore Oil & Gas Research InstituteHainan Engineering Research Center for Virtual Reality Technology and Systems (QiongfaGaigaoji [2023] No. 818), Hainan Vocational University of Science and TechnologyNEPU Sanya Offshore Oil & Gas Research InstituteAbstract Enhancing the level of geological characterization and analysis has always been a challenging task in the exploration and development of unconventional oil and gas reservoirs. In order to address these challenges, geophysical logging is one of the most important data for characterizing target reservoir model. However, the vertical resolution of conventional well logging data is often insufficient to handle the fine-scale characterization tasks of complex unconventional reservoirs. Therefore, several vertical resolution enhancement techniques have been devised to attain as fine as possible reservoir characterization. Nevertheless, local sharp structure with sufficient high frequency information cannot always been well recovered. In this paper, to improve the vertical resolution of well-logging data, a novel fractal interpolation based well logging super-resolution method was proposed by employing bidirectional long short-term memory (BiLSTM) and adaptive mutation particle swarm optimization (AMPSO). Specifically, mutation factors are introduced into the particle swarm optimization (PSO) algorithm to enhance search accuracy. The AMPSO is utilized to obtain optimal solutions for the vertical scaling factors in the fractal interpolation iterated function system (IFS), ensuring these factors adhere to fractal theory constraints. This approach constructs an IFS that aligns closely with the fractal characteristics of the logging curves, thereby enabling more effective extraction of structural information and finer-scale information recovery. Experimental results indicate that, compared to well logging super-resolution methods such as bicubic interpolation, convolutional neural networks, and random forests, the proposed method not only effectively preserves the overall contours and detailed information of the well logging curves but also exhibits good stability across different regions and various well logging curves.https://doi.org/10.1007/s40948-025-00969-9Unconventional reservoirsSuper-resolutionFractal interpolationAMPSOBiLSTM |
| spellingShingle | Jian Han Yu Deng Bing Zheng Zhimin Cao Well logging super-resolution based on fractal interpolation enhanced by BiLSTM-AMPSO Geomechanics and Geophysics for Geo-Energy and Geo-Resources Unconventional reservoirs Super-resolution Fractal interpolation AMPSO BiLSTM |
| title | Well logging super-resolution based on fractal interpolation enhanced by BiLSTM-AMPSO |
| title_full | Well logging super-resolution based on fractal interpolation enhanced by BiLSTM-AMPSO |
| title_fullStr | Well logging super-resolution based on fractal interpolation enhanced by BiLSTM-AMPSO |
| title_full_unstemmed | Well logging super-resolution based on fractal interpolation enhanced by BiLSTM-AMPSO |
| title_short | Well logging super-resolution based on fractal interpolation enhanced by BiLSTM-AMPSO |
| title_sort | well logging super resolution based on fractal interpolation enhanced by bilstm ampso |
| topic | Unconventional reservoirs Super-resolution Fractal interpolation AMPSO BiLSTM |
| url | https://doi.org/10.1007/s40948-025-00969-9 |
| work_keys_str_mv | AT jianhan wellloggingsuperresolutionbasedonfractalinterpolationenhancedbybilstmampso AT yudeng wellloggingsuperresolutionbasedonfractalinterpolationenhancedbybilstmampso AT bingzheng wellloggingsuperresolutionbasedonfractalinterpolationenhancedbybilstmampso AT zhimincao wellloggingsuperresolutionbasedonfractalinterpolationenhancedbybilstmampso |