Fault and fracture network characterization using soft computing techniques: application to geologically complex and deeply-buried geothermal reservoirs Article Swipe
Qamar Yasin
,
Yan Ding
,
Qizhen Du
,
Hung Vo Thanh
,
Bo Liu
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1007/s40948-024-00792-8
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1007/s40948-024-00792-8
Geothermal energy is a sustainable energy source that meets the needs of the climate crisis and global warming caused by fossil fuel burning. Geothermal resources are found in complex geological settings, with faults and interconnected networks of fractures acting as pathways for fluid circulation. Identifying faults and fractures is an essential component of exploiting geothermal resources. However, accurately predicting fractures without high-resolution geophysical logs (e.g., image logs) and well-core samples is challenging. Soft computing techniques, such as machine learning, make it possible to map fracture networks at a finer resolution. This study employed four supervised machine learning techniques (multilayer perceptron (MLP), random forests (RF), extreme gradient boosting (XGBoost), and support vector regression (SVR)) to identify fractures in geothermal carbonate reservoirs in the sub-basins of East China. The models were trained and tested on a diverse well-logging dataset collected at the field scale. A comparison of the predicted results revealed that XGBoost with optimized hyperparameters and data division achieved the best performance than RF, MLP, and SVR with RMSE = 0.02 and R 2 = 0.92. The Q-learning algorithm outperformed grid search, Bayesian, and ant colony optimizations. The blind well test demonstrates that it is possible to accurately identify fractures by applying machine learning algorithms to standard well logs. In addition, the comparative analysis indicates that XGBoost was able to handle the complex relationship between input parameters (e.g., DTP > RD > DEN > GR > CAL > RS > U > CNL) and fracture in geologically complex geothermal carbonate reservoirs. Furthermore, comparing the XGBoost model with previous studies proved superior in training and testing. This study suggests that XGBoost with Q-learning-based optimized hyperparameters and data division is a suitable algorithm for identifying fractures using well-log data to explore complex geothermal systems in carbonate rocks. Graphical abstract
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1007/s40948-024-00792-8
- https://link.springer.com/content/pdf/10.1007/s40948-024-00792-8.pdf
- OA Status
- gold
- Cited By
- 5
- References
- 36
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4396617921
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4396617921Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1007/s40948-024-00792-8Digital Object Identifier
- Title
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Fault and fracture network characterization using soft computing techniques: application to geologically complex and deeply-buried geothermal reservoirsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-05-03Full publication date if available
- Authors
-
Qamar Yasin, Yan Ding, Qizhen Du, Hung Vo Thanh, Bo LiuList of authors in order
- Landing page
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https://doi.org/10.1007/s40948-024-00792-8Publisher landing page
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https://link.springer.com/content/pdf/10.1007/s40948-024-00792-8.pdfDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://link.springer.com/content/pdf/10.1007/s40948-024-00792-8.pdfDirect OA link when available
- Concepts
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Geothermal gradient, Geology, Fault (geology), Characterization (materials science), Fracture (geology), Petrology, Seismology, Geotechnical engineering, Geophysics, Materials science, NanotechnologyTop concepts (fields/topics) attached by OpenAlex
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5Total citation count in OpenAlex
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2025: 4, 2024: 1Per-year citation counts (last 5 years)
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36Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| publication_date | 2024-05-03 |
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| abstract_inverted_index.= | 169, 174 |
| abstract_inverted_index.A | 143 |
| abstract_inverted_index.R | 172 |
| abstract_inverted_index.U | 240 |
| abstract_inverted_index.a | 4, 88, 134, 278 |
| abstract_inverted_index.GR | 234 |
| abstract_inverted_index.In | 209 |
| abstract_inverted_index.RD | 230 |
| abstract_inverted_index.RS | 238 |
| abstract_inverted_index.an | 50 |
| abstract_inverted_index.as | 40, 77 |
| abstract_inverted_index.at | 87, 139 |
| abstract_inverted_index.by | 20, 200 |
| abstract_inverted_index.in | 28, 117, 121, 245, 261, 292 |
| abstract_inverted_index.is | 3, 49, 71, 194, 277 |
| abstract_inverted_index.it | 81, 193 |
| abstract_inverted_index.of | 12, 37, 53, 124, 145 |
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| abstract_inverted_index.to | 83, 114, 196, 205, 219, 287 |
| abstract_inverted_index.CAL | 236 |
| abstract_inverted_index.DEN | 232 |
| abstract_inverted_index.DTP | 228 |
| abstract_inverted_index.RF, | 163 |
| abstract_inverted_index.SVR | 166 |
| abstract_inverted_index.The | 127, 176, 187 |
| abstract_inverted_index.and | 16, 34, 47, 68, 109, 131, 155, 165, 171, 183, 243, 263, 274 |
| abstract_inverted_index.ant | 184 |
| abstract_inverted_index.are | 26 |
| abstract_inverted_index.for | 42, 281 |
| abstract_inverted_index.map | 84 |
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| abstract_inverted_index.was | 217 |
| abstract_inverted_index.> | 229, 231, 233, 235, 237, 239, 241 |
| abstract_inverted_index.0.02 | 170 |
| abstract_inverted_index.CNL) | 242 |
| abstract_inverted_index.East | 125 |
| abstract_inverted_index.MLP, | 164 |
| abstract_inverted_index.RMSE | 168 |
| abstract_inverted_index.Soft | 73 |
| abstract_inverted_index.This | 91, 265 |
| abstract_inverted_index.able | 218 |
| abstract_inverted_index.best | 160 |
| abstract_inverted_index.data | 156, 275, 286 |
| abstract_inverted_index.four | 94 |
| abstract_inverted_index.fuel | 22 |
| abstract_inverted_index.grid | 180 |
| abstract_inverted_index.logs | 64 |
| abstract_inverted_index.make | 80 |
| abstract_inverted_index.such | 76 |
| abstract_inverted_index.test | 190 |
| abstract_inverted_index.than | 162 |
| abstract_inverted_index.that | 8, 150, 192, 215, 268 |
| abstract_inverted_index.well | 189, 207 |
| abstract_inverted_index.were | 129 |
| abstract_inverted_index.with | 32, 152, 167, 256, 270 |
| abstract_inverted_index.(RF), | 104 |
| abstract_inverted_index.0.92. | 175 |
| abstract_inverted_index.blind | 188 |
| abstract_inverted_index.field | 141 |
| abstract_inverted_index.finer | 89 |
| abstract_inverted_index.fluid | 43 |
| abstract_inverted_index.found | 27 |
| abstract_inverted_index.image | 66 |
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| abstract_inverted_index.logs) | 67 |
| abstract_inverted_index.logs. | 208 |
| abstract_inverted_index.meets | 9 |
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| abstract_inverted_index.needs | 11 |
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| abstract_inverted_index.using | 284 |
| abstract_inverted_index.(MLP), | 101 |
| abstract_inverted_index.(SVR)) | 113 |
| abstract_inverted_index.(e.g., | 65, 227 |
| abstract_inverted_index.China. | 126 |
| abstract_inverted_index.acting | 39 |
| abstract_inverted_index.caused | 19 |
| abstract_inverted_index.colony | 185 |
| abstract_inverted_index.crisis | 15 |
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| abstract_inverted_index.fossil | 21 |
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| abstract_inverted_index.proved | 259 |
| abstract_inverted_index.random | 102 |
| abstract_inverted_index.rocks. | 294 |
| abstract_inverted_index.scale. | 142 |
| abstract_inverted_index.source | 7 |
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| abstract_inverted_index.vector | 111 |
| abstract_inverted_index.XGBoost | 151, 216, 254, 269 |
| abstract_inverted_index.between | 224 |
| abstract_inverted_index.climate | 14 |
| abstract_inverted_index.complex | 29, 222, 247, 289 |
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| abstract_inverted_index.forests | 103 |
| abstract_inverted_index.machine | 78, 96, 202 |
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| abstract_inverted_index.search, | 181 |
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| abstract_inverted_index.fracture | 85, 244 |
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| abstract_inverted_index.identify | 115, 198 |
| abstract_inverted_index.learning | 97, 203 |
| abstract_inverted_index.networks | 36, 86 |
| abstract_inverted_index.pathways | 41 |
| abstract_inverted_index.possible | 82, 195 |
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| abstract_inverted_index.superior | 260 |
| abstract_inverted_index.testing. | 264 |
| abstract_inverted_index.training | 262 |
| abstract_inverted_index.well-log | 285 |
| abstract_inverted_index.Bayesian, | 182 |
| abstract_inverted_index.Graphical | 295 |
| abstract_inverted_index.addition, | 210 |
| abstract_inverted_index.algorithm | 178, 280 |
| abstract_inverted_index.carbonate | 119, 249, 293 |
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| abstract_inverted_index.comparing | 252 |
| abstract_inverted_index.component | 52 |
| abstract_inverted_index.computing | 74 |
| abstract_inverted_index.essential | 51 |
| abstract_inverted_index.fractures | 38, 48, 60, 116, 199, 283 |
| abstract_inverted_index.indicates | 214 |
| abstract_inverted_index.learning, | 79 |
| abstract_inverted_index.optimized | 153, 272 |
| abstract_inverted_index.predicted | 147 |
| abstract_inverted_index.resources | 25 |
| abstract_inverted_index.settings, | 31 |
| abstract_inverted_index.well-core | 69 |
| abstract_inverted_index.(XGBoost), | 108 |
| abstract_inverted_index.Geothermal | 1, 24 |
| abstract_inverted_index.Q-learning | 177 |
| abstract_inverted_index.accurately | 58, 197 |
| abstract_inverted_index.algorithms | 204 |
| abstract_inverted_index.comparison | 144 |
| abstract_inverted_index.exploiting | 54 |
| abstract_inverted_index.geological | 30 |
| abstract_inverted_index.geothermal | 55, 118, 248, 290 |
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| abstract_inverted_index.predicting | 59 |
| abstract_inverted_index.regression | 112 |
| abstract_inverted_index.reservoirs | 120 |
| abstract_inverted_index.resources. | 56 |
| abstract_inverted_index.sub-basins | 123 |
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| abstract_inverted_index.(multilayer | 99 |
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| abstract_inverted_index.comparative | 212 |
| abstract_inverted_index.geophysical | 63 |
| abstract_inverted_index.identifying | 282 |
| abstract_inverted_index.performance | 161 |
| abstract_inverted_index.reservoirs. | 250 |
| abstract_inverted_index.resolution. | 90 |
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| abstract_inverted_index.techniques, | 75 |
| abstract_inverted_index.Furthermore, | 251 |
| abstract_inverted_index.challenging. | 72 |
| abstract_inverted_index.circulation. | 44 |
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| abstract_inverted_index.outperformed | 179 |
| abstract_inverted_index.relationship | 223 |
| abstract_inverted_index.well-logging | 136 |
| abstract_inverted_index.interconnected | 35 |
| abstract_inverted_index.optimizations. | 186 |
| abstract_inverted_index.high-resolution | 62 |
| abstract_inverted_index.hyperparameters | 154, 273 |
| abstract_inverted_index.Q-learning-based | 271 |
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| institutions_distinct_count | 5 |
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| citation_normalized_percentile.is_in_top_10_percent | True |