CN102478668A  Method for applying seismic multiattribute parameters to predicting coal seam thickness  Google Patents
Method for applying seismic multiattribute parameters to predicting coal seam thickness Download PDFInfo
 Publication number
 CN102478668A CN102478668A CN2010105682723A CN201010568272A CN102478668A CN 102478668 A CN102478668 A CN 102478668A CN 2010105682723 A CN2010105682723 A CN 2010105682723A CN 201010568272 A CN201010568272 A CN 201010568272A CN 102478668 A CN102478668 A CN 102478668A
 Authority
 CN
 China
 Prior art keywords
 coal seam
 seismic properties
 thickness
 value
 seismic
 Prior art date
 Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
 Pending
Links
 OKTJSMMVPCPJKNUHFFFAOYSAN carbon Chemical compound data:image/svg+xml;base64,<?xml version='1.0' encoding='iso-8859-1'?>
<svg version='1.1' baseProfile='full'
              xmlns='http://www.w3.org/2000/svg'
                      xmlns:rdkit='http://www.rdkit.org/xml'
                      xmlns:xlink='http://www.w3.org/1999/xlink'
                  xml:space='preserve'
width='300px' height='300px' viewBox='0 0 300 300'>
<!-- END OF HEADER -->
<rect style='opacity:1.0;fill:#FFFFFF;stroke:none' width='300' height='300' x='0' y='0'> </rect>
<text x='138' y='170' class='atom-0' style='font-size:40px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#3B4143' >C</text>
<path d='M 168.364,138 L 168.356,137.828 L 168.334,137.657 L 168.297,137.489 L 168.246,137.325 L 168.181,137.166 L 168.103,137.012 L 168.011,136.867 L 167.908,136.729 L 167.793,136.601 L 167.667,136.483 L 167.532,136.377 L 167.388,136.282 L 167.237,136.201 L 167.079,136.132 L 166.916,136.078 L 166.749,136.037 L 166.578,136.012 L 166.407,136 L 166.235,136.004 L 166.064,136.023 L 165.895,136.056 L 165.729,136.103 L 165.569,136.165 L 165.414,136.24 L 165.266,136.328 L 165.126,136.429 L 164.996,136.541 L 164.875,136.664 L 164.766,136.797 L 164.669,136.939 L 164.584,137.088 L 164.512,137.245 L 164.454,137.407 L 164.41,137.573 L 164.38,137.743 L 164.365,137.914 L 164.365,138.086 L 164.38,138.257 L 164.41,138.427 L 164.454,138.593 L 164.512,138.755 L 164.584,138.912 L 164.669,139.061 L 164.766,139.203 L 164.875,139.336 L 164.996,139.459 L 165.126,139.571 L 165.266,139.672 L 165.414,139.76 L 165.569,139.835 L 165.729,139.897 L 165.895,139.944 L 166.064,139.977 L 166.235,139.996 L 166.407,140 L 166.578,139.988 L 166.749,139.963 L 166.916,139.922 L 167.079,139.868 L 167.237,139.799 L 167.388,139.718 L 167.532,139.623 L 167.667,139.517 L 167.793,139.399 L 167.908,139.271 L 168.011,139.133 L 168.103,138.988 L 168.181,138.834 L 168.246,138.675 L 168.297,138.511 L 168.334,138.343 L 168.356,138.172 L 168.364,138 L 166.364,138 Z' style='fill:#000000;fill-rule:evenodd;fill-opacity:1;stroke:#000000;stroke-width:0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1;' />
<path d='M 168.364,162 L 168.356,161.828 L 168.334,161.657 L 168.297,161.489 L 168.246,161.325 L 168.181,161.166 L 168.103,161.012 L 168.011,160.867 L 167.908,160.729 L 167.793,160.601 L 167.667,160.483 L 167.532,160.377 L 167.388,160.282 L 167.237,160.201 L 167.079,160.132 L 166.916,160.078 L 166.749,160.037 L 166.578,160.012 L 166.407,160 L 166.235,160.004 L 166.064,160.023 L 165.895,160.056 L 165.729,160.103 L 165.569,160.165 L 165.414,160.24 L 165.266,160.328 L 165.126,160.429 L 164.996,160.541 L 164.875,160.664 L 164.766,160.797 L 164.669,160.939 L 164.584,161.088 L 164.512,161.245 L 164.454,161.407 L 164.41,161.573 L 164.38,161.743 L 164.365,161.914 L 164.365,162.086 L 164.38,162.257 L 164.41,162.427 L 164.454,162.593 L 164.512,162.755 L 164.584,162.912 L 164.669,163.061 L 164.766,163.203 L 164.875,163.336 L 164.996,163.459 L 165.126,163.571 L 165.266,163.672 L 165.414,163.76 L 165.569,163.835 L 165.729,163.897 L 165.895,163.944 L 166.064,163.977 L 166.235,163.996 L 166.407,164 L 166.578,163.988 L 166.749,163.963 L 166.916,163.922 L 167.079,163.868 L 167.237,163.799 L 167.388,163.718 L 167.532,163.623 L 167.667,163.517 L 167.793,163.399 L 167.908,163.271 L 168.011,163.133 L 168.103,162.988 L 168.181,162.834 L 168.246,162.675 L 168.297,162.511 L 168.334,162.343 L 168.356,162.172 L 168.364,162 L 166.364,162 Z' style='fill:#000000;fill-rule:evenodd;fill-opacity:1;stroke:#000000;stroke-width:0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1;' />
<path d='M 168.364,146 L 168.356,145.828 L 168.334,145.657 L 168.297,145.489 L 168.246,145.325 L 168.181,145.166 L 168.103,145.012 L 168.011,144.867 L 167.908,144.729 L 167.793,144.601 L 167.667,144.483 L 167.532,144.377 L 167.388,144.282 L 167.237,144.201 L 167.079,144.132 L 166.916,144.078 L 166.749,144.037 L 166.578,144.012 L 166.407,144 L 166.235,144.004 L 166.064,144.023 L 165.895,144.056 L 165.729,144.103 L 165.569,144.165 L 165.414,144.24 L 165.266,144.328 L 165.126,144.429 L 164.996,144.541 L 164.875,144.664 L 164.766,144.797 L 164.669,144.939 L 164.584,145.088 L 164.512,145.245 L 164.454,145.407 L 164.41,145.573 L 164.38,145.743 L 164.365,145.914 L 164.365,146.086 L 164.38,146.257 L 164.41,146.427 L 164.454,146.593 L 164.512,146.755 L 164.584,146.912 L 164.669,147.061 L 164.766,147.203 L 164.875,147.336 L 164.996,147.459 L 165.126,147.571 L 165.266,147.672 L 165.414,147.76 L 165.569,147.835 L 165.729,147.897 L 165.895,147.944 L 166.064,147.977 L 166.235,147.996 L 166.407,148 L 166.578,147.988 L 166.749,147.963 L 166.916,147.922 L 167.079,147.868 L 167.237,147.799 L 167.388,147.718 L 167.532,147.623 L 167.667,147.517 L 167.793,147.399 L 167.908,147.271 L 168.011,147.133 L 168.103,146.988 L 168.181,146.834 L 168.246,146.675 L 168.297,146.511 L 168.334,146.343 L 168.356,146.172 L 168.364,146 L 166.364,146 Z' style='fill:#000000;fill-rule:evenodd;fill-opacity:1;stroke:#000000;stroke-width:0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1;' />
<path d='M 168.364,154 L 168.356,153.828 L 168.334,153.657 L 168.297,153.489 L 168.246,153.325 L 168.181,153.166 L 168.103,153.012 L 168.011,152.867 L 167.908,152.729 L 167.793,152.601 L 167.667,152.483 L 167.532,152.377 L 167.388,152.282 L 167.237,152.201 L 167.079,152.132 L 166.916,152.078 L 166.749,152.037 L 166.578,152.012 L 166.407,152 L 166.235,152.004 L 166.064,152.023 L 165.895,152.056 L 165.729,152.103 L 165.569,152.165 L 165.414,152.24 L 165.266,152.328 L 165.126,152.429 L 164.996,152.541 L 164.875,152.664 L 164.766,152.797 L 164.669,152.939 L 164.584,153.088 L 164.512,153.245 L 164.454,153.407 L 164.41,153.573 L 164.38,153.743 L 164.365,153.914 L 164.365,154.086 L 164.38,154.257 L 164.41,154.427 L 164.454,154.593 L 164.512,154.755 L 164.584,154.912 L 164.669,155.061 L 164.766,155.203 L 164.875,155.336 L 164.996,155.459 L 165.126,155.571 L 165.266,155.672 L 165.414,155.76 L 165.569,155.835 L 165.729,155.897 L 165.895,155.944 L 166.064,155.977 L 166.235,155.996 L 166.407,156 L 166.578,155.988 L 166.749,155.963 L 166.916,155.922 L 167.079,155.868 L 167.237,155.799 L 167.388,155.718 L 167.532,155.623 L 167.667,155.517 L 167.793,155.399 L 167.908,155.271 L 168.011,155.133 L 168.103,154.988 L 168.181,154.834 L 168.246,154.675 L 168.297,154.511 L 168.334,154.343 L 168.356,154.172 L 168.364,154 L 166.364,154 Z' style='fill:#000000;fill-rule:evenodd;fill-opacity:1;stroke:#000000;stroke-width:0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1;' />
</svg>
 data:image/svg+xml;base64,<?xml version='1.0' encoding='iso-8859-1'?>
<svg version='1.1' baseProfile='full'
              xmlns='http://www.w3.org/2000/svg'
                      xmlns:rdkit='http://www.rdkit.org/xml'
                      xmlns:xlink='http://www.w3.org/1999/xlink'
                  xml:space='preserve'
width='85px' height='85px' viewBox='0 0 85 85'>
<!-- END OF HEADER -->
<rect style='opacity:1.0;fill:#FFFFFF;stroke:none' width='85' height='85' x='0' y='0'> </rect>
<text x='35.0455' y='53.5909' class='atom-0' style='font-size:23px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#3B4143' >C</text>
<path d='M 53.5909,35.0455 L 53.5866,34.9458 L 53.5738,34.8469 L 53.5525,34.7495 L 53.5229,34.6542 L 53.4852,34.5619 L 53.4398,34.4731 L 53.3868,34.3886 L 53.3268,34.3089 L 53.2602,34.2347 L 53.1874,34.1665 L 53.1091,34.1048 L 53.0257,34.0501 L 52.9379,34.0027 L 52.8464,33.9631 L 52.7518,33.9314 L 52.6549,33.908 L 52.5563,33.8931 L 52.4568,33.8866 L 52.357,33.8888 L 52.2579,33.8995 L 52.16,33.9187 L 52.0642,33.9462 L 51.971,33.9819 L 51.8813,34.0254 L 51.7957,34.0765 L 51.7147,34.1348 L 51.6391,34.1998 L 51.5693,34.2711 L 51.506,34.3481 L 51.4494,34.4303 L 51.4002,34.517 L 51.3586,34.6077 L 51.3249,34.7015 L 51.2995,34.798 L 51.2824,34.8962 L 51.2738,34.9956 L 51.2738,35.0953 L 51.2824,35.1947 L 51.2995,35.2929 L 51.3249,35.3894 L 51.3586,35.4833 L 51.4002,35.5739 L 51.4494,35.6606 L 51.506,35.7428 L 51.5693,35.8198 L 51.6391,35.8911 L 51.7147,35.9561 L 51.7957,36.0144 L 51.8813,36.0655 L 51.971,36.109 L 52.0642,36.1447 L 52.16,36.1722 L 52.2579,36.1914 L 52.357,36.2021 L 52.4568,36.2043 L 52.5563,36.1978 L 52.6549,36.1829 L 52.7518,36.1595 L 52.8464,36.1279 L 52.9379,36.0882 L 53.0257,36.0408 L 53.1091,35.9861 L 53.1874,35.9244 L 53.2602,35.8562 L 53.3268,35.782 L 53.3868,35.7023 L 53.4398,35.6178 L 53.4852,35.529 L 53.5229,35.4367 L 53.5525,35.3414 L 53.5738,35.244 L 53.5866,35.1451 L 53.5909,35.0455 L 52.4318,35.0455 Z' style='fill:#000000;fill-rule:evenodd;fill-opacity:1;stroke:#000000;stroke-width:0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1;' />
<path d='M 53.5909,48.9545 L 53.5866,48.8549 L 53.5738,48.756 L 53.5525,48.6586 L 53.5229,48.5633 L 53.4852,48.471 L 53.4398,48.3822 L 53.3868,48.2977 L 53.3268,48.218 L 53.2602,48.1438 L 53.1874,48.0756 L 53.1091,48.0139 L 53.0257,47.9592 L 52.9379,47.9118 L 52.8464,47.8721 L 52.7518,47.8405 L 52.6549,47.8171 L 52.5563,47.8022 L 52.4568,47.7957 L 52.357,47.7979 L 52.2579,47.8086 L 52.16,47.8278 L 52.0642,47.8553 L 51.971,47.891 L 51.8813,47.9345 L 51.7957,47.9856 L 51.7147,48.0439 L 51.6391,48.1089 L 51.5693,48.1802 L 51.506,48.2572 L 51.4494,48.3394 L 51.4002,48.4261 L 51.3586,48.5167 L 51.3249,48.6106 L 51.2995,48.7071 L 51.2824,48.8053 L 51.2738,48.9047 L 51.2738,49.0044 L 51.2824,49.1038 L 51.2995,49.202 L 51.3249,49.2985 L 51.3586,49.3923 L 51.4002,49.483 L 51.4494,49.5697 L 51.506,49.6519 L 51.5693,49.7289 L 51.6391,49.8002 L 51.7147,49.8652 L 51.7957,49.9235 L 51.8813,49.9746 L 51.971,50.0181 L 52.0642,50.0538 L 52.16,50.0813 L 52.2579,50.1005 L 52.357,50.1112 L 52.4568,50.1134 L 52.5563,50.1069 L 52.6549,50.092 L 52.7518,50.0686 L 52.8464,50.0369 L 52.9379,49.9973 L 53.0257,49.9499 L 53.1091,49.8952 L 53.1874,49.8335 L 53.2602,49.7653 L 53.3268,49.6911 L 53.3868,49.6114 L 53.4398,49.5269 L 53.4852,49.4381 L 53.5229,49.3458 L 53.5525,49.2505 L 53.5738,49.1531 L 53.5866,49.0542 L 53.5909,48.9545 L 52.4318,48.9545 Z' style='fill:#000000;fill-rule:evenodd;fill-opacity:1;stroke:#000000;stroke-width:0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1;' />
<path d='M 53.5909,39.6818 L 53.5866,39.5822 L 53.5738,39.4833 L 53.5525,39.3858 L 53.5229,39.2906 L 53.4852,39.1983 L 53.4398,39.1095 L 53.3868,39.025 L 53.3268,38.9453 L 53.2602,38.8711 L 53.1874,38.8029 L 53.1091,38.7412 L 53.0257,38.6864 L 52.9379,38.6391 L 52.8464,38.5994 L 52.7518,38.5678 L 52.6549,38.5444 L 52.5563,38.5294 L 52.4568,38.523 L 52.357,38.5251 L 52.2579,38.5359 L 52.16,38.555 L 52.0642,38.5826 L 51.971,38.6183 L 51.8813,38.6618 L 51.7957,38.7129 L 51.7147,38.7712 L 51.6391,38.8362 L 51.5693,38.9075 L 51.506,38.9845 L 51.4494,39.0667 L 51.4002,39.1534 L 51.3586,39.244 L 51.3249,39.3379 L 51.2995,39.4343 L 51.2824,39.5326 L 51.2738,39.632 L 51.2738,39.7317 L 51.2824,39.831 L 51.2995,39.9293 L 51.3249,40.0257 L 51.3586,40.1196 L 51.4002,40.2103 L 51.4494,40.297 L 51.506,40.3792 L 51.5693,40.4562 L 51.6391,40.5274 L 51.7147,40.5925 L 51.7957,40.6507 L 51.8813,40.7018 L 51.971,40.7454 L 52.0642,40.7811 L 52.16,40.8086 L 52.2579,40.8278 L 52.357,40.8385 L 52.4568,40.8406 L 52.5563,40.8342 L 52.6549,40.8192 L 52.7518,40.7959 L 52.8464,40.7642 L 52.9379,40.7246 L 53.0257,40.6772 L 53.1091,40.6225 L 53.1874,40.5608 L 53.2602,40.4926 L 53.3268,40.4183 L 53.3868,40.3387 L 53.4398,40.2541 L 53.4852,40.1654 L 53.5229,40.073 L 53.5525,39.9778 L 53.5738,39.8804 L 53.5866,39.7815 L 53.5909,39.6818 L 52.4318,39.6818 Z' style='fill:#000000;fill-rule:evenodd;fill-opacity:1;stroke:#000000;stroke-width:0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1;' />
<path d='M 53.5909,44.3182 L 53.5866,44.2185 L 53.5738,44.1196 L 53.5525,44.0222 L 53.5229,43.927 L 53.4852,43.8346 L 53.4398,43.7459 L 53.3868,43.6613 L 53.3268,43.5817 L 53.2602,43.5074 L 53.1874,43.4392 L 53.1091,43.3775 L 53.0257,43.3228 L 52.9379,43.2754 L 52.8464,43.2358 L 52.7518,43.2041 L 52.6549,43.1808 L 52.5563,43.1658 L 52.4568,43.1594 L 52.357,43.1615 L 52.2579,43.1722 L 52.16,43.1914 L 52.0642,43.2189 L 51.971,43.2546 L 51.8813,43.2982 L 51.7957,43.3493 L 51.7147,43.4075 L 51.6391,43.4726 L 51.5693,43.5438 L 51.506,43.6208 L 51.4494,43.703 L 51.4002,43.7897 L 51.3586,43.8804 L 51.3249,43.9743 L 51.2995,44.0707 L 51.2824,44.169 L 51.2738,44.2683 L 51.2738,44.368 L 51.2824,44.4674 L 51.2995,44.5657 L 51.3249,44.6621 L 51.3586,44.756 L 51.4002,44.8466 L 51.4494,44.9333 L 51.506,45.0155 L 51.5693,45.0925 L 51.6391,45.1638 L 51.7147,45.2288 L 51.7957,45.2871 L 51.8813,45.3382 L 51.971,45.3817 L 52.0642,45.4174 L 52.16,45.445 L 52.2579,45.4641 L 52.357,45.4749 L 52.4568,45.477 L 52.5563,45.4706 L 52.6549,45.4556 L 52.7518,45.4322 L 52.8464,45.4006 L 52.9379,45.3609 L 53.0257,45.3136 L 53.1091,45.2588 L 53.1874,45.1971 L 53.2602,45.1289 L 53.3268,45.0547 L 53.3868,44.975 L 53.4398,44.8905 L 53.4852,44.8017 L 53.5229,44.7094 L 53.5525,44.6142 L 53.5738,44.5167 L 53.5866,44.4178 L 53.5909,44.3182 L 52.4318,44.3182 Z' style='fill:#000000;fill-rule:evenodd;fill-opacity:1;stroke:#000000;stroke-width:0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1;' />
</svg>
 [C] OKTJSMMVPCPJKNUHFFFAOYSAN 0.000 title claims abstract description 150
 239000003245 coal Substances 0.000 title claims abstract description 150
 238000003062 neural network model Methods 0.000 claims abstract description 13
 238000004458 analytical methods Methods 0.000 claims abstract description 12
 210000002569 neurons Anatomy 0.000 claims description 29
 238000010606 normalization Methods 0.000 claims description 29
 239000000284 extract Substances 0.000 claims description 24
 238000000034 method Methods 0.000 claims description 22
 238000010219 correlation analysis Methods 0.000 claims description 17
 238000004364 calculation method Methods 0.000 claims description 8
 238000004422 calculation algorithm Methods 0.000 claims description 6
 230000005284 excitation Effects 0.000 claims description 2
 238000005553 drilling Methods 0.000 claims 1
 230000000694 effects Effects 0.000 abstract description 12
 230000001537 neural Effects 0.000 abstract description 12
 280000398338 Seismic companies 0.000 abstract 7
 230000002596 correlated Effects 0.000 abstract 1
 238000004450 types of analysis Methods 0.000 abstract 1
 238000005516 engineering process Methods 0.000 description 7
 239000000203 mixture Substances 0.000 description 7
 238000001228 spectrum Methods 0.000 description 5
 238000011161 development Methods 0.000 description 4
 239000012530 fluid Substances 0.000 description 3
 238000009114 investigational therapy Methods 0.000 description 3
 230000005012 migration Effects 0.000 description 3
 238000011160 research Methods 0.000 description 3
 230000003595 spectral Effects 0.000 description 3
 238000003860 storage Methods 0.000 description 3
 238000010521 absorption reaction Methods 0.000 description 2
 230000000875 corresponding Effects 0.000 description 2
 238000009826 distribution Methods 0.000 description 2
 238000000605 extraction Methods 0.000 description 2
 238000000611 regression analysis Methods 0.000 description 2
 239000011435 rock Substances 0.000 description 2
 238000004088 simulation Methods 0.000 description 2
 239000004575 stone Substances 0.000 description 2
 241001269238 Data Species 0.000 description 1
 201000004569 blindness Diseases 0.000 description 1
 238000006243 chemical reaction Methods 0.000 description 1
 238000010835 comparative analysis Methods 0.000 description 1
 150000001875 compounds Chemical class 0.000 description 1
 238000004590 computer program Methods 0.000 description 1
 238000001514 detection method Methods 0.000 description 1
 238000011156 evaluation Methods 0.000 description 1
 238000001914 filtration Methods 0.000 description 1
 230000003993 interaction Effects 0.000 description 1
 238000004519 manufacturing process Methods 0.000 description 1
 238000003012 network analysis Methods 0.000 description 1
 238000011002 quantification Methods 0.000 description 1
 230000011514 reflex Effects 0.000 description 1
 230000004044 response Effects 0.000 description 1
 230000035945 sensitivity Effects 0.000 description 1
 230000001629 suppression Effects 0.000 description 1
 230000000007 visual effect Effects 0.000 description 1
Abstract
The embodiment of the invention provides a method for applying seismic multiattribute parameters to predicting the coal seam thickness. The method comprises: a suitable time window is selected in a threedimensional offset data body, seismic attribute data of amplitude, frequency, and instantaneity and the like are extracted from the time window, and a seismic attribute database is established; a correlated analysis is executed on seismic attributes and coal seam thicknesses and crosscorrelation analyses are further executed on the seismic attributes, so that a plurality of seismic attributes that are most meaningful are optimized as basic parameters of a coal seam thickness prediction model; with combination of known boring data, a multicomponent polynomial regression model and a BP artificial neural network model of between all the seismic attributes and the coal seam thicknesses are established by utilizing a multicomponent polynomial regression method and a BP artificial neural network method; and the models are utilized to predict coal seam thicknesses. According to the method provided in the embodiment of the invention, because multiattribute parameters are considered, obtained calculating models are perfect and realistic; an effect for prediction of the coal seam thickness is good; and credibility and accuracy are high.
Description
Technical field
The present invention relates to seismic data Processing and Interpretation Technology in the seismic prospecting, relate to a kind of method of application of seismic multiattribute parameter prediction thickness of coal seam particularly.
Background technology
In the coal field of seismic exploration, except will finding out in the exploiting field structure, the situation of change of thickness of coal seam to be provided also.Along with the development of combining the technology of adopting, the situation of change that coal is thick has become urgent problem, because most of coal seam belongs to typical thin layer, vertical resolution does not reach and solves the thick requirement of coal.How utilizing earthquake information, accurately obtain thickness of coal seam information in conjunction with borehole data, is the problem that current domestic and international many scholars are studying.
How to come evaluation of thinbed thickness to receive the attention of Chinese scholars, and carried out many theoretic discussions always, delivered the correlative study paper by the thin bed reflections ripple.Ricker (1953) has proposed to differentiate standard" the thunder gram standard " on stratum from the angle of resolution.This standard is, if this layer then can not be differentiated less than quarterwave the time difference of the reflection wave of rock stratum upper and lower interface in time domain, only is used as a face and treats.Widess (1973) proposes according to the relation of book layer thickness and seismic reflection response; When thickness of thin layer less than four of seismic event predominant wavelength/for the moment; Earthquake wave amplitude and thickness of thin layer are approximated to direct ratio; Broken through pure method of geometry first and asked for the boundary of reflector thickness,, provided the concrete definition of thin layer quantification from kinetic character.Ruter and Schepers (1978), Koefoed and Voogd (1980) draw through synthetic earthquake model investigation, exist almost relation (quasiLinerarity) between the compound wave amplitude of thickness of thin layer and seismic reflection.In China; The coalfield seismologist is theoretical according to the thin layer of Widess, adopts amplitude of vibration method to carry out the Coal Seam Thickness Change trend study, especially in the later stage eighties; Coal seam on probation reflection wave comprehensive characteristics parameter (comprising amplitude, energy, energy ratio) is carried out the thickness of coal seam estimation, has obtained certain progress.Qi Jinghua (1996) has drawn the expression formula of utilizing spectral amplitude ratio and the direct inverting thickness of coal seam of spectral amplitude duplicate ratio through theoretical analysis and model test.But because amplitude, energy represent all is reflection wave intensity, thus usually receive that the field excites, the influence of the thick factor of noncoal in reception and the Data Processing process.Cause the result of calculation dispersivity bigger, therefore, no matter home and overseas does not all also have the thick method of Inversion Calculation coal a kind of practicality, true than Huaihe River at present.
Seismic properties has been reacted geometry, kinematics, dynamics and the statistics characteristic of seismic waveshape, the seismic properties technology be through applied research, algorithm development and integrated software system extract, storage, visual, analyze, checking and estimate the technology of seismic properties.The seismic properties technology is applied to seismic interpretation processing, seismotectonics drawing, seismic stratigraphic interpretation, seismic lithology and various aspects such as simulation, reservoir description and simulation.Since the nineties in 20th century; The seismic properties technology is calculated from the single track instantaneous attribute; Develop into multiple tracks window when layer is got and calculate tens kinds of parameters, can comparatively accurately confirm position and looks such as the equal characteristic of wateroil interface, lithological change, variation in thickness, Crack Detection and earthquake.Seismic properties is becoming the key component of reservoir geophysics, and between prospecting seismology and production seismology, has set up a kind of special contact.Application of seismic attribute prediction thickness of thin layer comprises two aspects: one is the extraction of thin layer seismic properties; One is the relation research of thickness of thin layer and these attributes.The method of research roughly has two types: one type is to be utilized in the tuning thickness, and amplitude and thickness of thin layer are approximate linear; One type is to utilize spectral amplitude to come the forecasting coal layer thickness.
The inventor finds that there is following deficiency at least in prior art in realizing process of the present invention: these methods are owing to the use single parameter, and the effect on amplitude factor is a lot, shakes the multisolution of information insuperably, and effect is unsatisfactory.Although what have has used the multiattribute prediction; Just extract seismic properties with theoretical and model investigation achievement; Major side focuses on oil and gas reservoir prediction Study of Recognition Method; With one or more computing method predicting oil reservoir information, do not combine the actual attribute that carries out of study area preferred, because the sensitivity of array mode between various seismic properties information and various attribute reflection thickness has very big uncertainty; In different regions, different layers position seismic properties combination exists than big difference, and the confidence level of prediction is reduced.
Summary of the invention
The objective of the invention is to; A kind of method of application of seismic multiattribute parameter prediction thickness of coal seam is provided; Through application of seismic multiattribute parameters analysis method, therefrom optimize the most significant seismic properties as the thick forecast model basic parameter of coal, in conjunction with known borehole data; Set up the forecast model between seismic properties and the thickness of coal seam, in coal resources exploration and exploitation, utilize the earthquake multiattribute that thickness of coal seam is carried out highprecision forecast.
The embodiment of the invention provides a kind of method of application of seismic multiattribute parameter prediction thickness of coal seam, comprising: from seismic data, extract i seismic properties; Wherein, i is a positive integer; A said i seismic properties is carried out normalization to be handled; From known borehole data, extract the thickness of coal seam value; Said thickness of coal seam value is carried out normalization to be handled; I seismic properties after normalization handled and the thickness of coal seam value after the normalization processing are carried out correlation analysis, generate i related coefficient; From a said i seismic properties, extract j seismic properties, the absolute value of the pairing j of a said j seismic properties related coefficient is greater than the first preset correlation coefficient threshold; Wherein j is the positive integer less than i; A said j seismic properties is carried out crosscorrelation analysis, generate j
^{2}Individual crosscorrelation coefficient; From a said j seismic properties, extract k seismic properties; Wherein, K is the positive integer less than j; The absolute value of k related coefficient of a said k seismic properties and said thickness of coal seam value is greater than the second preset correlation coefficient threshold, and the said second preset correlation coefficient threshold is greater than the said first preset correlation coefficient threshold, and in the said k seismic properties crosscorrelation coefficient between any two seismic properties less than the crosscorrelation coefficient threshold value of presetting; Extract the thickness of coal seam value according to a said k seismic properties with from known borehole data, set up the thickness of coal seam forecast model; Utilize said thickness of coal seam forecast model, predict the thickness of coal seam of actual drill hole.
The method of the embodiment of the invention is owing to considered the multiattribute parameter simultaneously; Correlation analysis and crosscorrelation analysis have been carried out; Attribute is screened, found out the seismic properties that is closely related with thickness of coal seam, thereby the thickness of coal seam forecast model that draws is more perfect, more approaching reality; The better effects if of forecasting coal layer thickness, confidence level and accuracy are higher.
Description of drawings
In order to be illustrated more clearly in the embodiment of the invention or technical scheme of the prior art; To do one to the accompanying drawing of required use in embodiment or the description of the Prior Art below introduces simply; Obviously, the accompanying drawing in describing below only is some embodiments of the present invention, for those of ordinary skills; Under the prerequisite of not paying creative work property, can also obtain other accompanying drawing according to these accompanying drawings.
Fig. 1 is a thickness of coal seam tuning curve in the embodiment of the invention;
Fig. 2 is the overall flow figure of the method for the embodiment of the invention;
Fig. 3 is the version of BP neural network thickness of coal seam forecast model in the embodiment of the invention;
Fig. 4 is 131 thickness of coal seam prediction distribution figure in Huainan Pan Dong test site in the embodiment of the invention.
Embodiment
For the purpose, technical scheme and the advantage that make the embodiment of the invention clearer; To combine the accompanying drawing in the embodiment of the invention below; Technical scheme in the embodiment of the invention is carried out clear, intactly description; Obviously, described embodiment is the present invention's part embodiment, rather than whole embodiment.Based on the embodiment among the present invention, those of ordinary skills are not making the every other embodiment that is obtained under the creative work prerequisite, all belong to the scope of the present invention's protection.
The embodiment of the invention provides a kind of method of application of seismic multiattribute parameter highprecision forecast thickness of coal seam, with the thickness of coal seam problem in solving coal resources exploration and developing.The method of the embodiment of the invention is the PAL module of application examples such as Landmark company at first preferably, in the threeD migration data volume, chooses suitable time window, therefrom extracts seismic properties data such as amplitude class, frequency class, instantaneous class; Set up the seismic properties database; Then, carry out the correlation analysis of seismic properties and thickness of coal seam, and further these seismic properties are carried out crosscorrelation analysis; Therefrom optimize the basic parameter of the most significant a plurality of seismic properties as the thick forecast model of coal; Then, in conjunction with known borehole data, utilize multinomial homing method and/or BP Artificial Neural Network; Set up each seismic properties and coal multinomial regression model and the BP artificial nerve network model between thick; Further, the embodiment of the invention is also carried out error analysis to abovementioned thickness of coal seam forecast model so that prediction is smart further improves.The method of the embodiment of the invention has been owing to considered the multiattribute parameter simultaneously, thus the computation model that draws is more perfect, more near actual, the better effects if of forecasting coal layer thickness, confidence level and accuracy are higher.
The amplitude frequency characteristic of coal seam complex wave is below described.The coal seam is as " thin layer " (H≤λ/4) that define usually in the seismic prospecting, and its reflection wave is a coal seam roof and floor boundary reflection, and there is tuning point in coefficient stack complex waves such as interformational multiples and transformed wave with the synthetic reflection wave of the variation of thickness of coal seam.In viscoelastic body, the amplitude frequency characteristic of coal seam complex wave is:
R in the formula (1): the upper and lower reflection coefficient in coal seam; D: thickness of coal seam; β=2ad (a is the attenuation by absorption factor in coal seam), H (f) is an amplitudefrequency.
Fig. 1 is the thickness of coal seam tuning curve, and A representes reflection coefficient in Fig. 1, and is as shown in Figure 1: exist under the situation of thin layer, when ripple impinged perpendicularly on the thin layer surface, reflection coefficient is not only relevant with the wave impedance on both sides, interface, and was also relevant with the incident wave frequency.Thereby thin layer can regard a wave filter as, and incident wave on the thin layer surface reflex time takes place, and through a wave filter, has stood certain frequency filtering effect.The effect of thin bed reflections stack is that the composition to low frequency and high frequency has suppression, and the intermediate frequency composition of the reflection wave that receives is strengthened relatively.
Fig. 2 is the overall flow figure of the method for the embodiment of the invention.As shown in Figure 2, this method comprises the steps:
Step 100, from seismic data, extract i seismic properties; Wherein, i is a positive integer.
Particularly, the method for the embodiment of the invention is the PAL module of application examples such as Landmark company preferably, in the threeD migration data volume, chooses suitable time window, therefrom extracts seismic properties data such as amplitude class, frequency class, instantaneous class, sets up the seismic properties database.
The sorting technique of seismic properties has a lot, mainly contains following 4 kinds: first kind is in the comparatively popular sorting technique of academia, promptly from kinematics and dynamic (dynamical) angle, seismic properties is divided into several big type of amplitude, frequency, phase place, energy, waveform and ratio etc.; Second kind be the method for picking up by attribute with seismic properties be divided into layer bit attribute and the time two types of window attributes sorting technique; The third is the sorting technique that seismic properties is divided into 4 types of time, amplitude, frequency and decay that is proposed by Alistair R.Brown 1996; The 4th kind is the sorting technique based on reservoir characteristic that is proposed by Quincy Chen et al.1997, and this method helps the preferred seismic properties of object that will study according to institute, with the blindness and the randomness of minimizing property calculation.In embodiments of the present invention, be the sorting technique that adopts abovementioned first kind of seismic properties.
In the embodiment of the invention,, extract amplitude class, complex seismic trace statistics class, frequency spectrum statistics generic attribute according to the theory and the model investigation achievement of abovementioned first method.Wherein:
The amplitude generic attribute comprises 15 kinds: RMS amplitude, average absolute amplitude, passages, average peak amplitude, maximum valley amplitude, average valley amplitude, maximum absolute amplitude, absolute amplitude total amount, amplitude total amount, average energy, energy are overall, average amplitude, amplitude variations, the asymmetry of amplitude variations, the kurtosis of amplitude.Seismic amplitude or energy properties have reflected that wave impedance is poor, zone thickness, rock composition, reservoir pressure, factor of porosity and contain the variation of fluid composition.Both can be used to discern amplitude anomaly or sequence characteristic, also can be used to follow the trail of stratigraphy characteristic such as delta watercourse or sandstone.In addition, gathering that also can be used for discerning lithological change, unconformability, gas and fluid etc.
Complex seismic trace statistics generic attribute comprises 5 kinds: average reflection intensity, average instantaneous frequency, average instantaneous phase, reflection strength slope, instantaneous frequency slope.Complex seismic trace is actual to be the Hilbert conversion of seismic signal.It can help characteristic, lithology, river course and delta sandstone, reefs, unconformity surface, bed succession, crack, tuning effect of analytical gas, fluid etc.
Frequently (ability) spectrum statistics generic attribute comprises 6 kinds: effective bandwidth, arc length, average zero point of crossing frequency, dominant frequency sequence F1, F2, F3, dominant frequency peak value, dominant frequency peak value are to the slope of maximum frequency.It is to the frequency spectrum of seismic signal and energy spectrum, can disclose the wavelet that cranny development band, gassiness uptake zone, tuning effect, lithology or the absorption of stratum or oil gas effect cause and change.
After having extracted abovementioned multiple seismic properties, also need select or screen abovementioned seismic properties.Particularly, for each seismic trace, the characteristic parameter that extracts through said method will reach tens of kinds, that is to say to have very high feature space dimension.For the target of prediction, be not the characteristic that each parameter in the feature space has all reflected subsurface geology truly, real information is wherein arranged, extraneous noise is also arranged; Also exist correlativity between the various parameters simultaneously; Make the information redundance in the parameter space increase; Therefore must screen many reference amounts; So that optimize to finding the solution problem the most responsive (or the most effectively, the most representative), minimum seismic properties or the seismic properties combination of attribute number, improve the earthquake prediction precision, improve the processing relevant and the effect of interpretation procedure with seismic properties.
In embodiments of the present invention, the process of from a property set, picking out the attribute set that helps the thickness of coal seam earthquake prediction most is called attribute and selects.Below describe the embodiment of the invention in detail and carry out the processing procedure that seismic properties is selected.
Suppose each seismic trace has been extracted n characteristic, that is:
X＝(X
_{1}，X
_{2}，…，X
_{n})
^{T} (2)
Wherein, X
_{n}Be the data message of n seismic trace, select n characteristic Y
_{1}, Y
_{2}..., Y
_{n}, Y is meant a certain track data information in n the seismic trace, must satisfy linear behavio(u)r, irrelevance and variance maximality.
Abovementioned linear behavio(u)r is meant that each characteristic is the linear combination of original each characteristic, promptly satisfies:
Perhaps, Y
_{i}=(α
_{i})
^{T}X, wherein α
_{i}=(α
_{I1}, α
_{I2}..., α
_{In})
^{T}I=1,2 ..., n
Abovementioned irrelevance is meant each variable Y
_{i}(i=1,2 ... be incoherent n), promptly related coefficient is zero or approaches 0, satisfies following formula:
r(Y
_{i}，Y
_{j})＝0 i，j＝1，2，.…n，i≠j (4)
Abovementioned variance maximality is to instigate the difference between each parameter maximum, i.e. α
_{1}Should make Y
_{1}Variance reach maximum, α
_{2}Should make Y
_{2}Reach maximum.
Satisfy the characteristic Y of above three conditions
_{1}, Y
_{2}...., Y
_{n}Be called respectively with the target of prediction linear dependence, with n characteristic information of characteristic attribute linear independence.The embodiment of the invention has reached minimizing characteristic number through the screening process of abovementioned seismic properties, and the compressive features space dimensionality is given prominence to otherness, selects the purpose of susceptibility parameter.
Step 102, a said i seismic properties is carried out normalization handle.
Step 104, from known borehole data, extract the thickness of coal seam value.
Step 106, said thickness of coal seam value is carried out normalization handle.
Wherein, the normalization processing procedure is specific as follows in step 102 or the step 106:
The thickness of coal seam of the other seismologic record of well and the seismic properties data of extraction are carried out the normalization processing, and its method is: establishing sample data is x
_{p}(p=1,2 ..., P), the maximal value x in the definition sample data
_{Max}=max{x
_{p}, the minimum value x in the sample data
_{Min}=min{x
_{p}, have
Promptly handle to calculate by formula (5) normalization, sample data is converted into 0～1 interval data, a is a correction factor in the formula.
Step 108, i seismic properties after normalization handled and the thickness of coal seam value after the normalization processing are carried out correlation analysis, generate i related coefficient.
Particularly, the data after handling according to normalization are calculated related coefficient between the thick and seismic properties of coal according to formula (6), select with the thick related coefficient of coal greatlyyer, form the seismic properties collection that supplies the followup thick forecast model of coal that needs to set up to use.
In the formula (6), r is a related coefficient; x
_{i}Be i seismic properties value;
Be seismic properties mean value; y
_{i}Be i the thick value of coal;
Be the thick mean value of coal.Wherein, the thick value of coal obtains from known borehole data.
Step 110, from a said i seismic properties, extract j seismic properties, the absolute value of the pairing j of a said j seismic properties related coefficient is greater than first correlation coefficient threshold of presetting; Wherein j is the positive integer less than i.
Step 112, a said j seismic properties is carried out crosscorrelation analysis, generate j
^{2}Individual crosscorrelation coefficient.
Step 114, from a said j seismic properties, extract k seismic properties; Wherein, K is the positive integer less than j; The absolute value of k related coefficient of a said k seismic properties and said thickness of coal seam value is greater than the second preset correlation coefficient threshold, and the said second preset correlation coefficient threshold is greater than the said first preset correlation coefficient threshold, and in the said k seismic properties crosscorrelation coefficient between any two seismic properties less than the crosscorrelation coefficient threshold value of presetting.
In step 112step 114, further carry out attributive analysis based on simple crosscorrelation.In order to improve confidence level, to carrying out crosscorrelation analysis with the bigger seismic properties of the thick related coefficient of coal, the seismic properties that related coefficient is bigger merges, and has relative independentability to guarantee the seismic properties that is used to predict.If the attribute that related coefficient is very big returns, the stability of meeting impact prediction algorithm.The computing formula of simple crosscorrelation such as formula (7) are identical.
In the formula (7), r is a related coefficient; x
_{i}I seismic properties value for a kind of seismic properties;
Be a kind of seismic properties mean value; z
_{i}I seismic properties value for another kind of seismic properties;
Mean value for another kind of seismic properties.
In another optional embodiment, all right through type (8) of the embodiment of the invention and formula (9) are carried out preferred further to the seismic properties that formula (6) chooses.
At first, ask the related coefficient of equation of linear regression between thick and each seismic properties of coal, utilize the least square square law to ask the error between thickness of coal seam and each the seismic properties equation of linear regression.
Wherein
is the forecasting coal layer thickness; X is the seismic properties value, and a, b are regression coefficient.
Then, utilize formula (9) to calculate the thick and seismic properties coefficient R of coal
^{2}
Wherein, n is meant the borehole data number of participating in calculating, Y
_{i}For the coal of known boring is thick,
Be the thick value of coal that calculates according to formula (8).
Step 116, extract the thickness of coal seam value, set up the thickness of coal seam forecast model according to a said k seismic properties with from known borehole data.
Step 118, utilize said thickness of coal seam forecast model, predict the thickness of coal seam of actual drill hole.
Below specify the process of setting up the thickness of coal seam forecast model in the step 116.Can or utilize BP artificial neural network analysis method through multiple regression analysis.
Wherein, through the multiple regression analysis method, the process of multivariate regression model of setting up the forecasting coal layer thickness is following.
According to the thickness of coal seam and preferred seismic properties value of the other seismologic record of well, to carry out normalization and handle, its principle is: establishing sample data is x
_{p}(p=1,2 ..., P), definition x
_{Max}=max{x
_{p}, x
_{Min}=min{x
_{p}, normalization is handled and is calculated the data that promptly by (5) formula sample data are converted into 0～1 interval.
With the property set after the top normalization, set up other seismic properties of well and the thick higher polynomial regression model of coal, suppose to have p attribute, set up the m order polynomial regression equation of thick and p the attribute of coal, promptly
Wherein:
The expression forecasting coal is thick; x
_{i}(i=1,2 ..., p) represent the value of each amplitude attribute; a
_{Ij}(i=0,1 ..., p; J=1,2 ..., m, m are sample number) the expression regression coefficient.
According to the property value of borehole data and seismic trace near well, obtain a collection of test figure: A
_{1i}, A
_{2i}..., A
_{Pi}, y
_{i}(i=1,2 ..., m), make actual tests numerical value y
_{i}Go up corresponding with formula (10)
Between residual sum of squares (RSS)
Be minimum, ask each alpha with least square method
_{Ij}Value.
Wherein, it is following to set up the detailed process of BP artificial nerve network model of forecasting coal layer thickness.
The BP neural network model has self study, selforganization, strong fault tolerance property, calculates advantages such as simple, that parallel processing speed is fast, and it can approach any Nonlinear Mapping in theory arbitrarily, therefore uses the most extensive.
The BP network is through the network output error is fed back network parameter to be revised, thereby realizes the nonlinear mapping capability of network.RobetNielson has proved that 3 layers of BP network model with 1 hidden layer can approach any continuous function effectively, promptly comprise input layer, hidden layer and output layer.Based on the study area actual conditions, the network structure of the thickness of coal seam BP neural network prediction model of foundation is as shown in Figure 3, and Fig. 3 is the structural representation of the BP neural network thickness of coal seam forecast model of the embodiment of the invention.As shown in Figure 3, this BP neural network model adopts 3 layer network structures, and with 4 nodes of preferred 4 kinds of seismic properties as the elearning input layer, the middle layer of network is 2 nodes, and output layer is 1 node, sets up thickness of coal seam BP neural network prediction model.
1, the normalization of sample data
Set up the BP neural network model, at first preferably select for use Sigmoid function
as neuronic excitation function in the network.In order to effectively utilize the characteristic of Sigmoid function, to guarantee the neuronic nonlinear interaction of network, utilize formula (5) to carry out the normalization processing for the learning sample and the output data of numeric type, the output valve of each node is 0～1.
2, utilize back propagation learning to set up the neural network model of the thick prediction of coal
Supposing to have learning sample is (x
_{1p}, x
_{2p}..., x
_{Np}t
_{p}) (p=1,2 ..., P; P is a sample number), wherein, t
_{p}Refer to p sample data.Provide W (w at random
_{Ij}, θ
_{i}, v
_{i}), wherein, w
_{Ij}Be connected power, θ for hidden layer neuron i and input layer j
_{i}Be the threshold values of hidden layer neuron i, v
_{i}Be the be connected power of output layer neuron with hidden layer neuron i, then, according to the output y of formula (12)～p sample of (14) computational grid
_{p}
Wherein, I
_{i}Be the input of i hidden layer neuron, n is the neuron number of input layer; M is the neuron number of hidden layer; w
_{Ij}Be the be connected power of hidden neuron i with input layer j; θ
_{i}Threshold values for hidden neuron i.
Wherein, I
_{i}It is the input of i hidden layer neuron; O
_{i}Be the output of i hidden layer neuron.
Wherein, v
_{i}Be the be connected power of output layer neuron with hidden layer neuron i; y
_{p}Be the output of p sample.
Definition is weighed w by hidden layer neuron i with being connected of input layer j
_{Ij}, hidden layer neuron i threshold values θ
_{i}Weigh v with output layer neuron and being connected of hidden layer neuron i
_{i}The vector of forming is the connection weight vector W of network.
For sample p, the output error of define grid is:
And the definition error function is:
Along error function e
_{p}Negative gradient direction with W changes is revised W.If the modified value of W is Δ W, get
In the formula: η is a learning rate, gets the number between 0～1, and η is the actual value of calculating according to real data.
After trying to achieve Δ W, adopt iterative: W+ Δ W → W (18)
Former W is carried out corrected Calculation, obtain new connection weight vector W.
For all learning samples, all put in order and carry out abovementioned computation process, then the fixing value of W according to sample.P sample carried out forward calculating respectively, thereby obtains the energy function value of learning sample:
Through iterating, network is connected power W revise, make E reach predefined precision.
3, model error analysis
In the following formula, E is a standard error of estimate, and its value heals the bright institute of novel established model better; R is the coefficient of determination, and the model that the bigger explanation of its value is set up better.Make E, R reach certain accuracy requirement, make standard error of estimate less than presetting first threshold; Make the coefficient of determination greater than the second preset threshold value, this first threshold and second threshold value for example are 1, preferably, make E, R approach 1.
The beneficial effect of the embodiment of the invention is: the method for the embodiment of the invention is through choosing suitable time window in the threeD migration data volume; Therefrom extract seismic properties data such as amplitude class, frequency class, instantaneous class; Set up the seismic properties database; These attributes are done the thick correlation analysis of autocorrelation analysis and attribute and coal, therefrom optimize the basic parameter of the most significant seismic properties, in conjunction with known borehole data as the thick forecast model of coal; Application of seismic multiattribute analytical approach; Utilize multinomial to return and the BP Artificial Neural Network, set up each attribute and coal multinomial regression model and the artificial nerve network model between thick, more perfect, the more approaching reality of the computation model that draws.Show through typical coal resources exploiting fields such as East China, south China and North China are used: its error is less than 10%; Satisfy the coal resources development requires fully, the method for the application of seismic multiattribute parameter highprecision forecast thickness of coal seam of the embodiment of the invention has solved the problem of the highprecision forecast thickness of coal seam in coal resources exploration and the exploitation.
Below through a concrete example technique scheme of the embodiment of the invention is described.The following example of takeing is to be example with bottom, exploiting field, Huainan field Pan Dong Xisi exploration test site.
Local area top layer seismogeology is general, and the deep seismic geologic condition is better.Fundamental purpose layer 131 thickness of coal seam is bigger, and composes and to deposit stablely, and the roof and floor lithology in these coal seams is main with mud stone, chiltern mud stone and sandstone, with the bigger physical difference of coal seam existence itself, has good reflecting interface.
1, seismic properties is extracted and related coefficient calculating
The embodiment of the invention adopts the PAL property extracting module of the Landmark Poststack of company, and window is as the time window that extracts attributive analysis when confirming respectively along the 131 coal seam equal 26ms.In the window, extract 42 kinds of seismic properties altogether at this moment, wherein the amplitude generic attribute is 16 kinds, 5 kinds of complex seismic trace generic attributes, and (ability) spectrum statistics generic attribute is 8 kinds frequently, and the sequence statistics comprises 7 kinds of attributes, and ASSOCIATE STATISTICS comprises 6 kinds of association attributeses.According to the known borehole data in exploration test site, bottom, exploiting field, Huainan field Pan Dong Xisi; It is thick as shown in table 1 with related coefficient seismic properties to adopt least square method for example to calculate the coal of drill hole, and table 1 is the correlation coefficient charts of Huainan Pan Dong test site thickness of coal seam and attribute.
Table 1
2, seismic properties is preferred
For the 131 coal seam; The absolute value that from table 1, optimizes related coefficient is greater than 10 kinds of 0.35 seismic properties, that is: arc length, average absolute amplitude, average peak amplitude, average reflection intensity, average valley amplitude, dominant frequency sequence 1, maximum absolute amplitude, passages, maximum valley amplitude and dominant frequency peak value.
For the relative independentability that guarantees each attribute and the stability of algorithm, utilize formula (7) to carry out the crosscorrelation analysis of seismic properties, as shown in table 2, table 2 is the preferred 10 kinds of seismic properties cross correlation numerical tables in Pan 13 coal seams, east.Also calculate the facies relationship between thick of seismic properties and coal simultaneously, seen also table 2 again.
Table 2
According to the crosscorrelation coefficient between each seismic properties; And with reference to seismic properties and coal the related coefficient between thick; Reject crosscorrelation coefficient between seismic properties bigger and seismic properties and coal thick between the less seismic properties of related coefficient, can set corresponding compare threshold.Through the crosscorrelation analysis of seismic properties, obtain the basic parameter of 4 useful seismic properties at last as forecast model.They are respectively for the 131 coal seam: arc length, dominant frequency sequence 1, maximum absolute amplitude, four kinds of attributes of dominant frequency peak value.With the basic parameter of abovementioned these seismic properties as regression model and BP neural network prediction model.
3, set up the thickness of coal seam forecast model
3.1, set up the multivariate statistics forecast model
According to 131 coal seam, Pan Dong test site, Huainan actual observation point data, be the basis with the seismic properties collection after the normalization, set up seismic properties and the coal multinomial regression model between thick respectively as follows:
Set up polynomial regression model of quaternary: through selecting to carry out correlation analysis between arc length, dominant frequency sequence 1, maximum absolute amplitude, four kinds of attributes of dominant frequency peak value and the thickness of coal seam, polynomial regression model of quaternary of calculating acquisition is:
y＝0.0534656x
_{1}+0.000247426x
_{2}0.0363759x
_{3}0.0140472x
_{4}+5.90867 (22)
In the formula (22), y is the thick value of coal (m) of prediction; x
_{1}Be arc length; x
_{2}Be dominant frequency F1; x
_{3}Be maximum absolute amplitude; x
_{4}Be the dominant frequency peak value.
Set up the regression model of quaternary quadratic polynomial: the regression model of the quaternary quadratic polynomial that obtains through the mathematical regression analytical calculation is:
(23)
3.2, set up the BP neural network prediction model
Utilize back propagation learning to set up the neural network model of the thick prediction of coal; According to 131 coal seam, Pan Dong test site, Huainan actual observation point data; Filter out 13,14 and 19 measured datas respectively as learning training and test sample book;, as learning sample network is trained with boring point seismic properties.
Through iteration, weight coefficient W between input layer and hidden layer and the weight coefficient V between hidden layer and output layer are respectively:
The hidden layer weight coefficient:
The output layer weight coefficient:
V＝[2.52863410472906 29.8586552283255 27.8594790230161] (25)
4, the thickness of coal seam error analysis that predicts the outcome
The reliability that predicts the outcome for further testing model; With BP artificial neural network and polynomial regression model exploration test site, bottom, exploiting field, Pan Dong Xisi, Huainan 131 thickness of coal seam is carried out forecast analysis and check; It is as shown in table 3 to predict the outcome, and table 3 is a 131 thickness of coal seam predicated error statistical form in the embodiment of the invention.Can find out according to model predication value and measured value and error comparative analysis thereof: use once relatively large with quadratic polynomial forecast of regression model thickness of coal seam error; Although once identical fine in some known point data with the quadratic polynomial regression model, it is thick to be not useable for whole study area forecasting coal; But BP artificial nerve network model forecasting coal layer thickness data can be applicable to whole study area, remove nonvalue point, and nearly all data are all available, and error is also less, and precision is high, explains with the Neural Network model predictive thickness of coal seam the most stable.It is as shown in Figure 4 to predict the outcome based on the thickness of coal seam of BP neural network prediction model, and Fig. 4 is a Huainan Pan Dong test site 131 thickness of coal seam prediction distribution figure (unit among the figure: m) in the embodiment of the invention.
Table 3
One of ordinary skill in the art will appreciate that all or part of flow process that realizes in the foregoing description method; Be to instruct relevant hardware to accomplish through computer program; Described program can be stored in the computer read/write memory medium; This program can comprise the flow process like the embodiment of abovementioned each side method when carrying out.Wherein, described storage medium can be magnetic disc, CD, readonly storage memory body (ReadOnly Memory, ROM) or at random store memory body (Random AccessMemory, RAM) etc.
Above embodiment is only in order to the technical scheme of the explanation embodiment of the invention, but not to its restriction; Although the embodiment of the invention has been carried out detailed explanation with reference to previous embodiment; Those of ordinary skill in the art is to be understood that: it still can be made amendment to the technical scheme that aforementioned each embodiment put down in writing, and perhaps part technical characterictic wherein is equal to replacement; And these are revised or replacement, do not make the spirit and the scope of each embodiment technical scheme of the essence disengaging embodiment of the invention of relevant art scheme.
Claims (7)
1. the method for an application of seismic multiattribute parameter prediction thickness of coal seam is characterized in that said method comprises:
From seismic data, extract i seismic properties; Wherein, i is a positive integer;
A said i seismic properties is carried out normalization to be handled;
From known borehole data, extract the thickness of coal seam value;
Said thickness of coal seam value is carried out normalization to be handled;
I seismic properties after normalization handled and the thickness of coal seam value after the normalization processing are carried out correlation analysis, generate i related coefficient;
From a said i seismic properties, extract j seismic properties, the absolute value of the pairing j of a said j seismic properties related coefficient is greater than the first preset correlation coefficient threshold; Wherein j is the positive integer less than i;
A said j seismic properties is carried out crosscorrelation analysis, generate j
^{2}Individual crosscorrelation coefficient;
From a said j seismic properties, extract k seismic properties; Wherein, K is the positive integer less than j; The absolute value of k related coefficient of a said k seismic properties and said thickness of coal seam value is greater than the second preset correlation coefficient threshold, and the said second preset correlation coefficient threshold is greater than the said first preset correlation coefficient threshold, and in the said k seismic properties crosscorrelation coefficient between any two seismic properties less than the crosscorrelation coefficient threshold value of presetting;
Extract the thickness of coal seam value according to a said k seismic properties with from known borehole data, set up the thickness of coal seam forecast model;
Utilize said thickness of coal seam forecast model, predict the thickness of coal seam of actual drill hole.
2. method according to claim 1 is characterized in that, saidly a said i seismic properties is carried out normalization handles and to be based on following algorithm:
Wherein, x
_{p}Be sample data, p=1,2 ..., P, x
_{Max}=max{x
_{p}Be the maximal value in the sample data, x
_{Min}=min{x
_{p}Be the minimum value in the sample data, a is a correction factor.
3. method according to claim 1 is characterized in that, said i seismic properties and the thickness of coal seam value after the normalization processing after normalization is handled carried out correlation analysis, generates i related coefficient and is based on following algorithm:
Wherein, r is a related coefficient, x
_{i}Be i seismic properties value;
Be seismic properties mean value, y
_{i}Be i thickness of coal seam value,
Mean value for thickness of coal seam.
4. method according to claim 1 is characterized in that, said a said j seismic properties is carried out crosscorrelation analysis, generates j
^{2}Individual crosscorrelation coefficient is based on following algorithm:
Wherein, r is a related coefficient, x
_{i}Be a kind of i seismic properties value of seismic properties,
Be a kind of seismic properties mean value, z
_{i}Be i seismic properties value of another kind of seismic properties,
Mean value for another kind of seismic properties.
5. method according to claim 1 is characterized in that, said thickness of coal seam forecast model is repeatedly a polynomial regression model, and according to extracting the thickness of coal seam value in a said k seismic properties and the known borehole data, setting up repeatedly, polynomial regression model comprises:
Thickness of coal seam in the known drilling data and a said k seismic properties are carried out the normalization processing respectively;
Set up k the m order polynomial regression equation between the seismic properties of stating after thickness of coal seam and the normalization processing after normalization is handled; Said equation is:
, wherein,
The thickness of coal seam value of expression prediction, x
_{i}(i=1 ..., k) be each value in the said k seismic properties, a
_{Ij}(i=1 ..., k; J=1,2 ..., m) be regression coefficient;
According to a known borehole data and a said k seismic properties, obtain test figure: A
_{1i}, A
_{2i}..., A
_{Pi}, y
_{i}(i=1,2 ..., m), make actual tests numerical value y
_{i}With accordingly
Between residual sum of squares (RSS)
Be minimum, ask each coefficient a with least square method
_{Ij}Value.
6. method according to claim 1 is characterized in that, said thickness of coal seam forecast model is the BP neural network model, according to extracting the thickness of coal seam value in a said k seismic properties and the known borehole data, sets up the BP neural network model and comprises:
Select the neuronic excitation function of Sigmoid function
for use as the BP neural network model;
Learning sample and output data for numeric type are carried out the normalization processing, and the output valve that makes each node is 0～1;
Supposing to have learning sample is (x
_{1p}, x
_{2p}..., x
_{Np}t
_{p}) (p=1,2 ..., P; P is a sample number), wherein, t
_{p}Refer to p sample data, provide W (w at random
_{Ij}, θ
_{i}, v
_{i}), wherein, w
_{Ij}Be connected power, θ for hidden layer neuron i and input layer j
_{i}Be the threshold values of hidden layer neuron i, v
_{i}Be the be connected power of output layer neuron, calculate the output y of p sample of BP neural network model with hidden layer neuron i
_{p}Said calculating y
_{p}Process be based on following formula:
Wherein, I
_{i}Be the input of i hidden layer neuron, n is the neuron number of input layer, and m is the neuron number of hidden layer; And,
Wherein, O
_{i}Be the output of i hidden layer neuron; And,
Definition is weighed w by hidden layer neuron i with being connected of input layer j
_{Ij}, hidden layer neuron i threshold values θ
_{i}Weigh v with output layer neuron and being connected of hidden layer neuron i
_{i}The vector of forming is the connection weight vector W of BP neural network model; For sample p, the output error of define grid is:
And the definition error function is:
Along error function e
_{p}Negative gradient direction with W changes is revised W, and the modified value of establishing W is Δ W, gets
Wherein, η is a learning rate, gets the number between 0～1;
After trying to achieve Δ W, adopt iterative: W+ Δ W → W carries out corrected Calculation to former W, obtains connecting weight vector W ';
For all learning samples, all put in order and carry out abovementioned computation process, then the fixing value of W ' according to sample;
P sample carried out forward calculating respectively, thereby obtain the energy function value
of learning sample
Through iterating, the connection weight vector W of BP neural network model is revised, make E reach predefined precision.
7. according to claim 5 or 6 described methods, it is characterized in that said method also comprises: said thickness of coal seam forecast model is carried out error analysis; Saidly said thickness of coal seam forecast model carried out error analysis comprise:
Basis of calculation evaluated error makes said accurate evaluated error less than presetting first threshold;
Calculate the coefficient of determination, make the said coefficient of determination greater than the second preset threshold value.
Priority Applications (1)
Application Number  Priority Date  Filing Date  Title 

CN2010105682723A CN102478668A (en)  20101130  20101130  Method for applying seismic multiattribute parameters to predicting coal seam thickness 
Applications Claiming Priority (1)
Application Number  Priority Date  Filing Date  Title 

CN2010105682723A CN102478668A (en)  20101130  20101130  Method for applying seismic multiattribute parameters to predicting coal seam thickness 
Publications (1)
Publication Number  Publication Date 

CN102478668A true CN102478668A (en)  20120530 
Family
ID=46091375
Family Applications (1)
Application Number  Title  Priority Date  Filing Date 

CN2010105682723A Pending CN102478668A (en)  20101130  20101130  Method for applying seismic multiattribute parameters to predicting coal seam thickness 
Country Status (1)
Country  Link 

CN (1)  CN102478668A (en) 
Cited By (18)
Publication number  Priority date  Publication date  Assignee  Title 

CN104142516A (en) *  20131028  20141112  中国石油化工股份有限公司  Method for predicting thickness of thin single sand bed 
CN104280773A (en) *  20130712  20150114  中国石油天然气集团公司  Method for predicting thin layer thickness by utilization of timefrequency spectrum cross plot changing along with geophone offsets 
CN105277979A (en) *  20151016  20160127  中国石油天然气集团公司  Seismic attribute optimization method and device 
CN105319582A (en) *  20140731  20160210  中国石油天然气股份有限公司  Method and device for selection of seismic attribute parameters 
US20160086079A1 (en) *  20140602  20160324  Westerngeco Llc  Properties link for simultaneous joint inversion 
CN105572737A (en) *  20160126  20160511  电子科技大学  Earthquake attribute analysis method based on fractional domain saliency detection 
CN105956662A (en) *  20160420  20160921  东南大学  Underground structure deformation prediction method based on BPregression analysis prediction model 
CN106199725A (en) *  20160816  20161207  中国石油化工股份有限公司  A kind of coal petrography thickness prediction method and device based on positive amplitude summation attribute 
CN106772598A (en) *  20161212  20170531  中国石油大学(华东)  Using the method for receiver function periodic measurement sedimentary formation time thickness 
CN107102379A (en) *  20160219  20170829  师素珍  A kind of method that seat earth watery prediction is carried out based on many attribution inversions 
CN107894615A (en) *  20171113  20180410  中国石油化工股份有限公司华北油气分公司勘探开发研究院  A kind of method of quantitative evaluation 3D seismics attribute forecast reservoir parameter validity 
CN108333629A (en) *  20180124  20180727  中国矿业大学  A method of it is thick using empirical mode decomposition and support vector machines quantitative forecast coal 
CN109345007A (en) *  20180913  20190215  中国石油大学(华东)  A kind of Favorable Reservoir development area prediction technique based on XGBoost feature selecting 
CN109633743A (en) *  20190117  20190416  中国矿业大学  A method of based on waveform separation seismic facies technological prediction coal seam thickness 
CN109709607A (en) *  20181207  20190503  中国石油天然气股份有限公司  A kind of prediction thin sandstone reservoirs thickness approach and device 
CN110191999A (en) *  20170206  20190830  哈里伯顿能源服务公司  Multilayer groundbed frontier distance (DTBB) inverting carried out with multiple initial guess 
CN111045080A (en) *  20191225  20200421  安徽省煤田地质局勘查研究院  Coal bed gas content prediction method based on PSOBP model and seismic attribute parameters 
CN111221038A (en) *  20181126  20200602  中国石油天然气股份有限公司  Method and device for quantitatively predicting thickness of thin reservoir 
Citations (7)
Publication number  Priority date  Publication date  Assignee  Title 

US5444619A (en) *  19930927  19950822  Schlumberger Technology Corporation  System and method of predicting reservoir properties 
CN1504762A (en) *  20021205  20040616  大庆石油管理局  Dual nerval net container rock prediction method 
CN1975462A (en) *  20060905  20070606  孟召平  Coal seam thickness analyzing method based on earthquake attribute 
CN101126815A (en) *  20060817  20080220  中国石油天然气股份有限公司  Method for oil gas detection using lithologic seismic factor and lithologic resistance 
CN101149439A (en) *  20071113  20080326  符力耘  High resolution ratio nonlinear reservoir properties inversion method 
CN102053270A (en) *  20091030  20110511  中国石油化工股份有限公司  Sedimentary formation unitbased seismic facies analysis method 
JP2011148929A (en) *  20100122  20110804  Nippon Steel Corp  Method for estimating the amount of raised dust of coal to be charged, method for estimating the thickness of deposited carbon, and method for operating chambertype coke oven 

2010
 20101130 CN CN2010105682723A patent/CN102478668A/en active Pending
Patent Citations (7)
Publication number  Priority date  Publication date  Assignee  Title 

US5444619A (en) *  19930927  19950822  Schlumberger Technology Corporation  System and method of predicting reservoir properties 
CN1504762A (en) *  20021205  20040616  大庆石油管理局  Dual nerval net container rock prediction method 
CN101126815A (en) *  20060817  20080220  中国石油天然气股份有限公司  Method for oil gas detection using lithologic seismic factor and lithologic resistance 
CN1975462A (en) *  20060905  20070606  孟召平  Coal seam thickness analyzing method based on earthquake attribute 
CN101149439A (en) *  20071113  20080326  符力耘  High resolution ratio nonlinear reservoir properties inversion method 
CN102053270A (en) *  20091030  20110511  中国石油化工股份有限公司  Sedimentary formation unitbased seismic facies analysis method 
JP2011148929A (en) *  20100122  20110804  Nippon Steel Corp  Method for estimating the amount of raised dust of coal to be charged, method for estimating the thickness of deposited carbon, and method for operating chambertype coke oven 
NonPatent Citations (4)
Title 

孟召平 等: "基于地震属性的煤层厚度预测模型及其应用", 《地球物理学报》, vol. 49, no. 2, 30 March 2006 (20060330) * 
胡宗正 等: "三维地震属性参数在煤层厚度预测中的应用", 《中国煤炭地质》, vol. 20, no. 6, 25 June 2008 (20080625) * 
郭彦省等: "地震属性及其在煤层厚度预测中的应用", 《中国矿业大学学报》, vol. 33, no. 05, 30 September 2004 (20040930) * 
靳吉祥: "基于地震属性多元回归分析的煤层厚度预测方法研究", 《太原理工大学硕士学位论文》, 15 October 2010 (20101015) * 
Cited By (23)
Publication number  Priority date  Publication date  Assignee  Title 

CN104280773A (en) *  20130712  20150114  中国石油天然气集团公司  Method for predicting thin layer thickness by utilization of timefrequency spectrum cross plot changing along with geophone offsets 
CN104280773B (en) *  20130712  20170405  中国石油天然气集团公司  Using the timefrequency spectrum changed with geophone offset cross figure predict thickness of thin layer method 
CN104142516A (en) *  20131028  20141112  中国石油化工股份有限公司  Method for predicting thickness of thin single sand bed 
US20160086079A1 (en) *  20140602  20160324  Westerngeco Llc  Properties link for simultaneous joint inversion 
US9852373B2 (en) *  20140602  20171226  Westerngeco L.L.C.  Properties link for simultaneous joint inversion 
CN105319582A (en) *  20140731  20160210  中国石油天然气股份有限公司  Method and device for selection of seismic attribute parameters 
CN105277979A (en) *  20151016  20160127  中国石油天然气集团公司  Seismic attribute optimization method and device 
CN105572737B (en) *  20160126  20180515  电子科技大学  A kind of seismic attributes analysis method based on the detection of score field conspicuousness 
CN105572737A (en) *  20160126  20160511  电子科技大学  Earthquake attribute analysis method based on fractional domain saliency detection 
CN107102379A (en) *  20160219  20170829  师素珍  A kind of method that seat earth watery prediction is carried out based on many attribution inversions 
CN105956662A (en) *  20160420  20160921  东南大学  Underground structure deformation prediction method based on BPregression analysis prediction model 
CN106199725A (en) *  20160816  20161207  中国石油化工股份有限公司  A kind of coal petrography thickness prediction method and device based on positive amplitude summation attribute 
CN106772598A (en) *  20161212  20170531  中国石油大学(华东)  Using the method for receiver function periodic measurement sedimentary formation time thickness 
CN110191999A (en) *  20170206  20190830  哈里伯顿能源服务公司  Multilayer groundbed frontier distance (DTBB) inverting carried out with multiple initial guess 
CN107894615B (en) *  20171113  20190618  中国石油化工股份有限公司华北油气分公司勘探开发研究院  A kind of method of quantitative evaluation 3D seismics attribute forecast reservoir parameter validity 
CN107894615A (en) *  20171113  20180410  中国石油化工股份有限公司华北油气分公司勘探开发研究院  A kind of method of quantitative evaluation 3D seismics attribute forecast reservoir parameter validity 
CN108333629A (en) *  20180124  20180727  中国矿业大学  A method of it is thick using empirical mode decomposition and support vector machines quantitative forecast coal 
CN109345007A (en) *  20180913  20190215  中国石油大学(华东)  A kind of Favorable Reservoir development area prediction technique based on XGBoost feature selecting 
CN109345007B (en) *  20180913  20210604  中国石油大学(华东)  Advantageous reservoir development area prediction method based on XGboost feature selection 
CN111221038A (en) *  20181126  20200602  中国石油天然气股份有限公司  Method and device for quantitatively predicting thickness of thin reservoir 
CN109709607A (en) *  20181207  20190503  中国石油天然气股份有限公司  A kind of prediction thin sandstone reservoirs thickness approach and device 
CN109633743A (en) *  20190117  20190416  中国矿业大学  A method of based on waveform separation seismic facies technological prediction coal seam thickness 
CN111045080A (en) *  20191225  20200421  安徽省煤田地质局勘查研究院  Coal bed gas content prediction method based on PSOBP model and seismic attribute parameters 
Similar Documents
Publication  Publication Date  Title 

CN102478668A (en)  Method for applying seismic multiattribute parameters to predicting coal seam thickness  
CN101738639B (en)  Method for improving computing precision of rock fracture parameters  
CN104502997B (en)  A kind of method of utilization fracture spacing curve prediction fracture spacing body  
CN101281253B (en)  Method for enhancing oil gas detecting accuracy using vibration amplitude with offset distance variation characteristic  
CN103163553B (en)  Based on earthquake detecting method of hydrocarbon and the device of multiple pore medium model  
CN102736107B (en)  Energy constraint heterogeneous reservoir thickness identification system  
US8498177B2 (en)  Determining a position of a geological layer relative to a wavelet response in seismic data  
CN103675907A (en)  AVO inversion hydrocarbon detection method based on petrographic constraints  
CN1975462A (en)  Coal seam thickness analyzing method based on earthquake attribute  
CN103792573B (en)  A kind of seismic impedance inversion based on frequency spectrum fusion  
CN104698492A (en)  Abnormal formation pressure calculation method  
CN104570101A (en)  AVO (amplitude versus offset) threeparameter inversion method based on particle swarm optimization  
GB2473251A (en)  Method of Assessing Hydrocarbon Source Rock Candidate  
CN105301644A (en)  Oil/gas detection method based on multiparameter gradient vector and sea color matrix and device  
CN104632202A (en)  Method and device for determining dry clay threeporosity logging parameter values  
Dezfoolian et al.  Conversion of 3D seismic attributes to reservoir hydraulic flow units using a neural network approach: An example from the Kangan and Dalan carbonate reservoirs, the world's largest nonassociated gas reservoirs, near the Persian Gulf  
Pafeng et al.  Prestack waveform inversion of threedimensional seismic data—An example from the Rock Springs Uplift, Wyoming, USA  
CN101937101A (en)  Method for identifying whether timelapse seism is implemented or not  
Raptakis et al.  Multiple estimates of soil structure at a vertical strong motion array: Understanding uncertainties from different shear wave velocity profiles  
CN105259576A (en)  Hydrocarbon reservoir identification method by means of seismic statistical characteristic  
CN105242307A (en)  Complex carbonate stratum earthquake porosity obtaining method and apparatus  
CN102253414A (en)  Reservoir detecting method based on analysis of earthquake lines  
Adabnezhad et al.  Threedimensional modeling of geomechanical units using acoustic impedance in one of the gas fields in South of Iran  
CN104516021A (en)  Ray elastic parameter inversion method capable of improving stability and precision of analysis formula  
Pallavika et al.  Finite difference modeling of SHwave propagation in multilayered porous crust 
Legal Events
Date  Code  Title  Description 

PB01  Publication  
C06  Publication  
SE01  Entry into force of request for substantive examination  
C10  Entry into substantive examination  
WD01  Invention patent application deemed withdrawn after publication 
Application publication date: 20120530 

C02  Deemed withdrawal of patent application after publication (patent law 2001) 