{"id":60111,"date":"2023-04-27T05:00:31","date_gmt":"2023-04-26T21:00:31","guid":{"rendered":"http:\/\/learncmg.cn\/?p=60111"},"modified":"2023-04-27T05:00:31","modified_gmt":"2023-04-26T21:00:31","slug":"276-%e4%ba%ba%e5%b7%a5%e7%a5%9e%e7%bb%8f%e7%bd%91%e7%bb%9c%e5%92%8c%e6%a2%af%e5%ba%a6%e9%81%97%e4%bc%a0%e7%ae%97%e6%b3%95%e4%bc%98%e5%8c%96%e7%a2%b3%e9%85%b8%e7%9b%90%e5%b2%a9%e6%b2%b9%e8%97%8f","status":"publish","type":"post","link":"http:\/\/learncmg.cn\/?p=60111","title":{"rendered":"276. \u4eba\u5de5\u795e\u7ecf\u7f51\u7edc\u548c\u68af\u5ea6\u9057\u4f20\u7b97\u6cd5\u4f18\u5316\u78b3\u9178\u76d0\u5ca9\u6cb9\u85cf\u5de5\u7a0b\u6ce8\u6c34\u79bb\u5b50\u6d53\u5ea6"},"content":{"rendered":"

Optimization of ionic concentrations in engineered water injection in carbonate reservoir through ANN and FGA<\/a><\/h3>\n
\u6458\u8981<\/span><\/h5>\n

\u6d3b\u6027\u6ce8\u5165\uff08EWI\uff09\u5df2\u88ab\u8d8a\u6765\u8d8a\u591a\u5730\u6d4b\u8bd5\u548c\u5e94\u7528\u4e8e\u63d0\u9ad8\u6cb9\u85cf\u4e2d\u7684\u6d41\u4f53\u9a71\u66ff\u6548\u679c\u3002\u901a\u8fc7\u6539\u53d8\u79bb\u5b50\u6d53\u5ea6\u6765\u4e0e\u5b54\u58c1\u53d1\u751f\u76f8\u4e92\u4f5c\u7528\uff0c\u4ece\u800c\u4fc3\u8fdb\u4e86\u6cb9\u7684\u6d41\u52a8\u6027\u3002\u5728\u78b3\u9178\u76d0\u5ca9\u4e2d\uff0c\u8fd9\u79cd\u673a\u5236\u6539\u53d8\u4e86\u5929\u7136\u5ca9\u77f3\u7684\u6da6\u6e7f\u6027\uff0c\u6210\u4e3a\u4e00\u79cd\u76f8\u5f53\u5177\u6709\u5438\u5f15\u529b\u7684\u91c7\u6536\u65b9\u6cd5\u3002<\/span>
\n\u76ee\u524d\uff0c\u57fa\u4e8e\u5b9e\u9a8c\u6570\u636e\u7684\u7b80\u5316\u6a21\u578b\u4ecd\u9650\u5236\u4e86\u8fd9\u79cd\u6ce8\u5165\u65b9\u6cd5\u7684\u6570\u503c\u6a21\u62df\u3002\u56e0\u6b64\uff0c\u672c\u7814\u7a76\u5229\u7528\u4eba\u5de5\u795e\u7ecf\u7f51\u7edc\uff08ANN\uff09\u7684\u5b66\u4e60\u80fd\u529b\u5c06\u79bb\u5b50\u7ec4\u5408\u4e0e\u76f8\u5bf9\u6e17\u900f\u7387\uff08Kr\uff09\u4e4b\u95f4\u7684\u5206\u6790\u5173\u8054\u8fdb\u884c\u6574\u5408\uff0c\u4ee5\u63cf\u8ff0\u6da6\u6e7f\u6027\u6539\u53d8\u3002<\/span>
\n\u4f18\u5316\u5df4\u897f\u76d0\u4e0b\u57fa\u51c6\u6a21\u578b\u6ce8\u5165\u7cfb\u7edf\u4e2d\u7684\u79bb\u5b50\u7ec4\u6210\uff0c\u4ee5\u6700\u5927\u5316\u6cb9\u7530\u7684\u51c0\u73b0\u503c\uff08NPV\uff09\u3002\u4f18\u5316\u7ed3\u679c\u8868\u660e\uff0c\u5bf9\u4e8e\u6d4b\u8bd5\u7684\u6848\u4f8b\u6765\u8bf4\uff0cEWI\u662f\u6700\u6709\u5229\u53ef\u56fe\u7684\u65b9\u6cd5\u3002\u4e0e\u5e38\u89c4\u7684\u6c34\u6ce8\u5165\u76f8\u6bd4\uff0cEWI\u8fd8\u4f7f\u6cb9\u7684\u91c7\u6536\u91cf\u589e\u52a0\u4e86\u7ea68.7%\uff0c\u5e76\u4e14\u51cf\u5c11\u4e86\u7ea652%\u7684\u7d2f\u79ef\u6c34\u751f\u4ea7\u91cf\u3002<\/span><\/p>\n

\u65b9\u6cd5\u8bba<\/span><\/h5>\n

\u7531\u4e8e\u8fd9\u9879\u5de5\u4f5c\u4ec5\u5728\u8ba1\u7b97\u73af\u5883\u4e2d\u8fdb\u884c\uff0c\u56e0\u6b64\u9700\u8981\u4f7f\u7528\u5408\u6210\u4fe1\u606f\u6765\u6784\u5efa\u672a\u6765\u795e\u7ecf\u7f51\u7edc\u8bad\u7ec3\u7684\u6570\u636e\u5e93\u3002\u56e0\u6b64\uff0c\u9009\u62e9\u4e86CMG\u63d0\u4f9b\u7684\u4e00\u4e9b\u6a21\u677f\u3002\u6211\u4eec\u4f7f\u7528EWI\u5730\u7403\u5316\u5b66\u6a21\u62df\u4e0e\u4f20\u7edf\u8f6f\u4ef6\uff08\u7ec4\u5206\u6a21\u62df\u5668\uff09\u6a21\u62df\u4e86\u8fd9\u4e9b\u6a21\u677f\uff0c\u5bf9\u6bcf\u79cd\u60c5\u51b5\u8fdb\u884c\u4e86\u4e0d\u540c\u77ff\u5316\u5ea6\u7684\u6a21\u62df\u3002<\/span>
\n\u4e4b\u540e\uff0c\u4e00\u4e2a\u7279\u5b9a\u7684\u7b97\u6cd5\uff08Kr-Module\uff09\u8c03\u6574\u4e86\u4e00\u4e2a\u5e38\u89c4\u6a21\u677f\u7684\u521d\u59cb\u76f8\u5bf9\u6e17\u900f\u66f2\u7ebf\uff0c\u76f4\u5230\u83b7\u5f97\u4e0e\u5730\u7403\u5316\u5b66\u6a21\u62df\u76f8\u540c\u7684\u751f\u4ea7\u6a21\u62df\u3002\u751f\u6210\u7684\u6570\u636e\u5e93\u7531\u539f\u59cbKr\u548c\u76f8\u5e94\u7684\u77ff\u5316\u5ea6\u5ea6\u6848\u4f8b\u4f5c\u4e3a\u8f93\u5165\uff0c\u4ee5\u53ca\u65b0\u53d1\u73b0\u7684Kr\u66f2\u7ebf\u4f5c\u4e3a\u8f93\u51fa\uff0c\u5f00\u59cb\u7f51\u7edc\u5f00\u53d1\u3002<\/span>
\n\u5728\u57f9\u8bad\u9636\u6bb5\uff0c\u6211\u4eec\u6d4b\u8bd5\u4e86\u4e0d\u540c\u7684\u7f51\u7edc\u6a21\u5f0f\uff0c\u76f4\u5230\u8fbe\u5230\u6700\u5927\u7684\u9884\u6d4b\u8d28\u91cf\uff0c\u57fa\u4e8e\u7edf\u8ba1\u5de5\u5177\u3002\u7f51\u7edc\u901a\u8fc7\u539f\u59cb\u66f2\u7ebf\u548c\u7ed9\u5b9a\u7684\u76d0\u5ea6\u63d0\u4f9b\u4e86\u4e00\u4e2a\u65b0\u7684Kr\u66f2\u7ebf\u3002\u7136\u540e\uff0c\u6211\u4eec\u5c06\u6700\u4f73\u7684\u795e\u7ecf\u7f51\u7edc\u4e0e\u6a21\u62df\u8026\u5408\uff0c\u901a\u8fc7\u6bd4\u8f83\u5730\u7403\u5316\u5b66\u6a21\u62df\uff08\u5728CMG\u8f6f\u4ef6\u4e2d\u6a21\u62df\uff09\u548cANN\u4e4b\u95f4\u7684\u751f\u4ea7\u7ed3\u679c\u6765\u8fdb\u884c\u9a8c\u8bc1\u3002<\/span><\/p>\n

Abstract.<\/span><\/h4>\n

Engineered Water Injection (EWI) has been increasingly tested and applied to enhance fluid displacement in reservoirs. The modification of ionic concentration provides interactions with the pore wall, which facilitates the oil mobility. This mechanism in carbonates alters the natural rock wettability being quite an attractive recovery method. Currently, numerical simulation with this injection method remains limited to simplified models based on experimental data. Therefore, this study uses Artificial Neural Networks (ANN) learnability to incorporate the analytical correlation between the ionic combination and the relative permeability (Kr), which depicts the wettability alteration.<\/span>
\nThe ionic composition in the injection system of a Brazilian Pre-Salt benchmark is optimized to maximize the Net Present Value (NPV) of the field. The optimization results indicate the EWI to be the most profitable method for the cases tested. EWI also increased oil recovery by about 8.7% with the same injected amount and reduced the accumulated water production around 52%, compared to the common water injection.<\/span><\/p>\n

Methodology<\/span><\/h4>\n

As this work was performed only in a computational environment, it was necessary to use synthetic information to compose the database for future neural network training; therefore, some templates provided by CMG were selected. We simulated these templates using EWI geochemical modeling with different salinity for each case through conventional software (compositional simulator). After that, a specific algorithm (Kr-Module) adjusted the initial relative permeability curves of a regular template until obtaining the same production simulation with geochemical modeling. The database generated was composed of the original Kr and corresponding salinity case as input and the newfound Kr curve as output, starting the network development. At the training stage, we tested different network patterns up to the maximum forecasting quality, based on statistical tools. The network provided a new Kr curve through the original curve and a given salinity. Then, we coupled the best neural network to the simulation, performing the validation by comparing the production outcome between the geochemical simulation (modeled in the CMG software) and the ANN.<\/span>
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\n<\/p>\n","protected":false},"excerpt":{"rendered":"

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