{"id":61041,"date":"2023-06-20T05:27:08","date_gmt":"2023-06-19T21:27:08","guid":{"rendered":"http:\/\/learncmg.cn\/?p=61041"},"modified":"2023-06-20T05:27:08","modified_gmt":"2023-06-19T21:27:08","slug":"312-%e5%9f%ba%e4%ba%8e%e7%94%9f%e4%ba%a7%e6%95%b0%e6%8d%ae%e7%9a%84%e5%8f%82%e6%95%b0%e5%8c%96%e5%8a%a0%e9%80%9f%e7%a2%b3%e9%85%b8%e7%9b%90%e5%b2%a9%e5%82%a8%e5%b1%82co2%e6%b7%b7%e6%ba%b6%e6%b0%b4","status":"publish","type":"post","link":"http:\/\/learncmg.cn\/?p=61041","title":{"rendered":"312. \u57fa\u4e8e\u751f\u4ea7\u6570\u636e\u53c2\u6570\u5316\u7684\u78b3\u9178\u76d0\u5ca9\u50a8\u5c42CO2\u6df7\u76f8\u6c14\u6c34\u4ea4\u66ff\u6ce8\u5165\u4f18\u5316"},"content":{"rendered":"<p><span style=\"color: #000000;\">Enhancing oil recovery in reservoirs with light oil and high gas content relies on optimizing the miscible water alternating gas (WAG) injection profile.However, this can be costly and time-consuming due to computationally demanding compositional simulation models and numerous other well control variables. This study introduces WAG<sub>eq<\/sub>, a novel approach that expedites the convergence of the optimization algorithm for miscible water alternating gas (WAG) injection in carbonate reservoirs. The WAG<sub>eq<\/sub>\u00a0leverages production data to create flexible solutions that maximize the net present value (NPV) of the field, while providing practical implementation of individual WAG profiles for each injector. The WAG<sub>eq<\/sub>\u00a0utilizes an injection priority index to rank the wells and determine which should inject water or gas at each time interval. The index is built using a parametric equation that considers factors such as producer and injector relationship, water cut (<em>W<\/em><sub>CUT<\/sub>), gas\u2013oil ratio (GOR), and wells cumulative gas production, to induce desirable effects on production and WAG profile. To evaluate WAG<sub>eq<\/sub>\u2019s effectiveness, two other approaches were compared: a benchmark solution named WAG<sub>bm<\/sub>, in which the injected fluid is optimized for each well over time, and a traditional baseline strategy with fixed 6-month WAG cycles. The procedures were applied to a synthetic simulation case (SEC1_2022) with characteristics of a Brazilian pre-salt carbonate field with karstic formations and high CO<sub>2<\/sub>\u00a0content. The WAG<sub>eq<\/sub>\u00a0outperformed the baseline procedure, improving the NPV by 6.7% or 511 USD million. Moreover, WAG<sub>eq<\/sub>\u00a0required fewer simulations (less than 350) than WAG<sub>bm<\/sub>\u00a0(up to 2000), while delivering a slightly higher NPV. The terms of the equation were also found to be essential for producing a WAG profile with regular patterns on each injector, resulting in a more practical solution. In conclusion, WAG<sub>eq<\/sub>\u00a0significantly reduces computational requirements while creating consistent patterns across injectors, which are crucial factors to consider when planning a practical WAG strategy.<\/span><\/p>\n<h4><\/h4>\n<p><span style=\"color: #000000;\">\u5728\u8f7b\u8d28\u6cb9\u548c\u9ad8\u542b\u6c14\u6cb9\u85cf\u4e2d\u63d0\u9ad8\u91c7\u6536\u7387\u4f9d\u8d56\u4e8e\u4f18\u5316\u6df7\u76f8\u6c34\u4ea4\u66ff\u6c14\uff08WAG\uff09\u6ce8\u5165\u5256\u9762\u3002\u7136\u800c\uff0c\u7531\u4e8e\u8ba1\u7b97\u8981\u6c42\u9ad8\u7684\u6210\u5206\u6a21\u62df\u6a21\u578b\u548c\u8bb8\u591a\u5176\u4ed6\u4e95\u63a7\u53d8\u91cf\uff0c\u8fd9\u53ef\u80fd\u662f\u6602\u8d35\u548c\u8017\u65f6\u7684\u3002\u672c\u7814\u7a76\u4ecb\u7ecd\u4e86WAGeq\uff0c\u8fd9\u662f\u4e00\u79cd\u52a0\u5feb\u4e86\u78b3\u9178\u76d0\u5ca9\u50a8\u5c42\u6df7\u76f8\u6c34\u4ea4\u66ff\u6c14\uff08WAG\uff09\u6ce8\u5165\u4f18\u5316\u7b97\u6cd5\u6536\u655b\u7684\u65b0\u65b9\u6cd5\u3002WAGeq\u5229\u7528\u751f\u4ea7\u6570\u636e\u521b\u5efa\u7075\u6d3b\u7684\u89e3\u51b3\u65b9\u6848\uff0c\u6700\u5927\u9650\u5ea6\u5730\u63d0\u9ad8\u6cb9\u7530\u7684\u51c0\u73b0\u503c\uff08NPV\uff09\uff0c\u540c\u65f6\u4e3a\u6bcf\u4e2a\u6ce8\u5165\u4e95\u63d0\u4f9b\u5355\u72ecWAG\u5256\u9762\u7684\u5b9e\u9645\u5b9e\u65bd\u3002WAGeq\u5229\u7528\u6ce8\u5165\u4f18\u5148\u7ea7\u6307\u6570\u5bf9\u6cb9\u4e95\u8fdb\u884c\u6392\u5e8f\uff0c\u5e76\u786e\u5b9a\u6bcf\u4e2a\u65f6\u95f4\u95f4\u9694\u5e94\u6ce8\u5165\u6c34\u6216\u5929\u7136\u6c14\u7684\u6cb9\u4e95\u3002\u8be5\u6307\u6570\u662f\u4f7f\u7528\u4e00\u4e2a\u53c2\u6570\u65b9\u7a0b\u5efa\u7acb\u7684\uff0c\u8be5\u65b9\u7a0b\u8003\u8651\u4e86\u751f\u4ea7\u4e95\u548c\u6ce8\u5165\u4e95\u5173\u7cfb\u3001\u542b\u6c34\u7387\uff08WCUT\uff09\u3001\u6c14\u6cb9\u6bd4\uff08GOR\uff09\u548c\u6cb9\u4e95\u7d2f\u8ba1\u5929\u7136\u6c14\u4ea7\u91cf\u7b49\u56e0\u7d20\uff0c\u4ee5\u5bf9\u4ea7\u91cf\u548cWAG\u5256\u9762\u4ea7\u751f\u7406\u60f3\u7684\u5f71\u54cd\u3002\u4e3a\u4e86\u8bc4\u4f30WAGeq\u7684\u6709\u6548\u6027\uff0c\u6bd4\u8f83\u4e86\u53e6\u5916\u4e24\u79cd\u65b9\u6cd5\uff1a\u4e00\u79cd\u662f\u540d\u4e3aWAGbm\u7684\u57fa\u51c6\u89e3\u51b3\u65b9\u6848\uff0c\u5176\u4e2d\u968f\u7740\u65f6\u95f4\u7684\u63a8\u79fb\uff0c\u6bcf\u53e3\u4e95\u7684\u6ce8\u5165\u6d41\u4f53\u90fd\u4f1a\u5f97\u5230\u4f18\u5316\uff0c\u53e6\u4e00\u79cd\u662f\u56fa\u5b9a6\u4e2a\u6708WAG\u5468\u671f\u7684\u4f20\u7edf\u57fa\u7ebf\u7b56\u7565\u3002\u8be5\u7a0b\u5e8f\u5e94\u7528\u4e8e\u4e00\u4e2a\u5177\u6709\u5df4\u897f\u76d0\u4e0a\u78b3\u9178\u76d0\u5ca9\u6cb9\u7530\u7279\u5f81\u7684\u5408\u6210\u6a21\u62df\u6848\u4f8b\uff08SEC1_2022\uff09\uff0c\u8be5\u78b3\u9178\u76d0\u5ca9\u6cb9\u7530\u5177\u6709\u5ca9\u6eb6\u5730\u5c42\u548c\u9ad8CO2\u542b\u91cf\u3002WAGeq\u4f18\u4e8e\u57fa\u7ebf\u7a0b\u5e8f\uff0c\u5c06\u51c0\u73b0\u503c\u63d0\u9ad8\u4e866.7%\uff0c\u53735.11\u4ebf\u7f8e\u5143\u3002\u6b64\u5916\uff0c\u4e0eWAGbm\uff08\u9ad8\u8fbe2000\uff09\u76f8\u6bd4\uff0cWAGeq\u9700\u8981\u66f4\u5c11\u7684\u6a21\u62df\uff08\u5c11\u4e8e350\uff09\uff0c\u540c\u65f6\u63d0\u4f9b\u7565\u9ad8\u7684NPV\u3002\u8be5\u65b9\u7a0b\u7684\u9879\u4e5f\u88ab\u53d1\u73b0\u5bf9\u4e8e\u5728\u6bcf\u4e2a\u6ce8\u5165\u4e95\u4e0a\u4ea7\u751f\u5177\u6709\u89c4\u5219\u56fe\u6848\u7684WAG\u8f6e\u5ed3\u81f3\u5173\u91cd\u8981\uff0c\u4ece\u800c\u4ea7\u751f\u66f4\u5b9e\u7528\u7684\u89e3\u51b3\u65b9\u6848\u3002\u603b\u4e4b\uff0cWAGeq\u663e\u8457\u964d\u4f4e\u4e86\u8ba1\u7b97\u9700\u6c42\uff0c\u540c\u65f6\u5728\u6ce8\u5165\u4e95\u4e4b\u95f4\u521b\u5efa\u4e86\u4e00\u81f4\u7684\u6a21\u5f0f\uff0c\u8fd9\u662f\u89c4\u5212\u5b9e\u9645WAG\u7b56\u7565\u65f6\u9700\u8981\u8003\u8651\u7684\u5173\u952e\u56e0\u7d20\u3002<\/span><\/p>\n<h4><\/h4>\n<h3 style=\"text-align: center;\"><a href=\"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13202-023-01643-0.pdf?pdf=button%20sticky\">Accelerated optimization of CO<\/a><sub><a href=\"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13202-023-01643-0.pdf?pdf=button%20sticky\">2<\/a><\/sub><a href=\"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13202-023-01643-0.pdf?pdf=button%20sticky\">-miscible water-alternating-gas injection in carbonate reservoirs using production data-based parameterization<\/a><\/h3>\n<h4>Abstract<\/h4>\n<p><img class=\"wp-image-61042\" src=\"http:\/\/learncmg.cn\/wp-content\/uploads\/2023\/06\/word-image-61041-1.png\" \/><br \/>\n<img loading=\"lazy\" class=\"wp-image-61043 aligncenter\" src=\"http:\/\/learncmg.cn\/wp-content\/uploads\/2023\/06\/word-image-61041-2.png\" width=\"580\" height=\"586\" \/><br \/>\n<img loading=\"lazy\" class=\"wp-image-61044 aligncenter\" src=\"http:\/\/learncmg.cn\/wp-content\/uploads\/2023\/06\/word-image-61041-3.png\" width=\"560\" height=\"876\" \/><br \/>\n<img loading=\"lazy\" class=\"wp-image-61045 aligncenter\" src=\"http:\/\/learncmg.cn\/wp-content\/uploads\/2023\/06\/word-image-61041-4.png\" width=\"798\" height=\"496\" \/><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Enhancing oil recovery in reservoirs with light oi<span class=\"more-link\"><a href=\"http:\/\/learncmg.cn\/?p=61041\">Continue 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