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INTEGRATED RESERVOIR MODELLING WORKFLOW AND OPTIMIZATION FRAMEWORK FOR CO2 SEQUESTRATION USING WATER ALTERNATING GAS PROCESS UNDER GEOLOGICAL UNCERTAINTIES<\/a><\/h3>\n

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\u672c\u8bba\u6587\u5305\u62ec\u4ee5\u4e0b\u516d\u7ae0\uff1a<\/span><\/h4>\n

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\n\u7b2c\u4e94\u7ae0\u63cf\u8ff0\u4e86\u5728\u5730\u8d28\u4e0d\u786e\u5b9a\u6027\u4e0b\u5229\u7528\u6c34\u66ff\u6c14\u8fc7\u7a0b\u8fdb\u884c\u4e8c\u6c27\u5316\u78b3\u5c01\u5b58\u7684\u4f18\u5316\u6846\u67b6\u3002\u91c7\u7528\u5065\u58ee\u4f18\u5316\u5de5\u4f5c\u6d41\u7a0b\u786e\u5b9a\u4e86\u5728\u5730\u8d28\u4e0d\u786e\u5b9a\u6027\u4e0b\u6c34\u548c\u6c14\u6ce8\u5165\u7684\u6700\u4f73\u5468\u671f\u957f\u5ea6\u3002\u603b\u5171\u4ea7\u751f\u4e86200\u4e2a\u5730\u8d28\u5b9e\u73b0\u7684\u4e09\u7ef4\u5b54\u9699\u5ea6\u3001\u6c34\u5e73\u548c\u5782\u76f4\u6e17\u900f\u7387\u5206\u5e03\uff0c\u4ee5\u8003\u8651\u5730\u8d28\u7ea6\u675f\u3002\u8fd9\u4e00\u5de5\u4f5c\u6d41\u7a0b\u7684\u7b2c\u4e00\u6b65\u662f\u901a\u8fc7\u91cf\u5316\u4e8c\u6c27\u5316\u78b3\u7d2f\u79ef\u6ce8\u5165\u6765\u5bf9\u6240\u6709\u5b9e\u73b0\u8fdb\u884c\u6392\u540d\uff0c\u4ee5\u9009\u62e9\u4ee3\u8868\u6cb9\u85cf\u6574\u4f53\u4e0d\u786e\u5b9a\u6027\u7684P10\u3001P50\u548cP90\u3002\u4f7f\u7528CMG-GEM\u6210\u5206\u6cb9\u85cf\u6a21\u62df\u8f6f\u4ef6\u8bc4\u4f30\u4e86\u6c34\u66ff\u6c14\u8fc7\u7a0b\u3002\u5b9e\u9a8c\u8bbe\u8ba1\u521b\u5efa\u4e86250\u4e2a\u6a21\u62df\u4efb\u52a1\uff0c\u5305\u62ec\u5468\u671f\u957f\u5ea6\u548c\u5730\u8d28\u4e0d\u786e\u5b9a\u6027\u53c2\u6570\u3002\u7136\u540e\uff0c\u5229\u7528\u6210\u5206\u6cb9\u85cf\u6a21\u62df\u5668\u5bf9\u751f\u6210\u7684\u5de5\u4f5c\u8fdb\u884c\u8bc4\u4f30\uff0c\u4ee5\u8ba1\u7b9720\u5e74\u5185\u7684CO2\u5b58\u50a8\u91cf\uff0c\u4e4b\u540e\u662f40\u5e74\u7684\u6ce8\u5165\u540e\u8ddf\u968f\u5e74\u3002\u968f\u540e\uff0c\u8fdb\u884c\u4e86\u5065\u58ee\u4f18\u5316\u6d41\u7a0b\uff0c\u4ee5\u786e\u5b9a\u5728\u8003\u8651\u5b54\u9699\u5ea6\u3001\u6e17\u900f\u7387\u548c\u5404\u5411\u5f02\u6027\u6a21\u5f0f\u7684\u5730\u8d28\u4e0d\u786e\u5b9a\u6027\u4e0b\u9ad8CO2\u5c01\u5b58\u7684\u771f\u6b63\u6700\u4f73\u89e3\u3002\u4e3a\u4e86\u6bd4\u8f83\u8fdb\u884c\u4e86\u57fa\u4e8e\u5355\u4e2a\u5b9e\u73b0\u7684\u540d\u4e49\u4f18\u5316\u3002\u5728\u5730\u8d28\u4e0d\u786e\u5b9a\u6027\u4e0b\u8fdb\u884c\u7684\u5065\u58ee\u4f18\u5316\u6846\u67b6\u7ed3\u679c\u6bd4\u540d\u4e49\u5b9e\u73b0\u4f18\u5316\u7ed3\u679c\u5b9e\u73b0\u4e86\u66f4\u9ad8\u7684\u4e8c\u6c27\u5316\u78b3\u5c01\u5b58\u3002\u8be5\u7814\u7a76\u5efa\u8bae\u4e86\u4e00\u79cd\u5feb\u901f\u53ef\u9760\u7684\u5065\u58ee\u4f18\u5316\u5de5\u4f5c\u6d41\u7a0b\uff0c\u53ef\u4ee5\u4ee3\u8868\u4e3b\u8981\u53c2\u6570\u7684\u4e0d\u786e\u5b9a\u6027\uff0c\u5305\u62ec\u50a8\u5c42\u7269\u7406\u7279\u6027\u3001\u5730\u8d28\u548c\u7ecf\u6d4e\u56e0\u7d20\uff0c\u7528\u4e8e\u4e8c\u6c27\u5316\u78b3\u5c01\u5b58\u3002<\/span>
\n\u7b2c\u516d\u7ae0\u603b\u7ed3\u4e86\u672c\u7814\u7a76\u7684\u53d1\u73b0\uff0c\u5305\u62ec\u5efa\u8bae\u548c\u672a\u6765\u9879\u76ee\u7684\u53ef\u80fd\u6027\u3002<\/span><\/p>\n

Abstract<\/span><\/h3>\n

Carbon Capture and Storage (CCS) is an attractive emerging method to mitigate and slow down greenhouse gases emission. In this vein, carbon storage in deep saline aquifers is touted as the most suitable site because it is identified as the largest storage capacity. However, the construction of realistic 3D geological models for this storage formation is a significant issue. The lack of subsurface datasets in these storage sites is an obstacle to build a realistic model. Also, injection strategies are strongly influenced by CO2 sequestration efficiency. The water alternating gas (WAG) process is a common technique to improve sweep efficiency in EOR projects. This process could be a promising technique in the CCS aspect. Besides, the modelling and numerical simulation are useful tools to evaluate the reasonable 3D models and effective injection technique. However, geological uncertainties (e.g., porosity and permeability distributions) are crucial factors for modelling and reservoir simulation results.<\/span>
\nTherefore, this study was proposed a systematic workflow to integrate 3D modelling, reservoir simulation, and geological uncertainties. The new modelling framework was developed with available subsurface data. This framework could enhance the accuracy of 3D porosity and permeability models. Also, the robust optimization approach was implemented to improve CO2 trapping using the WAG process under geological uncertainties. Ultimately, the systematic workflow could increase the 90% amount of CO2 injection stored in the storage site.<\/span>
\nThis dissertation composes of six chapters as follows:<\/span>
\nChapter 1 presents the research motivation, background, and objectives, as well as the outline of the dissertation. Furthermore, this chapter introduces previous studies on Artificial Neural Networks, Geostatistical modelling, Water Alternating Gas process.<\/span>
\nChapter 2 describes the literature review of CO2 sequestration modelling, geological uncertainties, optimization under geological uncertainty, and study area. Detail information on geological modelling and reservoir simulation of CO2 sequestration was provided in this chapter. The critical role of geological risk was highlighted, focusing on geological CO2 sequestration. Also, robust optimization under geological uncertainties was introduced based on previous studies. Furthermore, the study area was presented for a better understanding of the characteristic of fluvial sandstone reservoirs in Cuu Long Basin, Vietnam. Ultimately, the available data is introduced for a better understanding of subsurface pieces of information to conduct the modelling and simulation studies.<\/span>
\nChapter 3 describes the development of an integrated geological modelling workflow. Adopting the object-based modelling, Sequential Gaussian Simulation, and Artificial Neural Network (ANN), a new modelling workflow named \u201cencapsulated framework\u201d was developed to construct the reasonable 3D porosity and permeability models in Nam Vang field. The advantages, methodology of the encapsulated framework build the models, as well as the comparison with traditional framework, were presented in this chapter. Petrel package was employed as the object-based method to construct the lithofacies models. Also, Sequential Gaussian Simulation was adapted to rank the lithofacies distribution. Then, Artificial Neural Networks was predicted the petrophysical models using seismic attributes and well log measurement. To integrating the ranking lithofacies and ANN models, the co-kriging was used to distribute the final porosity and permeability models. Also, conventional models were constructed for comparison purposes. Finally, the ECLIPSE simulator was performed the Drill Stem Test matching to evaluate the accuracy between the new and traditional models. The results of history matching indicated that the developed porosity and permeability models are better for future field development plan as well as the CO2 sequestration assessment.<\/span>
\nChapter 4 discusses the simulation workflow and results of CO2 sequestration in a fluvial sandstone reservoir. The defining problems are the first step of reservoir simulation work. Then, the dynamic datasets were collected to import in the simulator. Besides, this chapter was conducted several simulation scenarios to evaluate the impact of geological factors and injection strategies for CO2 sequestration. The sensitivity analysis was performed to determine the suitable injection rate for the project. This injection rate used throughout the work for consistent simulation results. The channel distribution and anisotropy were changed to investigate the CO2 plume migration in a fluvial sandstone reservoir. Also, the injection strategies comprise continuously, and WAG injection was compared to determine the effective injection methods for further studies. The simulation results indicated that WAG technology was enhanced the solubility and residual trapping when comparing with continuous CO2 injection. This increase was due to the migration of CO2 after injection caused by drainage and imbibition processes in porous media. Therefore, the WAG technique was suggested for optimization studies.<\/span>
\nChapter 5 describes the optimization framework of CO2 sequestration using the Water Alternating Gas process under geological uncertainties. A robust optimization workflow was used to determine the optimal cycle length of water and gas injection under geological uncertainties. A total of 200 geological realizations of the 3D porosity, horizontal and vertical permeability distributions were generated to consider the geological constraints. The first step of this workflow is to rank all realizations by quantifying CO2 cumulative injection to select P10, P50, and P90 that represent the overall uncertainty of a reservoir. The WAG process was evaluated using CMG-GEM compositional reservoir simulation software. The experimental design created 250 simulation jobs, including the cycle length and geological uncertainty parameters. Then, the generated jobs were assessed using the compositional reservoir simulator to calculate the CO2 storage amounts by the end of 20 years-injection followings by 40 post-injection years. Subsequently, the robust optimization procedure was applied to determine the true optimal solution of the high CO2 trapping by considering the geological uncertainties in porosity, permeability, and anisotropy models. The nominal optimization based on a single realization was conducted for comparison. The proposed robust optimization workflow under geological uncertainties resulted in higher CO2 trapping than the nominal realization optimization. This study suggested a fast and reliable robust optimization workflow that can represent the uncertainties of the main parameters, including petrophysical properties, geology, and economic factor, for CO2 sequestration<\/span>
\nChapter 6 summarizes the conclusions of the findings of this research, including the recommendations and the possibility of a future project.<\/span>
\n<\/span>
\n \"\u56fe\u793a\n\u63cf\u8ff0\u5df2\u81ea\u52a8\u751f\u6210\"<\/span>
\n\"\u56fe\u7247\u5305\u542b<\/span>
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