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首页> 外文期刊>Journal of Petroleum & Environmental Biotechnology >Rigorous Modeling of Solution Gas?￠????Oil Ratios for a Wide Ranges of Reservoir Fluid Properties
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Rigorous Modeling of Solution Gas?￠????Oil Ratios for a Wide Ranges of Reservoir Fluid Properties

机译：各种储层流体性质的固溶体气体比的严格建模

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摘要

The reservoir fluid properties, the solution gas–oil ratio (GOR), are of great importance in various aspects of petroleum engineering. Therefore, a rapid means for estimating such parameters is much sort after. In this study, the linear interaction and general optimization method is applied in the development of a precise and reliable model for estimating the solution GOR. In order to develop a model that would be comprehensive, a reliable and extensive databank comprising of more than 1000 datasets collected from various geographical locations, including Asia, Mediterranean Basin, North America, Africa, and Middle East was compiled. Furthermore, the model developed was benchmarked against widely-used empirical methods in order to evaluate the performance of method proposed in predicting solution GOR data. The results show that the model proposed in this study outperforms the empirical methods to which it was compared. This study also investigated the influence of the reservoir fluid properties on the estimated solution GOR for the newly- developed model. Results show that bubble point pressure and gas gravity have the largest and the smallest influences on the predicted solution GOR, respectively. Finally, the Leverage approach was applied to determine the applicability domain for the proposed method via the detection of outlier data points. It was determined that only 26 data points, out of more than 1000 data, are identified as outlier data points.
机译：在石油工程的各个方面，储层的流体性质，固溶气油比（GOR）都非常重要。因此，紧随其后的是用于估计此类参数的快速方法。在这项研究中，线性相互作用和一般优化方法被用于开发精确可靠的模型来估计解GOR。为了开发一个全面的模型，编制了一个可靠且广泛的数据库，其中包含从亚洲，地中海盆地，北美，非洲和中东等不同地理位置收集的1000多个数据集。此外，针对广泛使用的经验方法，对开发的模型进行了基准测试，以评估所提出的方法在预测解决方案GOR数据中的性能。结果表明，本研究中提出的模型优于将其进行比较的经验方法。这项研究还研究了储层流体性质对新开发模型的估计溶液GOR的影响。结果表明，气泡点压力和气体重力分别对预测解GOR的影响最大和最小。最后，通过检测离群数据点，利用杠杆方法确定所提出方法的适用范围。已确定，在1000多个数据中，只有26个数据点被标识为异常数据点。

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