|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | Genetic algorithm application for matching ordinary black oil PVT data显示文摘In the study of reservoirs, it is vital that we have a realistic physical model of the reservoir fluid that accurately describes the hydrocarbon system and its properties. The available equations of state (EOS) to model the fluid phase behavior have some inherent deficiencies that may cause erroneous predictions for real reservoir fluids, so these models should be tuned against experimental data by adjusting some parameters. Since there are many matching parameters, tuning the EOS against experimental data is a tedious and difficult work. In this study, a genetic algorithm as an optimization technique is used to solve this regression problem. This study presents a new method that uses a specially designed genetic algorithm to search for suitable regression parameters to match the EOS against measured data. The proposed method has been tested on three real black oil samples. The results show the surprising performance of the developed genetic algorithm to match the experimental data of the selected fluid samples. The main advantage of the used method is its high speed in finding a solution. Also, finding more than one solution, working automatically, confining the role of experts to the last stage, reducing costs and having the possibility of evaluating the different situations are the other advantages of this method to match ordinary black oil PVT data and makes it an ideal method to implement as an automatic EOS tuning algorithm for black oils. | Mohammad Taghizadeh Sarvestani Behnam Sedaee Sola Fariborz Rashidi | 2012 | Petroleum Science2012，9，2： | 1 |
| 2 | Mechanistic simulation of fracture effects on miscible CO_(2)injection显示文摘Miscible injection of carbon dioxide(CO_(2))into oil reservoirs as an Enhanced Oil Recovery(EOR)method has proved to be highly advantageous.According to the volume of the world's recoverable oil resides inside the fractured reservoirs,investigation of the controlling parameters in the efficient injection of miscible CO_(2) is of paramount importance,mainly owing to the intricacies and complexities associated with this process.This complexity in fractured reservoirs arises due to the presence of two distinct media for fluid transfer(i.e.,matrix and fracture network)and the corresponding differences in fluid velocities.Accordingly,performance of miscible carbon dioxide injection in these reservoirs was investigated through mechanistic simulation model in the form of dual-porosity(DP),and dual porosity-dual permeability(DPP).Moreover,due to limited supply and high injection costs of this gas in its pure form,performance of the miscible CO_(2) injection combined with C_(1),N_(2),and H_(2)S was also surveyed and compared to pure gas injection case.A sensitivity analysis was also performed based on fracture porosity,fracture horizontal and vertical permeability,matrix horizontal permeability,block height shape factor,matrix capillary pressure,and impure injected components in DP and DPP models,showing that matrix horizontal permeability and capillary pressure have the greatest,and porosity has the lowest impact on miscibility performance and oil recovery in these models.In the end,after investigating the effect of different injection cases on miscibility performance and oil recovery,it was concluded that the highest oil recovery in miscible gas injection obtained through optimization of a gas composition having the lowest minimum miscibility pressure(MMP)and the lowest density. | Seyed Mohammad Hossein Hashemi Behnam Sedaee | 2022 | Petroleum Research2022，7，4： | 0 |
| 3 | Data driven prediction of oil reservoir fluid properties显示文摘Accuracy of the fluid property data plays an absolutely pivotal role in the reservoir computational processes.Reliable data can be obtained through various experimental methods,but these methods are very expensive and time consuming.Alternative methods are numerical models.These methods used measured experimental data to develop a representative model for predicting desired parameters.In this study,to predict saturation pressure,oil formation volume factor,and solution gas oil ratio,several Artificial Intelligent(AI)models were developed.582 reported data sets were used as data bank that covers a wide range of fluid properties.Accuracy and reliability of the model was examined by some statistical parameters such as correlation coefficient(R2),average absolute relative deviation(AARD),and root mean square error(RMSE).The results illustrated good accordance between predicted data and target values.The model was also compared with previous works and developed empirical correlations which indicated that it is more reliable than all compared models and correlations.At the end,relevancy factor was calculated for each input parameters to illustrate the impact of different parameters on the predicted values.Relevancy factor showed that in these models,solution gas oil ratio has greatest impact on both saturation pressure and oil formation volume factor.In the other hand,saturation pressure has greatest effect on solution gas oil ratio. | Kazem Monfaredi Sobhan Hatami Amirsalar manouchehri Behnam Sedaee | 2023 | Petroleum Research2023，8，3： | 0 |
您所在的机构暂未开通此部分的功能，请联系管理员