Abstract
The optimization of oil field development scheme considering the uncertainty of reservoir model is a challenging and difficult problem in reservoir engineering design. The most common method used in this regard is to generate multiple models based on statistical analysis of uncertain reservoir parameters and requires a large number of simulations to efficiently handle all uncertainties, thus requiring a huge amount of computational power. In order to reduce the computational burden, a method which combines reservoir simulation, an economic model, polynomial chaos expansion with response surface methodology, and Levy flight particle swarm optimization (LFPSO) algorithm is proposed to determine the optimal injection-production parameters with reservoir uncertainties at a reasonable computational cost. This approach is applied to a five-spot well pattern optimization design for obtaining the optimal parameters, including oil-water well distance, injection rate, and bottom hole pressure, while considering the uncertainties of porosity, permeability, and relative permeability. The results of the case study indicated that the integrated approach is practical and efficient for performing reservoir optimization with uncertain reservoir parameters.
Highlights
Liang Zhang,1,2 ZhiPing Li,1,2 Hong Li,3 Caspar Daniel Adenutsi,4 FengPeng Lai,1,2 KongJie Wang,1 and Sen Yang1
In order to reduce the computational burden, a method which combines reservoir simulation, an economic model, polynomial chaos expansion with response surface methodology, and Levy flight particle swarm optimization (LFPSO) algorithm is proposed to determine the optimal injection-production parameters with reservoir uncertainties at a reasonable computational cost. is approach is applied to a five-spot well pattern optimization design for obtaining the optimal parameters, including oil-water well distance, injection rate, and bottom hole pressure, while considering the uncertainties of porosity, permeability, and relative permeability. e results of the case study indicated that the integrated approach is practical and efficient for performing reservoir optimization with uncertain reservoir parameters
E cumulative distribution function (CDF) of net present value (NPV), which were estimated by coupling the 2nd, 3rd, and 4th order polynomial chaos expansion (PCE) with Latin hypercube sampling (LHS), are shown in Figure 5. e root mean square deviation (RMSD) between the CDFs of NPV estimated from the 2nd and 3rd order PCE was 7.23%, while based on the 3rd and 4th order PCE, the corresponding value was 3.42%, which showed that the convergence of the 3rd order PCE was satisfactory
Summary
Mahomed e optimization of oil field development scheme considering the uncertainty of reservoir model is a challenging and difficult problem in reservoir engineering design. E most common method used in this regard is to generate multiple models based on statistical analysis of uncertain reservoir parameters and requires a large number of simulations to efficiently handle all uncertainties, requiring a huge amount of computational power. In order to reduce the computational burden, a method which combines reservoir simulation, an economic model, polynomial chaos expansion with response surface methodology, and Levy flight particle swarm optimization (LFPSO) algorithm is proposed to determine the optimal injection-production parameters with reservoir uncertainties at a reasonable computational cost. A common characteristic of all these approaches is the very large number of simulations required to efficiently handle multiple reservoir models generated according to the uncertainty statistics, which will require huge computational power and slow convergence
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