Optimization of waterflooding performance in a layered reservoir using a combination of capacitance-resistive model and genetic algorithm method

Managing oil production from reservoirs to maximize the future economic return of the asset is an important issue in petroleum engineering. In many applications in reservoir modeling and management, there is a need for rapid estimation of large-scale reservoirs. The capacitance-resistive model (CRM), regarded as a promising rapid evaluator of reservoir performance, has recently been used for simulation of single-layer reservoirs. Injection and production rates are considered as input and output signals in this model. Connections between the wells and the effects of injection rates on production rates are calculated based on these signals to develop a simple model for the reservoir. In this study, CRM is improved to model a multilayer reservoir and is applied to estimate and optimize waterflooding performance in an Iranian layered reservoir. In this regard, CRM is coupled with production logging tools (PLT) data to study the effects of layers. A fractional-flow model is also coupled with the developed CRM to estimate oil production. Genetic algorithm (GA) method is used to minimize the error objective function for the total production history and oil production history to evaluate model parameters. GA is then used to maximize oil production by reallocating the injected water volumes, which is the main purpose of this research. The results show that our fast method is able to model liquid and oil production history and is in good agreement with available field data. Taking into account the reservoir constraints, the optimal injection schemes have been obtained. For the proposed injection profile, the field hydrocarbon production will increase by up to 1.8% until 2016. Also, the wells will reach the water-cut constraint 2 yr later than the current situation, which increases the production period of the field.Azadeh Mamghaderi, Alireza Bastami, Peyman Pourafshar

A new method to forecast reservoir performance during immiscible and miscible gas-flooding via transfer functions approach

Document ID: 145384-MSAbstract In order to improve oil recovery during gas flooding, it is crucial to afford an accurate estimation of future performance. So far, different approaches have been developed to present forecasting. The most common reservoir simulator, grid-based, which has the highest accuracy, suffers from some weaknesses; time-consuming computation and also need for large quantity of data. Sometimes, a quick overview of reservoir performance is adequate or all required data are not accessible. Therefore, in this study a fast simulator is introduced to provide a quick overview with the minimum amount of data. In this study, the development of a method based on transfer functions (TF) is presented to model immiscible and miscible gas flooding. TF is a mathematical representation in Laplace domain which demonstrates the relation between the input and output signals of a system. In the proposed method, reservoirs are modeled with a combination of TFs. The order and arrangement of the TFs are chosen based on the physical conditions of the reservoir which are ascertained by checking several cases. Injection and production mass rates act as input and output signals respectively. TF parameters are calculated using history matching. Also, a fractional flow model is introduced and coupled with the TF system to obtain oil production rate as the final output. In an attempt to validate the approach, it is required to compare the results with the grid-based method. Six different synthetic cases are constructed and used to validate the developed approach. The results state a good agreement with those obtained from the numerical simulators. This approach is a quick way to predict gas flooding performance with minimum amount of data, production and injection rates data are the only requirements. It can be a new window for the future of fast simulators. It provides estimations with plausible certainty. On the other hand, the analytical solution of method enables its utilization in finding optimum rates for gas injection in a short period of time. The method also presents some key parameters such as well connectivity. It is fair to state that the use of the model is limited to situations when a rapid estimation is looked for and/or adequate data is not accessible.Mohammad Sayyafzadeh, Azadeh Mamghaderi, Peyman Pourafshary, and Manouchehr Haghigh

A fast simulator for hydrocarbon reservoirs during gas injection

A fast simulator is presented to forecast quickly the performance of oil reservoirs during gas (miscible and immiscible) injection based on transfer functions (TF). In this method, it is assumed a reservoir consists of a combination of TFs. The order and arrangement of TFs are chosen based on the physical conditions of the reservoir that are ascertained by examining several cases. The selected arrangement and orders can be extended. The only required data of this method is production and injection history that are easily accessible. Injection and production rates act as input and output signals to these TFs, respectively. By analyzing input and output signals, matching parameters are calculated for each case study. The outcomes of the method are compared with those obtained by a grid-based simulator. The comparison indicates a good agreement.M. Sayyafzadeh, A. Mamghaderi, P. Pourafshary, and M. Haghigh