Evolving smart approach for determination dew point pressure through condensate gas reservoirs

Connectionist model for predicting minimum gas miscibility pressure: Application to gas injection process

“…A set of laboratorial data accessible in 47 the open literature was gained to test the reliability of the proposed HGAPSO-LSSVM model which its 48 generated results have been compared with the other proposed intelligent approaches. Moreover, the 49 performances of both implemented solutions certify statistically the strong potential of models in predic- 50 tion of the MMP. 51 Ó 2015 Elsevier Ltd. All rights reserved.…”

“…By reaching to the MMP, the displacement is piston-like 67 and the oil recovery is 100% at 1 pore volume of the injected gas, 68 if the displacement process is represented as a one dimensional, 69 two-phase, dispersion-free flow [2-4]. 70 Optimum displacement efficiency of gas flooding happens at 71 whereas following constraints should be considered [45][46][47][48][49][50]: The assembled real database was separated into three subsets. 280 The first, which is utilized in the process of training, contains 281 80% of the whole data (68 data point) and is equal to 55 data lines.…”

Real-Time Estimation of Some Thermodynamics Properties During a Microwave Heating Process

“…An example is the prediction of oil flow rates from reservoirs using a combination of artificial neural networks and an imperialist competitive algorithm [2], as well as the prediction of thermodynamic properties such as miscibility, solubility, and dew point. Other applications include phase equilibrium, dehydrator performance, and gas analysis in materials such as natural gas, ionic fluids, hydrates, and oil field brine [3]- [7].…”

Dew point pressure model for gas condensate reservoirs based on multi-gene genetic programming approach

“…This approach has convergence problems because matching parameters of selected EOS should be tuned with some experimental data by least squared method. In addition, the EOS approach does not generalize to unseen data, and usually memorizes the data that were used to develop it [17].…”

“…In recent decades, the DPP estimation using artificial intelligent techniques such as artificial neural networks (ANNs), adaptive neuro-fuzzy inference system (ANFIS) and support vector machine (SVM) has been investigated in several studies and good predictions have been reported [17][18][19][20][21][22][23][24]. Despite the acceptable performance of ANNs, there is no efficient procedure to select the structure to build such networks.…”