Abdolhossein Hemmati-Sarapardeh

Using an artificial neural network to predict carbon dioxide compressibility factor at high pressure and temperature

Carbon dioxide injection, which is widely used as an enhanced oil recovery (EOR) method, has the potential of being coupled with CO2 sequestration and reducing the emission of greenhouse gas. Hence, knowing the compressibility factor of carbon dioxide is of a vital significance. Compressibility factor (Z-factor) is traditionally measured through time consuming, expensive and cumbersome experiments. Hence, developing a fast, robust and accurate model for its estimation is necessary. In this study, a new reliable model on the basis of feed forward artificial neural networks is presented to predict CO2 compressibility factor. Reduced temperature and pressure were selected as the input parameters of the proposed model. To evaluate and compare the results of the developed model with pre-existing models, both statistical and graphical error analyses were employed. The results indicated that the proposed model is more reliable and accurate compared to pre-existing models in a wide range of temperature (up to 1,273.15 K) and pressure (up to 140 MPa). Furthermore, by employing the relevancy factor, the effect of pressure and temprature on the Z-factor of CO2 was compared for below and above the critical pressure of CO2, and the physcially expected trends were observed. Finally, to identify the probable outliers and applicability domain of the proposed ANN model, both numerical and graphical techniques based on Leverage approach were performed. The results illustrated that only 1.75% of the experimental data points were located out of the applicability domain of the proposed model. As a result, the developed model is reliable for the prediction of CO2 compressibility factor.

Implementation of soft computing approaches for prediction of physicochemical properties of ionic liquid mixtures

The main objective of this study was to develop soft computing approaches for prediction of physicochemical properties of IL mixtures including: density, heat capacity, thermal conductivity, and surface tension. The proposed models in this study are based on support vector machine (SVM), least square support vector machines (LSSVM), and group method of data handling type polynomial neural network (GMDH-PNN) systems. To find the LSSVM and SVM adjustable parameters, genetic algorithm (GA) as a meta-heuristic algorithm was utilized. The results showed that LSSVM is more robust and reliable for prediction of physicochemical properties of IL mixtures. The proposed GA-LSSVM model provides average absolute relative deviations of 0.38%, 0.18%, 0.77% and 1.18% for density, heat capacity, thermal conductivity, and surface tension, respectively, which demonstrates high accuracy of the model for prediction of physicochemical properties of IL mixtures.

Modeling the permeability of heterogeneous oil reservoirs using a robust method

Permeability as a fundamental reservoir property plays a key role in reserve estimation, numerical reservoir simulation, reservoir engineering calculations, drilling planning, and mapping reservoir quality. In heterogeneous reservoir, due to complexity, natural heterogeneity, non-uniformity, and non-linearity in parameters, prediction of permeability is not straightforward. To ease this problem, a novel mathematical robust model has been proposed to predict the permeability in heterogeneous carbonate reservoirs. To this end, a fairly new soft computing method, namely least square support vector machine (LSSVM) modeling optimized with coupled simulated annealing (CSA) optimization technique was utilized. Statistical and graphical error analyses have been employed separately to evaluate the accuracy and reliability of the proposed model. Furthermore, this model performance has been compared with a newly developed multilayer perceptron artificial neural network (MLP-ANN) model. The obtained results have shown the more robustness, efficiency and reliability of the proposed CSA-LSSVM model in comparison with the developed MLP-ANN model for the prediction of permeability in heterogeneous carbonate reservoirs. Estimations were found to be within acceptable agreement with the actual field data of permeability, with a root mean square error of approximately 0.42 for CSA-LSSVM model in testing phase, and a R-squared value of 0.98. Additionally, these error parameters for MLP-ANN are 0.68 and 0.89 in testing stage, respectively.

Towards Mechanistic Understanding of Steam Assisted Gravity Drainage in Naturally Fractured Reservoirs

Steam Assisted Gravity Drainage (SAGD) as a successful enhanced oil recovery (EOR) process has been applied to extract heavy and extra heavy oils. Huge amount of global heavy oil resources exists in carbonate reservoirs which are mostly naturally fractured reservoirs. Unlike clastic reservoirs, very few studies were carried out to determine the performance of SAGD in carbonate reservoirs. Even though SAGD is a highly promising technique, several uncertainties and unanswered questions still exist and they should be clarified for expansion of SAGD methods to world wide applications especially in naturally fractured reservoirs. In this communication, the effects of some operational and reservoir parameters on SAGD processes were investigated in a naturally fractured reservoir with oil wet rock using CMG-STARS thermal simulator. The purpose of this study was to investigate the role of fracture properties including fracture orientation, fracture spacing and fracture permeability on the SAGD performance in naturally fractured reservoirs. Moreover, one operational parameter was also studied; one new well configuration, staggered well pair was evaluated. Results indicated that fracture orientation influences steam expansion and oil production from the horizontal well pairs. It was also found that horizontal fractures have unfavorable effects on oil production, while vertical fractures increase the production rate for the horizontal well. Moreover, an increase in fracture spacing results in more oil production, because in higher fracture spacing model, steam will have more time to diffuse into matrices and heat up the entire reservoir. Furthermore, an increase in fracture permeability results in process enhancement and ultimate recovery improvement. Besides, diagonal change in the location of injection wells (staggered model) increases the recovery efficiency in long-term production plan.