Riyaz Kharrat

Global Dynamic Harmony Search algorithm: GDHS

This paper presents a new modification of Harmony Search (HS) algorithm to improve its accuracy and convergence speed and eliminates setting parameters that have to be defined before optimization process and it is difficult to predict fixed values for all kinds of problems. The proposed algorithm is named Global Dynamic Harmony Search (GDHS). In this modification, all the key parameters are changed to dynamic mode and there is no need to predefine any parameters; also the domain is changed to dynamic mode to help a faster convergence. Two experiments, with large sets of benchmark functions, are executed to compare the proposed algorithms with other ones. In the first experiment, 15 benchmark problems are used to compare the proposed algorithm with other similar algorithms based on the Harmony Search method and in the second experiment, 47 benchmark problems are used to compare the performance of the GDHS with other algorithms from different families, including: GA, PSO, DE and ABC algorithms. Results showed that the proposed algorithm outperforms the other algorithms, considering the point that the GDHS does not require any predefined parameter.

On the Control of Glass Micro-model Characteristics Developed by Laser Technology

The role of micro-models for studying fluid flow mechanisms at the pore scale is undeniable. Recently, application of laser technology has been much attended for developing micro-models with different flow patterns. However, there is no reported experience in the literature that has been correlated with the laser parameters for direct control of micro-model pore size characteristics. In this work, a CO2 laser device was used to construct flow patterns on the glass surfaces, and the effect of fractional power, engraving speed, and resolution ratio on etched depth as well as surface heterogeneity of constructed models were investigated. A new correlation has been proposed that relates the laser parameters to the pore morphology of the glass micro-models. The proposed exponential expression provides a reasonably accurate and fast tool for controlling micro-model characteristics. In addition, microscopic observation showed that sensible heterogeneity on glass surfaces induced by laser can be controlled by adjusting the resolution ratio, and is a good representation of reservoir rock surfaces. The permeability of the constructed models by a laser device is a better representation of real reservoir rock conditions. The results of this work can be helpful for designing and constructing micro-models with controlling pore morphology.

An Experimental Investigation of Silica Nanoparticles Effect on the Rheological Behavior of Polyacrylamide Solution to Enhance Heavy Oil Recovery

The use of polymer flooding as one of enhanced oil recovery methods has recently increased. The occurrence of high shear rates in reservoir and near well bore through perforation nozzles during polymer flooding cause shear degradation of polymers and therefore polymer viscosity has decreased. Rheological behavior of polymer solution in different conditions of oil reservoir is one of the key factors to develop use of polymer solutions. A few researches are available regarding improving rheological behavior of polymeric solution. In this study, to investigate the effect of nanoparticles on rheological behavior of polymer solutions two samples were prepared: polyacrylamide solution in water and suspension of silica nanoparticles in polyacrylamide solution. The sample viscosities in different shear rates were measured. The best rheology models were developed to state rheological behavior of prepared samples and the measured data were compared to power law model. An increase in the viscosity of the suspension solution with respect to polymer solution in different shear rates was observed. Rheological analysis showed that power law model is a good rheology model to demonstrate rheological behavior of suspension in low and medium shear rates and is an acceptable model for polymer solution in low shear rates. Two types of flooding test were performed in a glass micromodel: flooding by polyacrylamide solution and suspension of silica nanoparticles in polyacrylamide solution. The results of flooding test showed a 10% increase in oil recovery for nanosuspension solution in comparison with polymer solution after one pore volume fluid injection.

The effect of pore throat size and injection flowrate on the determination and sensitivity of different capillary number values at high-capillary-number flow in porous media

This study examines the effect of pore throat size and injection flowrate on the values of the pore-scale capillary number, the Newtonian-fluid capillary number and the apparent capillary number (Nc1, Nc2 and Nc3, respectively) and their sensitivity to change in high-capillary-number flow through porous media, which occurs in polymer-assisted dilute surfactant flooding (PADSF). Additionally, the influence of pore throat size and injection flowrate on oil recovery at breakthrough and at the end of displacement (ultimate) and the relationship between the effective shear rate γeff and the porous medium-dependent shift factor α are discussed. The results indicated that Nc2 was the smallest and Nc3 was the largest value. The difference between Nc2 and Nc3 is due to the increase in apparent viscosity of the polymer-contained surfactant solution during the flow through porous media and the change in Nc3 should be utilized to characterize the macroscopic behavior of the PADSF. Generally, the decrease in pore throat size and the increase in injection flowrate caused an increase in the ultimate oil recovery and Nc3. Moreover, the oil recovery at breakthrough decreased with an increase in pore throat size and injection flowrate. Finally, the rate of change of γeff, with change in α, increased almost uniformly with a decrease in pore throat size and an increase in injection flowrate.

Prediction of the asphaltene deposition profile along a wellbore during natural production from a reservoir

ABSTRACT The potential problem of asphaltene deposition during oil production has motivated both academics and industries to predict the asphaltene deposit profile in wellbores and pipelines. In this work, asphaltene deposition profile along an oil field well with the severe problem of asphaltene deposition was predicted. To do this, a comprehensive simulator for modeling of flow parameters such as pressure, temperature, and composition for a multiphase flow of oil, gas, and asphaltene from the reservoir to the surface was developed and coupled with the deposition model. By applying the simulator to an oil field well, it has been found that 60–70% of the total asphaltene thickness formed after 1 month of production, indicating that the problem of asphaltene deposition is bound to the initial stage of wellbore life. Moreover, the simulator was able to predict the accumulated asphaltene thickness and the time of wellbore plugging properly. This prediction is highly crucial if it is aimed to control the well performance and to optimize the productivity.

Prediction of Asphaltene Precipitation during Pressure Depletion and CO2 Injection for Heavy Crude

Abstract In this work, a thermodynamic approach is used for modeling the phase behavior of asphaltene precipitation. The precipitated asphaltene phase is represented by an improved solid model, and the oil and gas phases are modeled with an equation of state. The Peng-Robinson equation of state (PR-EOS) was used to perform flash calculations. Then, the onset point and the amount of precipitated asphaltene were predicted. A computer code based on the solid model was developed and used for predicting asphaltene precipitation data reported in the literature as well as the experimental data obtained from high-pressure, high-temperature asphaltene precipitation experiments performed on Sarvak reservoir crude, one of Iranian heavy oil reserves, under pressure depletion and CO2 injection conditions. The model parameters, obtained from sensitivity analysis, were applied in the thermodynamic model. It has been found that the solid model results describe the experimental data reasonably well under pressure depletion conditions. Also, a significant improvement has been observed in predicting the asphaltene precipitation data under gas injection conditions. In particular, for the maximum value of asphaltene precipitation and for the trend of the curve after the peak point, good agreement was observed, which could not be found in the available literature.

A Review on Thermal Enhanced Heavy Oil Recovery from Fractured Carbonate Reservoirs

Heavy oil in Middle East fractured carbonate reservoirs account for 25–30% of the total oil in place in the region. Production of heavy oil from such reservoirs is thought to play an important role in the future of the ever-growing world’s energy consumption in which Iran’s recoverable heavy oil is more than 85 billion barrels. The offshore Ferdows field in Iran is reportedly on the order of 30 billion barrels of oil and holds perhaps the greatest promise to add significant future carbonate heavy oil production within the region. With depletion of conventional petroleum reserves and increase of hydrocarbon fuel demand, there is no doubt that there will be a tremendous demand on the development of heavy oil reservoirs in the coming decades. Despite its strategic importance, recovery of heavy crude from fractured carbonate reservoirs has found limited applications due to the complexity of such reservoirs. As most of the oil is stored in matrix due to its higher storage capacity than fracture network, reservoir development plans will aim at maximizing the matrix oil recovery. For reservoirs with high recovery factor, minimizing matrix residual oil saturation is a critical issue to extend the life of the reservoir. For reservoirs with low recovery factor, accelerating the production rate is more vital. For each of these reservoir types, different Enhanced Oil Recovery (EOR) methods should be considered and implemented accordingly. In this study, a comprehensive review is conducted to figure out the feasibility of heavy oil recovery from fractured carbonate reservoirs by use of Cyclic Steam Stimulation (CSS), Steam injection, In-Situ Combustion (ISC), Steam Assisted Gravity Drainage (SAGD), Vapor Extraction (VAPEX) and Expanding Solvent-Steam Assisted Gravity Drainage (ES-SAGD).

Prediction of asphaltene precipitation during gas injection

ABSTRACT Maintaining the flow of multiphase fluid from the reservoir to the surface has been an important issue with wide economic importance for the petroleum industry. Asphaltene precipitation due to change in temperature, pressure, and composition of oil can adversely affect the oil flow to the surface by reducing the available diameter of the tubing. In this study, the precipitation of asphaltene from an Iranian crude oil was investigated. To do our study, through information about asphaltene instability in the live oil during both natural depletion and gas injection conditions about oil sample from Iranian oil field was gathered. Then, the solid model and scaling model were utilized to predict the weight percent of precipitated asphaltene at a wide range of the pressure and temperature. Results of the work revealed that both models predict the increase in weight percent of precipitated asphaltene when lean gas injected to the live oil at the maximum point of asphaltene instability. In addition, the study showed that both models are capable of predicting the experimental data of asphaltene precipitation; while scaling modeling is more reliable when the gas is injected to the oil.

Comparison and evaluation of several models in prediction of asphaltene deposition profile along an oil well: a case study

Deposition of asphaltenes on the inner surface of oil wells and pipelines causes flow blockage or significant production loss in these conduits. Generally, asphaltenes are stable in reservoir condition; however, change in pressure, temperature, and composition can trigger phase separation and then deposition of asphaltene along the flow stream. Therefore, it is required to identify the possibility of asphaltene precipitation and accurately quantify deposition tendency of these heavy organic molecules. This work is aimed at detailed assessment of the predictive capability of five deposition models available in the literature for calculating the magnitude and profile of asphaltene deposition in wellbores. To end this, firstly we discuss and describe these five models known as Friedlander and Johnstone (Ind Eng Chem 49:1151–1156, 1957), Beal (Nucl Sci Eng 40:1–11, 1970), Escobedo and Mansoori (SPE annual technical conference and exhibition, 1995), Cleaver and Yates (Chem Eng Sci 30:983–992, 1975), and Jamialahmadi et al. (Int J Heat Mass Transf 52:4624–4634, 2009). Afterward, thermodynamic modeling of live oil and a wellbore P–T relationship of the flowing fluid were used in a graphical method in order to identify asphaltene precipitation zone along axial wellbore length. Then, the five models were applied to the wellbore to forecast the deposition tendency of precipitated asphaltene particles and to obtain a profile of deposited asphaltenes. Most importantly, a measured deposit profile of the investigated wellbore enabled us to select the most accurate one for estimating the asphaltene deposition rate. The validation method presented in this work reveals that Cleaver and Yates (1975), Jamialahmadi et al. (2009), and Escobedo and Mansoori (1995) models have a satisfactory performance in predicting asphaltene deposition profile along the wellbore when compared to caliper measurement of the well.

Modeling of steam distillation mechanism during steam injection process using artificial intelligence.

Steam distillation as one of the important mechanisms has a great role in oil recovery in thermal methods and so it is important to simulate this process experimentally and theoretically. In this work, the simulation of steam distillation is performed on sixteen sets of crude oil data found in the literature. Artificial intelligence (AI) tools such as artificial neural network (ANN) and also adaptive neurofuzzy interference system (ANFIS) are used in this study as effective methods to simulate the distillate recoveries of these sets of data. Thirteen sets of data were used to train the models and three sets were used to test the models. The developed models are highly compatible with respect to input oil properties and can predict the distillate yield with minimum entry. For showing the performance of the proposed models, simulation of steam distillation is also done using modified Peng-Robinson equation of state. Comparison between the calculated distillates by ANFIS and neural network models and also equation of state-based method indicates that the errors of the ANFIS model for training data and test data sets are lower than those of other methods.