Genetic algorithm application for matching ordinary black oil PVT data

In the study of reservoirs, it is vital that we have a realistic physical model of the reservoir fluid that accurately describes the hydrocarbon system and its properties. The available equations of state （EOS） to model the fluid phase behavior have some inherent deficiencies that may cause erroneous predictions for real reservoir fluids, so these models should be tuned against experimental data by adjusting some parameters. Since there are many matching parameters, tuning the EOS against experimental data is a tedious and difficult work. In this study, a genetic algorithm as an optimization technique is used to solve this regression problem. This study presents a new method that uses a specially designed genetic algorithm to search for suitable regression parameters to match the EOS against measured data. The proposed method has been tested on three real black oil samples. The results show the surprising performance of the developed genetic algorithm to match the experimental data of the selected fluid samples. The main advantage of the used method is its high speed in finding a solution. Also, finding more than one solution, working automatically, confining the role of experts to the last stage, reducing costs and having the possibility of evaluating the different situations are the other advantages of this method to match ordinary black oil PVT data and makes it an ideal method to implement as an automatic EOS tuning algorithm for black oils.

Prediction of immiscible gas flooding performance:a modified capacitance-resistance model and sensitivity analysis

Reservoir performance prediction is one of the main steps during a field development plan.Due to the complexity and time-consuming aspects of numerical simulators,it is helpful to develop analytical tools for a rapid primary analysis.The capacitance-resistance model(CRM)is a simple technique for reservoir management and optimization.This method is an advanced time-dependent material balance equation which is combined with a productivity equation.CRM uses production/injection data and bottom-hole pressure as inputs to build a reliable model,which is then combined with the oil-cut model and converted to a predictive tool.CRM has been studied thoroughly for water flooding projects.In this study,a modified model for gas flooding systems based on gas density and average reservoir pressure is developed.A detailed procedure is described in a synthetic reservoir model using a genetic algorithm.Then,a streamline simulation is implemented for validation of the results.The results show that the proposed model is able to calculate interwell connectivity parameters and oil production rates.Moreover,a sensitivity analysis is carried out to investigate effects of drawdown pressure and gas PVT properties on the new model.Finally,acceptable ranges of input data and limitations of the model are comprehensively discussed.

Novel empirical correlations for estimation of bubble point pressure,saturated viscosity and gas solubility of crude oils

Knowledge of petroleum fluid properties is crucial for the study of reservoirs and their development. Estimation of reserves in an oil reservoir or determination of its performance and economics requires a good knowledge of the fluid physical properties. Bubble point pressure, gas solubility and viscosity of oils are the most important parameters in use for petroleum and chemical engineers. In this study a simple-to-use, straight-forward mathematical model was correlated on a set of 94 crude oil data. Three correlations were achieved based on an exponential regression, which were different from conventional empirical correlations, and were evaluated against 12 laboratory data other than those used for the regression. It is concluded that the new exponential equation is of higher precision and accuracy than the conventional correlations and is a more convenient mathematical formulation.

Enduring effect of permeability texture for enhancing accuracy and reducing uncertainty of reservoir fluid flow through porous media

Modeling reservoir permeability is one of the crucial tasks in reservoir simulation studies.Traditionally,it is done by kriging-based methods.More rigorous modeling of the permeability results in more reliable outputs of the reservoir models.Recently,a new category of geostatistical methods has been used for this purpose,namely multiple point statistics(MPS).By this new category of permeability modeling methods,one is able to predict the heterogeneity of the reservoir permeability as a continuous variable.These methods consider the direction of property variation in addition to the distances of known locations of the property.In this study,the reservoir performance of a modified version of the SPE 10 solution project as a pioneer case is used for investigating the efficiency of these methods and paralleling them with the kriging-based one.In this way,the permeability texture concept is introduced by applying some MPS methods.This study is accomplished in the conditions of real reservoir dimensions and velocities for the whole reservoir life.A continuous training image is used as the input of calculation for the permeability modeling.The results show that the detailed permeability of the reservoir as a continuous variable makes the reservoir simulation show the same fluid front movement and flooding behavior of the reservoir similar to the reference case with the same permeability heterogeneity.Some MPS methods enable the reservoir simulation to reproduce the fluid flow complexities such as bypassing and oil trapping during water flooding similar to the reference case.Accordingly,total oil production is predicted with higher accuracy and lower uncertainty.All studied cases are identical except for the permeability texture.Even histograms and variograms of permeabilities for the studied reservoir are quite similar,but the performance of the reservoir shows that kriging-based method results have slightly less accuracy than some MPS methods.Meanwhile,it results in lower uncertainty in outputs for this water flooding case performance.

Mathematical Modeling of Onset of Convection in a Porous Layer with Viscosity Variation

Instability theory is applied to diffusion-convection phenomenon in porous media, where the area in direction of transfer is large and viscosity of the oil varies due to gas dissolution. An important application of this theory arises where diffusion-convection is employed as an EOR technique in oil reservoirs. As a bed of gas is formed below a colunm of oil, gas starts to diffuse into the oil. Therefore, the oil becomes lighter and an inverse gradient of density is developed as more gas diffuses in. Although this inverse density gradient is potentially unstable, convection will not initiate until the gradient extends to a certain value. The condition at which convection begins is known as "the onset of convectin" and is well specified by the dimensionless Rayleigh number. In this study, an instability analysis is made for convection-diffusion in large porous media. Unlike Other studies where viscosity is assumed constant in this work viscosity is postulated to be a function of gas concentration. It is shown that the mathematical model developed reduces to previous models if the viscosity vacations are ignored.