Diffusion and dispersion are considered as the two fundamental mechanisms for the MISCIBLE GAS INJECTION process in the oil reservoirs, which unfortunately are mostly disregarded in the reservoir simulators. In fact, these two phenomena tend to control the miscibility of the injected fluid with oil in the porous media. Neglecting theses (existing) phenomena in reservoir simulators may bring about erroneous results, which may subsequently role an unreliable basis to be applied into the GAS INJECTION projects. The aim of this work is to exhibit the error that should be emerged in case of neglecting these phenomena, in a quantitative manner. Initially, the effects of diffusion and dispersion are shown during a MISCIBLE GAS-INJECTION process using a one-dimensional model under different scenarios. Such scenarios include both considering and neglecting the diffusion and dispersion terms. A comparison between the results from each scenario should demonstrate if a considerable error should arise in the simulation results by neglecting these effects. It would also show if such neglecting will result in inaccurate estimation of GAS composition change, oil recovery factor, and the GAS breakthrough time. Based on the expected results, an appropriate dispersion coefficient for accurate design of MISCIBLE GAS INJECTION processes will be estimated and introduced.

In this work the likelihood of asphaltene deposition problems during dynamic displacement of oil by natural GAS in unconsolidated porous media is experimentally inspected. The two different rock materials, limestone and sandstone, are used as a representative of porous media. Dynamic flow experiments indicate that the increase of natural GAS INJECTION increases asphaltene deposition in the unconsolidated matrix. The results of the study show that increase in asphaltene deposition leads to pore plugging, porosity reduction and absolute permeability damage. Irreducible water measurements showed that natural GAS-induced asphaltenes change the rock wet ability to oil-wet.

The measurement of the minimum miscibility pressure (MMP) is one of the most important steps in the project design of MISCIBLE GAS INJECTION for which several experimental and modeling methods have been proposed. On the other hand, the standard procedure for compositional studies of MISCIBLE GAS INJECTION process is the regression of EOS to the conventional PVT tests. Moreover, this procedure does not necessarily result in an accurate calculation of the MMP. In this study, an effective procedure is presented using both conventional PVT and slim tube data in the regression to provide appropriate EOS parameters for field studies including MISCIBLE GAS INJECTION. In the first step, the EOS parameters were subjected to regression to the conventional PVT data. In addition, these parameters were then used as inputs for simultaneous regression to the conventional PVT and MMP data. MMP is modeled through the automated execution of a series of compositional simulation of slim tube. Moreover, the regression uses a stochastic optimization for minimizing an objective function (regression) have been coupled with two separate core calculations, (1) equilibrium calculations of the conventional tests and (2) compositional simulation of the slim tube. For evaluation, a number of real reservoir fluids from field data are used from reliable datasets in the literature. Finally, the promising results demonstrated that this procedure is capable to provide EOS parameters for accurate predictions in the MISCIBLE GAS INJECTION processes.

CO2 INJECTION processes are among the effective methods for enhanced oil recovery. A key parameter in the design of CO2 INJECTION project is the minimum miscibility pressure (MMP). From an experimental point of view, slim tube displacements test routinely determines the MMP. Because such experiments are very expensive and time-consuming, searching for fast and robust methods for determination of MMP is usually requested. The Neural Network (NN) and Support Vector Machine Regression (SVM) were used to designs networks for estimation MMP. The Networks Trained by trusted data including independent variables. The validity of these new models were successfully approved by comparing the models results to the pure and impure experimental slim-tube CO2-oil MMP and the calculated results for the common pure and impure CO2-oil MMP correlations. The average accuracy of the predicted values for the neural network in terms of coefficient of determination (R2) and mean square error (MSE) are 0.9863 and 0.0018. These values for the SVM regression are 0.9870 and 0.0017, respectively. In addition, the new models could be used for predicting the impure CO2-oil MMP at higher fractions of non-CO2 components (Up to 100% for methane and 50% for hydrogen sulfide).

Simulation is used as a management important tool for reservoir studies and history matching is the most important of issues in the simulation. One of the most important steps in hydrocarbon reservoir simulation is history match. To have a reliable future production or functioning from the reservoir, it is necessary to run a history match on simulated model of the reservoir. This process is generally carried out either manually or automatically. In this paper, the effect of precise and high quality history match is investigated in reservoir production forecast via applying some MISCIBLE GAS INJECTION and natural depletion scenarios on two different normal and fractured reservoir models separately. It is worthy of note that both reservoir models were subjected to automatic history match prepared. In each model, two situations of history match with different precision levels were chosen and then different scenarios were simulated. Finally, the result of scenarios were compared and evaluated according to the precision of each situation.

This study investigates asphaltene precipitation and deposition due to MISCIBLE/imMISCIBLE GAS INJECTION as well as its impact on rock properties and GAS injectivity in one of Iranian southwestern oilfields. Displacement characteristics like GOR, breakthrough time and oil recovery are also studied.According to experiments, induced MISCIBLE/ imMISCIBLE GAS INJECTION permeability impairments are negligible. Therefore, GAS INJECTION has no negative impact on the formation regarding asphaltene precipitation and deposition.Ultimate oil recoveries are about 73% and 66% for MISCIBLE and imMISCIBLE GAS INJECTIONs respectively. These approximate oil recoveries are obtained in laboratory core scale representing microscopic sweep efficiencies then to convert it to field recoveries it should be multiplied by macroscopic ones to be experienced in the field application.

Asphaltene instability is one of the major problems in GAS INJECTION projects throughout the world. Numerous models have been developed to predict asphaltene precipitation, The scaling equation is an attractive tool because of its simplicity and not involving complex properties of asphaltene. In this work, a new scaling model is presented to account for asphaltene precipitation due to GAS INJECTION at reservoir conditions. Extensive published data from literature have been used in model preparation. To check predictive capability of the equation, MISCIBLE GAS INJECTION experiments are conducted for a southwest Iranian oil reservoir. Experimental results show that methane INJECTION has significant effect on asphaltene precipitation and direct effect of temperature is less severe than other parameters. In addition to the accuracy and simplicity, the proposed equation provides universal parameters which make this approach novel for evaluation of future GAS INJECTION projects when simple PVT data are available.