Activating solution gas drive as an extra oil production mechanism after carbonated water injection

Enhanced oil recovery(EOR)methods are mostly based on different phenomena taking place at the interfaces between fluid–fluid and rock–fluid phases.Over the last decade,carbonated water injection(CWI)has been considered as one of the multi-objective EOR techniques to store CO2 in the hydrocarbon bearing formations as well as improving oil recovery efficiency.During CWI process,as the reservoir pressure declines,the dissolved CO2 in the oil phase evolves and gas nucleation phenomenon would occur.As a result,it can lead to oil saturation restoration and subsequently,oil displacement due to the hysteresis effect.At this condition,CO2 would act as insitu dissolved gas into the oil phase,and play the role of an artificial solution gas drive(SGD).In this study,the effect of SGD as an extra oil recovery mechanism after secondary and tertiary CWI(SCWI-TCWI)modes has been experimentally investigated in carbonate rocks using coreflood tests.The depressurization tests resulted in more than 25%and 18%of original oil in place(OOIP)because of the SGD after SCWI and TCWI tests,respectively.From the ultimate enhanced oil recovery point of view,the efficiency of SGD was observed to be more than one-third of that of CWI itself.Furthermore,the pressure drop data revealed that the system pressure depends more on the oil production pattern than water production.

Implementation of multilayer perceptron(MLP)and radial basis function(RBF)neural networks to predict solution gas-oil ratio of crude oil systems

Exact determination of pressure-volume-temperature(PVT)properties of the reservoir oils is necessary for reservoir calculations,reservoir performance prediction,and the design of optimal production conditions.The objective of this study is to develop intelligent and reliable models based on multilayer perceptron(MLP)and radial basis function(RBF)neural networks for estimating the solution gas–oil ratio as a function of bubble point pressure,reservoir temperature,oil gravity(API),and gas specific gravity.These models were developed and tested using a total of 710 experimental data sets representing the samples of crude oil from various geographical locations around the world.Performance of the developed MLP and RBF models were evaluated and investigated against a number of well-known empirical correlations using statistical and graphical error analyses.The results indicated that the proposed models outperform the considered empirical correlations,providing a strong agreement between predicted and experimental values,However,the developed RBF exhibited higher accuracy and efficiency compared to the proposed MLP model.

Effectiveness and sensitivity analysis of solution gas re-injection in Baikouquan tight formation,Mahu sag for enhanced oil recovery

To address the fast productivity decline of the horizontal wells and low oil recovery during natural depletion in Baikouquan formation,the approach of solution gas re-injection was proposed with the primary objective of further developing this formation.Herein,a field-scale numerical compositional reservoir model was built up based on the formation properties and then the effects of permeability,fractures and formation stress on the production dynamics were thoroughly investigated.A sensitivity analysis,which can correlate the oil recovery with these parameters,was also performed.The results showed that the re-injection of solution gas could remarkably retard the production depletion of the horizontal wells thereby improving the oil production.The oil recovery rate increased with permeability,fracture half-length,fracture conductivity,and formation dip.With regard to the fracture distribution,it was found that the interlaced fracture outperformed the aligned fracture for the solution gas re-injection.The influence of the formation stress should be carefully considered in the production process.Sensitivity analysis indicated that the formation dip was the paramount parameter,and the permeability,fracture half-length,and fracture conductivity also played central roles.The results of this study supplement earlier observations and provide constructive envision for enhanced oil recovery of tight reservoirs.

CONVERGENCE OF THE APPROXIMATE SOLUTIONS TO ISENTROPIC GAS DYNAMICS

This paper gives four pairs of entropies (η_i, q_i) (i=1, 2, 3, 4) to the isentropic gas dynamics equations ρ_t+(ρu)_x=0 (ρu)_t+(ρu^2+p(ρ))_x=0 p(ρ)=k^2ρ~γ,1<γ<3。 when all the function equations are satisfied

HIGH-PERFORMANCE GAS DIFFUSION POROUS STARVED OF ELECTROLYTE SOLUTION

The performance of gas diffusion porous electrode starved of electrolyte solution can be significantly increased by decreasing the thickness of uneven liquid film covering the catalyst agglomerates.

The Aysmptotic Behavior of Solution to a Class of Inhomogeneons Systems of Gas Dynamics

Consider an initial-boundary problem vt - ux=0,u, + （）x + f（u） = （）x,θt+ux=（）ux=（）x+ （E） v（x,0） = v0（x）,u（x,0） = u0（x）,θ（0,x） = θ0（x）, （I） u（t,0） = u（t,1） = θx（t,0） = θx（t,1） （J） Sufficient and necessary conditions for （E）, （I） and （J） to have asymptotic stability of the gobal smooth solution are given by means of the elemental L2 energy method.

A robust predictive tool for estimating CO2 solubility in potassium based amino acid salt solutions

The acid gas absorption in four potassium based amino acid salt solutions was predicted using artificial neural network(ANN). Two hundred fifty-five experimental data points for CO_2 absorption in the four potassium based amino acid salt solutions containing potassium lysinate, potassium prolinate, potassium glycinate, and potassium taurate were used in this modeling. Amine salt solution's type, temperature, equilibrium partial pressure of acid gas, the molar concentration of the solution, molecular weight, and the boiling point were considered as inputs to ANN to prognosticate the capacity of amino acid salt solution to absorb acid gas. Regression analysis was employed to assess the performance of the network. Levenberg–Marquardt back-propagation algorithm was used to train the optimal ANN with 5:12:1 architecture. The model findings indicated that the proposed ANN has the capability to predict precisely the absorption of acid gases in various amino acid salt solutions with Mean Square Error(MSE) value of 0.0011, the Average Absolute Relative Deviation(AARD) percent of 5.54%,and the correlation coefficient(R^2) of 0.9828.

Advances in Supercritical Fluid Crystallization

The supercritical fluid crystallization technique is a novel technology for preparing ultrafine particles. This paper introduced the concept and features of the technique with an emphasis on three kinds of supercritical fluid crystallization techniques, i.e. rapid expansion of supercritical solutions, supercritical fluid anti-solvent and particles from gas saturated solutions Some questions and the prospect of this technique were also discussed.

Shock relations in gases of heterogeneous thermodynamic properties

Shock relations usually found in literatures are derived theoretically under the assumption of homogeneous thermodynamic properties, i.e., constant ratio of specific heats, γ. However, high temperature effects post a strong shock wave may result in thermodynamic heterogeneities and failure to the original shock relations. In this paper, the shock relations are extended to take account of high-temperature effects. Comparison indicates that the present approach is more feasible than other analytical approaches to reflect the influence of γ heterogeneity on the post-shock parameters.

Data driven prediction of oil reservoir fluid properties

Accuracy of the fluid property data plays an absolutely pivotal role in the reservoir computational processes.Reliable data can be obtained through various experimental methods,but these methods are very expensive and time consuming.Alternative methods are numerical models.These methods used measured experimental data to develop a representative model for predicting desired parameters.In this study,to predict saturation pressure,oil formation volume factor,and solution gas oil ratio,several Artificial Intelligent(AI)models were developed.582 reported data sets were used as data bank that covers a wide range of fluid properties.Accuracy and reliability of the model was examined by some statistical parameters such as correlation coefficient(R2),average absolute relative deviation(AARD),and root mean square error(RMSE).The results illustrated good accordance between predicted data and target values.The model was also compared with previous works and developed empirical correlations which indicated that it is more reliable than all compared models and correlations.At the end,relevancy factor was calculated for each input parameters to illustrate the impact of different parameters on the predicted values.Relevancy factor showed that in these models,solution gas oil ratio has greatest impact on both saturation pressure and oil formation volume factor.In the other hand,saturation pressure has greatest effect on solution gas oil ratio.

A new heuristic model for estimating the oil formation volume factor

The necessity of oil formation volume factor(Bo)determination does not need to be greatly emphasized.Different types of reservoir oil have specific conditions which impart the hydrocarbon's major properties,among which is the oil formation volume factor.Therefore,it seems imperative to construct a model capable of estimating the value of oil formation volume factor.Previous studies have resulted in a number of correlations for oil formation volume factor estimation;however,a large portion of them do not provide an acceptable accuracy(at least in some range of data)and cause a huge error at these points.Some others are not flexible enough to be tuned for a specific type of reservoir oil and a comprehensive piece of work does not exist as well in order to compare the applicability of the new models for estimating the oil formation volume factor.In this research,a model based on simulated annealing(SA)has been built in terms of temperature,solution gas-oil ratio,and gravity of oil and gas to predict the oil formation volume factor.This model is compared with the models proposed in the most recent studies,which shows the greater performance of the new method.In addition,in this paper the models of the recent years were compared with each other and their applicability were discussed.Aiming to compare the models,420 data points were selected and the estimated values of each model for oil formation volume factor were compared with their experimental ones.