Deterministic tools to predict gas assisted gravity drainage recovery factor

Naturally fractured rocks contain most of the world's petroleum reserves.This significant amount of oil can be recovered efficiently by gas assisted gravity drainage(GAGD).Although,GAGD is known as one of the most effective recovery methods in reservoir engineering,the lack of available simulation and mathematical models is considerable in these kinds of reservoirs.The main goal of this study is to provide efficient and accurate methods for predicting the GAGD recovery factor using data driven techniques.The proposed models are developed to relate GAGD recovery factor to the various parameters including model height,matrix porosity and permeability,fracture porosity and permeability,dip angle,viscosity and density of wet and non-wet phases,injection rate,and production time.In this investigation,by considering the effective parameters on GAGD recovery factor,three different efficient,smart,and fast models including artificial neural network(ANN),least square support vector machine(LSSVM),and multi-gene genetic programming(MGGP)are developed and compared in both fractured and homogenous porous media.Buckinghamπtheorem is also used to generate dimensionless numbers to reduce the number of input and output parameters.The efficiency of the proposed models is examined through statistical analysis of R-squared,RMSE,MSE,ARE,and AARE.Moreover,the performance of the generated MGGP correlation is compared to the traditional models.Results demonstrate that the ANN model predicts the GAGD recovery factor more accurately than the LSSVM and MGGP models.The maximum R^(2)of 0.9677 and minimum RMSE of 0.0520 values are obtained by the ANN model.Although the MGGP model has the lowest performance among the other used models(the R2 of 0.896 and the RMSE of 0.0846),the proposed MGGP correlation can predict the GAGD recovery factor in fractured and homogenous reservoirs with high accuracy and reliability compared to the traditional models.Results reveal that the employed models can easily predict GAGD recovery factor without requiring complicate governing equations or running complex and time-consuming simulation models.The approach of this research work improves our understanding about the most significant parameters on GAGD recovery and helps to optimize the stages of the process,and make appropriate economic decisions.

New empirical scaling equations for oil recovery by free fall gravity drainage in naturally fractured reservoirs

Gas-oil gravity drainage is a recognized major contributor to production in fractured reservoirs. While various empirical and analytical methods have been proposed to model this process, many of them contain assumptions that are questionable or require parameters that are not accessible at the field level. The aim of this work is to provide new, easy-to-use scaling equations for estimating the recoverable oil through gravity drainage in naturally fractured reservoirs, considering the effects of resistance capillary pressure. To accomplish this, data from four oilfields undergoing gravity drainage, including rock properties (eight sets), block height (three sets), and fluid properties (four sets), were used to generate a wide range of recovery curves using a single porosity numerical simulation model. Aronofsky's and Lambert's functions were then utilized to match the generated recovery curves. Statistical analysis revealed that the Aronofsky's function is more accurate in replicating the recovery patterns, while the Lambert's function tends to overestimate the early-time oil recovery and underestimate the oil recovery at a later stage in the majority of cases. A sensitivity analysis was subsequently performed, revealing that parameters such as absolute permeability, viscosity of oil, height of block, gas and oil density, characteristics of relative permeability and capillary pressure curves and interfacial tension (IFT) influence the amount of time taken to achieve the final recovery. Of these parameters, absolute permeability has the most significant effect on the amount of time needed to attain the final recovery, while the effect of difference between oil and gas densities is the lowest. Consequently, two different expressions were developed using nonlinear multiple regression analysis of simulated gravity drainage data which can be combined with the Aronofsky model to substitute the rate convergence constant. The new scaling equations include the effects of capillary pressure and other relevant factors in gravity drainage simulations. Both forms show satisfactory accuracy, as evidenced by the statistical parameters obtained (R2 = 0.99 and MSE = 0.0019 for both established correlations). The new correlations were verified using a wide range of oilfield data and are expected to provide a better understanding of the recovery process in naturally fractured reservoirs.

Experimental investigation on stable displacement mechanism and oil recovery enhancement of oxygen-reduced air assisted gravity drainage

The effects of gravity,capillary force,and viscous force on the migration characteristics of oil and gas interface in oxygen-reduced air-assisted gravity drainage(OAGD)were studied through a two-dimensional visualization model.The effects of bond number,capillary number and low-temperature oxidation on OAGD recovery were studied by long core displacement experiments.On this basis,the low-temperature oxidation number was introduced and its relationship with the OAGD recovery was established.The results show that the shape and changing law of oil and gas front are mainly influenced by gravity,capillary force and viscous force.When the bond number is constant(4.52×10-4),the shape of oil-gas front is controlled by capillary number.When the capillary number is less than 1.68×10-3,the oil and gas interface is stable.When the capillary number is greater than 2.69×10-2,the oil and gas interface shows viscous fingering.When the capillary number is between 1.68×10-3 and 2.69×10-2,the oil and gas interface becomes capillary fingering.The core flooding experiments results show that for OAGD stable flooding,before the gas breakthrough,higher recovery is obtained in higher gravity number and lower capillary number.In this stage,gravity is predominant in controlling OAGD recovery and the oil recovery could be improved by reducing injection velocity.After gas breakthrough,higher recovery was obtained in lower gravity and higher capillary numbers,which means that the viscous force had a significant influence on the recovery.Increasing gas injection velocity in this stage is an effective measure to improve oil recovery.The low-temperature oxidation number has a good correlation with the recovery and can be used to predict the OAGD recovery.

Prediction of oil recovery in naturally fractured reservoirs subjected to reinfiltration during gravity drainage using a new scaling equation

By comparing numerical simulation results of single-porosity and dual-porosity models,the significant effect of reinfiltration to naturally fractured reservoirs was confirmed.A new governing equation was proposed for oil drainage in a matrix block under the reinfiltration process.Utilizing inspectional analysis,a dimensionless equation suitable for scaling of recovery curves for matrix blocks under reinfiltration has been obtained.By the design of experiments,test cases with different rock and fluid properties were defined to confirm the scope of the presented equation.The defined cases were simulated using a realistic numerical simulation approach.This method can estimate the oil amount getting into the matrix block through reinfiltration,help simulate the oil drainage process in naturally fractured reservoirs accurately,and predict the recovery rate of matrix block in the early to middle periods of production.Using the defined scaling equation in the dual-porosity model can improve the accuracy of the predicted recovery rate.

Gravity Drainage Visualization Experimental Study of Heavy Oil Reservoirs

Liaohe oilfield gravity drainage assisted steam flooding of heavy oil reservoir has made significant development effect, but the drain rule of condensate is unclear in the process of development. Heavy oil drainage microscopic visualization experimental study of using core model of glass etching, drainage process simulation of heavy oil reservoir and its influence factors were analyzed. Its method turns the drainage process of images into computer numerical signal through the image acquisition system, intuitive display flow pattern of drainage, and analyses the influence of homogeneity and the pressure differential regulation of drainage through the experimental data. The experiment results show that condensate around the steam chamber has a corresponding drainage channel, not uniform or diarrhea in the gravity drainage assisted steam flooding process. At the beginning of the drainage channels formation, the instantaneous drainage amount along with the change of pressure difference is not obvious. Instantaneous drainage amount increases with increasing pressure difference in the medium term. It tends to be stable in the later. The time of drainage channels to form homogeneous core is earlier than heterogeneous core. After the drainage channel, differential effects on heterogeneous core than homogeneous core of instantaneous drainage water.

Oil production rate predictions for steam assisted gravity drainage based on high-pressure experiments

Dual-well steam assisted gravity drainage(SAGD) has significant potential for extra-heavy oil recovery.China is conducting two dual-well SAGD pilot projects in the Fengcheng extra-heavy oil reservoir.Quick,direct predictions of the oil production rate by algebraic models rather than complex numerical models are of great importance for designing and adjusting the SAGD operations.A low-pressure scaled physical simulation was previously used to develop two separate theoretical models corresponding to the two different growth stages observed in the SAGD steam chambers,which are the steam chamber rising stage and the steam chamber spreading stage.A high-pressure scaled model experiment is presented here for one dual-well SAGD pattern to further improve the prediction models to reasonably predict oil production rates for full production.Parameters that significantly affect the oil recovery during SAGD were scaled for the model size based on the reservoir characteristics of the Fengcheng reservoir in China.Experimental results show the relationship between the evolution of the steam chamber and the oil production rate during the entire production stage.High-pressure scaled model test was used to improve the gravity drainage models by modifying empirical factors for the rising model and the depletion model.A new division of the SAGD production regime was developed based on the relationship between the oil production rate and the evolution of steam chamber.A method was developed to couple the rising and depletion models to predict oil production rates during the SAGD production,especially during the transition period.The method was validated with experiment data and field data from the literature.The model was then used to predict the oil production rate in the Fengcheng reservoir in China and the Athabasca reservoir in Canada.

Predicting the performance of steam assisted gravity drainage (SAGD) method utilizing artificial neural network (ANN)

As the price of oil decreases,it is becoming increasingly important for oil companies to operate in the most costeffective manner.This problem is especially apparent in Western Canada,where most oil production is dependent on costly enhanced oil recovery(EOR)techniques such as steam-assisted gravity drainage(SAGD).Therefore,the goal of this study is to create an artificial neural network(ANN)that is capable of accurately predicting the ultimate recovery factor of oil reservoirs by steam-assisted gravity drainage(SAGD).The developed ANN model featured over 250 unique entries for oil viscosity,steam injection rate,horizontal permeability,permeability ratio,porosity,reservoir thickness,and steam injection pressure collected from literature.The collected data set was entered through a feed-forward back-propagation neural network to train,validate,and test the model to predict the recovery factor of SAGD method as accurate as possible.Results from this study revealed that the neural network was able to accurately predict recovery factors of selected projects with less than 10%error.When the neural network was exposed to a new simulation data set of 64 points,the predictions were found to have an accuracy of 82%as measured by linear regression.Finally,the feasibility of ANN to predict the recovery performance of one of the most complicated enhanced heavy oil recovery techniques with reasonable accuracy was confirmed.

Study of reservoir properties and operational parameters influencing in the steam assisted gravity drainage process in heavy oil reservoirs by numerical simulation

This study was originally aimed at suggesting a two-dimensional program for the Steam Assisted Gravity Drainage(SAGD)process based on the correlations proposed by Heidari and Pooladi,using the MATLAB software.In fact,the work presented by Chung and Butler was used as the basis for this study.Since the steam chamber development process and the SAGD production performance are functions of reservoir properties and operational parameters,the new model is capable of analyzing the effects of parameters such as height variation at constant length,length variation at constant height,permeability variation,thermal diffusivity coefficient variation and well location on the production rate and the oil recovery among which,the most important one is the thermal diffusivity coefficient analysis.To investigate the accuracy and authenticity of the model outcomes,they were compared with the results obtained by Chung and Butler.The privilege of this method over that proposed by Heidari and Pooladi lies in its ability to investigate the effect of thermal diffusivity coefficient on recovery and analyzing the effect of temperature distribution changes on thickness diffusivity.Based on the observations,results reveal that the proposed model gives more accurate predictions compared to the old model proposed by Chung&Butler.

SAGD Optimization for Heterogeneous Reservoir

This paper demonstrates the use of a commercial simulator as a tool with which to optimize the SAGD （steam-assisted gravity drainage） start-up phase process. The factors affecting the start-up phase are the prime targets. Among the key investigated factors are wellbore geometry effects, reservoir heterogeneity and circulation phase length. Each of the parameters was investigated via steam chamber development observation along the well pair length and the cross sections in the mid, toe and heel areas. In addition, the cumulative recovery in given time, steam-to-oil ratio and CDOR （calendar day oil rate） production data are used to backup the observations produced in the simulated model. Furthermore, an additional component developed during the research is a statistical modification of a layer cake model with which to create a heterogeneous reservoir to represent real reservoir conditions, based on Monte Carlo＇s simulation.

Pad-scale control improves SAGD performance

Steam Assisted Gravity Drainage(SAGD)is widely used in the Athabasca oil sands deposit to recover bitumen.Since the viscosity of bitumen is high at original reservoir conditions,heat is required to lower its viscosity to the point it becomes mobile enough to be recovered under gravity drainage.To heat the reservoir,steam is injected into the formation and thus SAGD is energy intense.Given that the fuel used to generate steam is the largest operating cost,the steam-to-oil ratio is one of the key parameter for evaluating the economics of any SAGD project.Here,the use of dynamic distributed steam injection within a pad of SAGD wellpairs is explored.The results demonstrate that feedback control leads to improvements of the SOR over that of constant pressure.The results show that the controller is able to detect the“sweet spots”(oil zones with better geological properties)in the reservoir and dynamically deliver more steam to that region.Meanwhile,it reduces the steam injection towards relatively worse quality zones to lower the local SOR.

Data-driven model for predicting production periods in the SAGD process

Many studies have analyzed the cumulative production performance in the SAGD(steam assisted gravity drainage)process by data-driven models but a study based on these models for a dynamic analysis of a SAGD production period is still rare.It is important for engineers to define the production period in a SAGD process as it has a stable and high oil production rate and engineers need to reset operational conditions after the production period starts.In this paper,a series of SAGD models were constructed with selected ranges of reservoir properties and operational conditions.Three SAGD production period parameters,including the start date,end date,and duration,are collected based on the simulated production performances.artificial neural network,extreme gradient boosting,light gradient boosting machine,and catboost were constructed to reveal the hidden relationships between twelve input parameters and three output parameters.The data-driven models were trained,tested,and evaluated.The results showed that compared with the other output parameters,the R^(2) of the end date is the highest and it becomes higher with a larger training data set.The extreme gradient boosting algorithm is a better choice to predict the Start date while the artificial neural network generates better prediction for the other two output parameters.This study shows a significant potential in the use of data-driven models for the SAGD production dynamic analysis.The results also serve to support the utilization of the datadriven models as efficient tools for predicting a SAGD production period.