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.

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.

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.