The Pressure-Volume-Temperature(PVT)properties of crude oil are typically determined through laboratory analysis during the early phases of exploration and fielddevelopment.However,due to extensive data required,time-...The Pressure-Volume-Temperature(PVT)properties of crude oil are typically determined through laboratory analysis during the early phases of exploration and fielddevelopment.However,due to extensive data required,time-consuming nature,and high costs,laboratory methods are often not preferred.Machine learning,with its efficiencyand rapid convergence,has emerged as a promising alternative for PVT properties estimation.This study employs the modified particle swarm optimization-based group method of data handling(PSO-GMDH)to develop predictive models for estimating both the oil formation volume factor(OFVF)and bubble point pressure(P_(b)).Data from the Mpyo oil fieldin Uganda were used to create the models.The input parameters included solution gas-oil ratio(R_(s)),oil American Petroleum Institute gravity(API),specificgravity(SG),and reservoir temperature(T).The results demonstrated that PSO-GMDH outperformed backpropagation neural networks(BPNN)and radial basis function neural networks(RBFNN),achieving higher correlation coefficientsand lower prediction errors during training and testing.For OFVF prediction,PSO-GMDH yielded a correlation coefficient(R)of 0.9979(training)and 0.9876(testing),with corresponding root mean square error(RMSE)values of 0.0021 and 0.0099,and mean absolute error(MAE)values of 0.00055 and 0.00256,respectively.For P_(b)prediction,R was 0.9994(training)and 0.9876(testing),with RMSE values of 6.08 and 8.26,and MAE values of 1.35 and 2.63.The study also revealed that R_(s)significantlyimpacts OFVF and P_(b)predictions compared to other input parameters.The models followed physical laws and remained stable,demonstrating that PSO-GMDH is a robust and efficientmethod for predicting OFVF and P_(b),offering a time and cost-effective alternative.展开更多
Oil formation volume factor(OFVF)is considered one of the main parameters required to characterize the crude oil.OFVF is needed in reservoir simulation and prediction of the oil reservoir performance.Existing correlat...Oil formation volume factor(OFVF)is considered one of the main parameters required to characterize the crude oil.OFVF is needed in reservoir simulation and prediction of the oil reservoir performance.Existing correlations apply for specific oils and cannot be extended to other oil types.In addition,big errors were obtained when we applied existing correlations to predict the OFVF.There is a massive need to have a global OFVF correlation that can be used for different oils with less error.The objective of this paper is to develop a new empirical correlation for oil formation volume factor(OFVF)prediction using artificial intelligent techniques(AI)such as;artificial neural network(ANN),adaptive neuro-fuzzy inference system(ANFIS),and support vector machine(SVM).For the first time we changed the ANN model to a white box by extracting the weights and the biases from AI models and form a new empirical equation for OFVF prediction.In this paper we present a new empirical correlation extracted from ANN based on 760 experimental data points for different oils with different compositions.The results obtained showed that the ANN model yielded the highest correlation coefficient(0.997)and lowest average absolute error(less than 1%)for OFVF prediction as a function of the specific gravity of gas,the dissolved gas to oil ratio,the oil specific gravity,and the temperature of the reservoir compared with ANFIS and SVM.The developed empirical equation from the ANN model outperformed the previous empirical correlations and AI models for OFVF prediction.It can be used to predict the OFVF with a high accuracy.展开更多
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...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.展开更多
基金support from the Chinese Scholarship Council(Grant No.2022GXZ005733)。
文摘The Pressure-Volume-Temperature(PVT)properties of crude oil are typically determined through laboratory analysis during the early phases of exploration and fielddevelopment.However,due to extensive data required,time-consuming nature,and high costs,laboratory methods are often not preferred.Machine learning,with its efficiencyand rapid convergence,has emerged as a promising alternative for PVT properties estimation.This study employs the modified particle swarm optimization-based group method of data handling(PSO-GMDH)to develop predictive models for estimating both the oil formation volume factor(OFVF)and bubble point pressure(P_(b)).Data from the Mpyo oil fieldin Uganda were used to create the models.The input parameters included solution gas-oil ratio(R_(s)),oil American Petroleum Institute gravity(API),specificgravity(SG),and reservoir temperature(T).The results demonstrated that PSO-GMDH outperformed backpropagation neural networks(BPNN)and radial basis function neural networks(RBFNN),achieving higher correlation coefficientsand lower prediction errors during training and testing.For OFVF prediction,PSO-GMDH yielded a correlation coefficient(R)of 0.9979(training)and 0.9876(testing),with corresponding root mean square error(RMSE)values of 0.0021 and 0.0099,and mean absolute error(MAE)values of 0.00055 and 0.00256,respectively.For P_(b)prediction,R was 0.9994(training)and 0.9876(testing),with RMSE values of 6.08 and 8.26,and MAE values of 1.35 and 2.63.The study also revealed that R_(s)significantlyimpacts OFVF and P_(b)predictions compared to other input parameters.The models followed physical laws and remained stable,demonstrating that PSO-GMDH is a robust and efficientmethod for predicting OFVF and P_(b),offering a time and cost-effective alternative.
文摘Oil formation volume factor(OFVF)is considered one of the main parameters required to characterize the crude oil.OFVF is needed in reservoir simulation and prediction of the oil reservoir performance.Existing correlations apply for specific oils and cannot be extended to other oil types.In addition,big errors were obtained when we applied existing correlations to predict the OFVF.There is a massive need to have a global OFVF correlation that can be used for different oils with less error.The objective of this paper is to develop a new empirical correlation for oil formation volume factor(OFVF)prediction using artificial intelligent techniques(AI)such as;artificial neural network(ANN),adaptive neuro-fuzzy inference system(ANFIS),and support vector machine(SVM).For the first time we changed the ANN model to a white box by extracting the weights and the biases from AI models and form a new empirical equation for OFVF prediction.In this paper we present a new empirical correlation extracted from ANN based on 760 experimental data points for different oils with different compositions.The results obtained showed that the ANN model yielded the highest correlation coefficient(0.997)and lowest average absolute error(less than 1%)for OFVF prediction as a function of the specific gravity of gas,the dissolved gas to oil ratio,the oil specific gravity,and the temperature of the reservoir compared with ANFIS and SVM.The developed empirical equation from the ANN model outperformed the previous empirical correlations and AI models for OFVF prediction.It can be used to predict the OFVF with a high accuracy.
文摘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.
