Knowledge of petroleum fluid properties is crucial for the study of reservoirs and their development. Estimation of reserves in an oil reservoir or determination of its performance and economics requires a good knowle...Knowledge of petroleum fluid properties is crucial for the study of reservoirs and their development. Estimation of reserves in an oil reservoir or determination of its performance and economics requires a good knowledge of the fluid physical properties. Bubble point pressure, gas solubility and viscosity of oils are the most important parameters in use for petroleum and chemical engineers. In this study a simple-to-use, straight-forward mathematical model was correlated on a set of 94 crude oil data. Three correlations were achieved based on an exponential regression, which were different from conventional empirical correlations, and were evaluated against 12 laboratory data other than those used for the regression. It is concluded that the new exponential equation is of higher precision and accuracy than the conventional correlations and is a more convenient mathematical formulation.展开更多
Knowledge about reservoir fluid properties such as bubble point pressure(Pb)plays a vital role in improving reliability of oil reservoir simulation.In this work,hybrid of swarm intelligence and artificial neural netwo...Knowledge about reservoir fluid properties such as bubble point pressure(Pb)plays a vital role in improving reliability of oil reservoir simulation.In this work,hybrid of swarm intelligence and artificial neural network(ANN)as a robust and effective method was executed to determine the Pb of crude oil samples.In addition,the exactly precise Pb data samples reported in the literatures were employed to create and validate the PSO-ANN model.To prove and depict the reliability of the smart model developed in this study for estimating Pb of crude oils,the conventional approaches were applied on the same data set.Based on the results generated by PSO-ANN model and other conventional methods and equation of states(EOS),the PSO-ANN model is a reliable and accurate approach for estimating Pb of crude oils.This is certified by high value of correlation coefficient(R2)and insignificant value of average absolute relative deviation(AARD%)which are obtained from PSO-ANN outputs.Outcomes of this study could help reservoir engineers to have better understanding of reservoir fluid behavior in absence of reliable and experimental data samples.展开更多
Bubble point pressure is one of the most important pressureevolumeetemperature properties of crude oil,and it plays an important role in reservoir and production engineering calculations.It can be precisely determined...Bubble point pressure is one of the most important pressureevolumeetemperature properties of crude oil,and it plays an important role in reservoir and production engineering calculations.It can be precisely determined experimentally.Although,experimental methods present valid and reliable results,they are expensive,time-consuming,and require much care when taking test samples.Some equations of state and empirical correlations can be used as alternative methods to estimate reservoir fluid properties(e.g.,bubble point pressure);however,these methods have a number of limitations.In the present study,a novel numerical model based on artificial neural network(ANN)is proposed for the prediction of bubble point pressure as a function of solution gaseoil ratio,reservoir temperature,oil gravity(API),and gas specific gravity in petroleum systems.The model was developed and evaluated using 760 experimental data sets gathered from oil fields around the world.An optimization process was performed on networks with different structures.Based on the obtained results,a network with one hidden layer and six neurons was observed to be associated with the highest efficiency for predicting bubble point pressure.The obtained ANN model was found to be reliable for the prediction of bubble point pressure of crude oils with solution gaseoil ratios in the range of 8.61e3298.66 SCF/STB,temperatures between 74 and 341.6F,oil gravity values of 6e56.8 API and gas gravity values between 0.521 and 3.444.The performance of the developed model was compared against those of several well-known predictive empirical correlations using statistical and graphical error analyses.The results showed that the proposed ANN model outperforms all of the studied empirical correlations significantly and provides predictions in acceptable agreement with experimental data.展开更多
The transparent open box(TOB)learning network algorithm offers an alternative approach to the lack of transparency provided by most machine-learning algorithms.It provides the exact calculations and relationships amon...The transparent open box(TOB)learning network algorithm offers an alternative approach to the lack of transparency provided by most machine-learning algorithms.It provides the exact calculations and relationships among the underlying input variables of the datasets to which it is applied.It also has the capability to achieve credible and auditable levels of prediction accuracy to complex,non-linear datasets,typical of those encountered in the oil and gas sector,highlighting the potential for underfitting and overfitting.The algorithm is applied here to predict bubble-point pressure from a published PVT dataset of 166 data records involving four easy-tomeasure variables(reservoir temperature,gas-oil ratio,oil gravity,gas density relative to air)with uneven,and in parts,sparse data coverage.The TOB network demonstrates high-prediction accuracy for this complex system,although it predictions applied to the full dataset are outperformed by an artificial neural network(ANN).However,the performance of the TOB algorithm reveals the risk of overfitting in the sparse areas of the dataset and achieves a prediction performance that matches the ANN algorithm where the underlying data population is adequate.The high levels of transparency and its inhibitions to overfitting enable the TOB learning network to provide complementary information about the underlying dataset to that provided by traditional machine learning algorithms.This makes them suitable for application in parallel with neural-network algorithms,to overcome their black-box tendencies,and for benchmarking the prediction performance of other machine learning algorithms.展开更多
文摘Knowledge of petroleum fluid properties is crucial for the study of reservoirs and their development. Estimation of reserves in an oil reservoir or determination of its performance and economics requires a good knowledge of the fluid physical properties. Bubble point pressure, gas solubility and viscosity of oils are the most important parameters in use for petroleum and chemical engineers. In this study a simple-to-use, straight-forward mathematical model was correlated on a set of 94 crude oil data. Three correlations were achieved based on an exponential regression, which were different from conventional empirical correlations, and were evaluated against 12 laboratory data other than those used for the regression. It is concluded that the new exponential equation is of higher precision and accuracy than the conventional correlations and is a more convenient mathematical formulation.
文摘Knowledge about reservoir fluid properties such as bubble point pressure(Pb)plays a vital role in improving reliability of oil reservoir simulation.In this work,hybrid of swarm intelligence and artificial neural network(ANN)as a robust and effective method was executed to determine the Pb of crude oil samples.In addition,the exactly precise Pb data samples reported in the literatures were employed to create and validate the PSO-ANN model.To prove and depict the reliability of the smart model developed in this study for estimating Pb of crude oils,the conventional approaches were applied on the same data set.Based on the results generated by PSO-ANN model and other conventional methods and equation of states(EOS),the PSO-ANN model is a reliable and accurate approach for estimating Pb of crude oils.This is certified by high value of correlation coefficient(R2)and insignificant value of average absolute relative deviation(AARD%)which are obtained from PSO-ANN outputs.Outcomes of this study could help reservoir engineers to have better understanding of reservoir fluid behavior in absence of reliable and experimental data samples.
文摘Bubble point pressure is one of the most important pressureevolumeetemperature properties of crude oil,and it plays an important role in reservoir and production engineering calculations.It can be precisely determined experimentally.Although,experimental methods present valid and reliable results,they are expensive,time-consuming,and require much care when taking test samples.Some equations of state and empirical correlations can be used as alternative methods to estimate reservoir fluid properties(e.g.,bubble point pressure);however,these methods have a number of limitations.In the present study,a novel numerical model based on artificial neural network(ANN)is proposed for the prediction of bubble point pressure as a function of solution gaseoil ratio,reservoir temperature,oil gravity(API),and gas specific gravity in petroleum systems.The model was developed and evaluated using 760 experimental data sets gathered from oil fields around the world.An optimization process was performed on networks with different structures.Based on the obtained results,a network with one hidden layer and six neurons was observed to be associated with the highest efficiency for predicting bubble point pressure.The obtained ANN model was found to be reliable for the prediction of bubble point pressure of crude oils with solution gaseoil ratios in the range of 8.61e3298.66 SCF/STB,temperatures between 74 and 341.6F,oil gravity values of 6e56.8 API and gas gravity values between 0.521 and 3.444.The performance of the developed model was compared against those of several well-known predictive empirical correlations using statistical and graphical error analyses.The results showed that the proposed ANN model outperforms all of the studied empirical correlations significantly and provides predictions in acceptable agreement with experimental data.
文摘The transparent open box(TOB)learning network algorithm offers an alternative approach to the lack of transparency provided by most machine-learning algorithms.It provides the exact calculations and relationships among the underlying input variables of the datasets to which it is applied.It also has the capability to achieve credible and auditable levels of prediction accuracy to complex,non-linear datasets,typical of those encountered in the oil and gas sector,highlighting the potential for underfitting and overfitting.The algorithm is applied here to predict bubble-point pressure from a published PVT dataset of 166 data records involving four easy-tomeasure variables(reservoir temperature,gas-oil ratio,oil gravity,gas density relative to air)with uneven,and in parts,sparse data coverage.The TOB network demonstrates high-prediction accuracy for this complex system,although it predictions applied to the full dataset are outperformed by an artificial neural network(ANN).However,the performance of the TOB algorithm reveals the risk of overfitting in the sparse areas of the dataset and achieves a prediction performance that matches the ANN algorithm where the underlying data population is adequate.The high levels of transparency and its inhibitions to overfitting enable the TOB learning network to provide complementary information about the underlying dataset to that provided by traditional machine learning algorithms.This makes them suitable for application in parallel with neural-network algorithms,to overcome their black-box tendencies,and for benchmarking the prediction performance of other machine learning algorithms.
