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
Borehole blockage caused by asphaltene deposition is a problem in crude oil production in the Tahe Oilfield, Xinjiang, China. This study has investigated the influences of crude oil compositions, temperature and press...Borehole blockage caused by asphaltene deposition is a problem in crude oil production in the Tahe Oilfield, Xinjiang, China. This study has investigated the influences of crude oil compositions, temperature and pressure on asphaltene deposition. The asphaltene deposition trend of crude oil was studied by saturates, aromatics, resins and asphaltenes (SARA) method, and the turbidity method was applied for the first time to determine the onset of asphaltene flocculation. The results showed that the asphaltene deposition trend of crude oil by the turbidity method was in accordance with that by the SARA method. The asphaltene solubility in crude oil decreased with decreasing temperature and the amount of asphaltene deposits of T739 crude oil (from well T739, Tahe Oilfield) had a maximum value at 60℃. From the PVT results, the bubble point pressure of TH 10403CX crude oil (from well TH10403CX, Tahe Oilfield) at different temperatures can be obtained and the depth at which the maximum asphaltene flocculation would occur in boreholes can be calculated. The crude oil PVT results showed that at 50,90 and 130 ℃, the bubble point pressure of TH 10403CX crude oil was 25.2, 26,4 and 27.0 MPa, respectively. The depth of injecting asphaltene deposition inhibitors for TH10403CX was determined to be 2,700 m.展开更多
A design idea of fidelity sampling cylinder while drilling based on surface nitrogen precharging and supplemented by downhole pressurization was proposed, and the working mode and optimization method of sampling param...A design idea of fidelity sampling cylinder while drilling based on surface nitrogen precharging and supplemented by downhole pressurization was proposed, and the working mode and optimization method of sampling parameters were discussed. The nitrogen chamber in the sampling cylinder functions as an energy storage air cushion, which can supplement the pressure loss caused by temperature change in the sampling process to some extent. The downhole pressurization is to press the sample into the sample chamber as soon as possible, and further increase the pressure of sample to make up for the pressure that the nitrogen chamber cannot provide. Through the analysis of working mode of the sampling fidelity cylinder, the non-ideal gas state equation was used to deduce and calculate the optimal values of fidelity parameters such as pre-charged nitrogen pressure, downhole pressurization amount and sampling volume according to whether the bubble point pressure of the sampling fluid was known and on-site emergency sampling situation. Besides, the influences of ground temperature on fidelity parameters were analyzed, and corresponding correction methods were put forward. The research shows that the fidelity sampling cylinder while drilling can effectively improve the fidelity of the sample. When the formation fluid sample reaches the surface, it can basically ensure that the sample does not change in physical phase state and keeps the same chemical components in the underground formation.展开更多
In a shale gas and oil reservoir,hydrocarbon fluids are stored in organic nanopores with sizes on the order of~1-100 nm.The adsorption,selectivity,and phase behavior of hydrocarbons in the nanopores are crucial for es...In a shale gas and oil reservoir,hydrocarbon fluids are stored in organic nanopores with sizes on the order of~1-100 nm.The adsorption,selectivity,and phase behavior of hydrocarbons in the nanopores are crucial for estimating the gas-in-place and predicting the productivity.In this study,to understand the characteristics of the phase behavior of multicomponent hydrocarbon systems in shale reservoirs,the phase behavior of a CH_(4)/n-C_(4)H_(10)binary mixture in graphite nanopores was investigated by Grand Ca-nonical Monte Carlo(GCMC)molecular simulation.The method for determining the dew-point pressure and bubble-point pressure in the nanopores was explored.The condensation phenomenon was observed owing to the difference in the adsorption selectivities of the hydrocarbon molecules on the nanopore surfaces,and hence the dew-point pressure(and bubble-point pressure)of hydrocarbon mixtures in the nanopores significantly shifted.The GCMC simulations reproduced both the higher and lower bubble-point pressures in nanopores in previous studies.This work highlights the crucial role of the selec-tivity in the phase behavior of hydrocarbons in nanopores.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.
基金National High Technology Research and Development Program of China(No.2013AA064301)National Natural Science Foundation of China (No.51274210)12th National Science and Technology Major Project of the Ministry of Science and Technology of China (No.2011ZX05049-003-001-002)
文摘Borehole blockage caused by asphaltene deposition is a problem in crude oil production in the Tahe Oilfield, Xinjiang, China. This study has investigated the influences of crude oil compositions, temperature and pressure on asphaltene deposition. The asphaltene deposition trend of crude oil was studied by saturates, aromatics, resins and asphaltenes (SARA) method, and the turbidity method was applied for the first time to determine the onset of asphaltene flocculation. The results showed that the asphaltene deposition trend of crude oil by the turbidity method was in accordance with that by the SARA method. The asphaltene solubility in crude oil decreased with decreasing temperature and the amount of asphaltene deposits of T739 crude oil (from well T739, Tahe Oilfield) had a maximum value at 60℃. From the PVT results, the bubble point pressure of TH 10403CX crude oil (from well TH10403CX, Tahe Oilfield) at different temperatures can be obtained and the depth at which the maximum asphaltene flocculation would occur in boreholes can be calculated. The crude oil PVT results showed that at 50,90 and 130 ℃, the bubble point pressure of TH 10403CX crude oil was 25.2, 26,4 and 27.0 MPa, respectively. The depth of injecting asphaltene deposition inhibitors for TH10403CX was determined to be 2,700 m.
基金Supported by the Sinopec Major Science and Technology Project (JPE19007)。
文摘A design idea of fidelity sampling cylinder while drilling based on surface nitrogen precharging and supplemented by downhole pressurization was proposed, and the working mode and optimization method of sampling parameters were discussed. The nitrogen chamber in the sampling cylinder functions as an energy storage air cushion, which can supplement the pressure loss caused by temperature change in the sampling process to some extent. The downhole pressurization is to press the sample into the sample chamber as soon as possible, and further increase the pressure of sample to make up for the pressure that the nitrogen chamber cannot provide. Through the analysis of working mode of the sampling fidelity cylinder, the non-ideal gas state equation was used to deduce and calculate the optimal values of fidelity parameters such as pre-charged nitrogen pressure, downhole pressurization amount and sampling volume according to whether the bubble point pressure of the sampling fluid was known and on-site emergency sampling situation. Besides, the influences of ground temperature on fidelity parameters were analyzed, and corresponding correction methods were put forward. The research shows that the fidelity sampling cylinder while drilling can effectively improve the fidelity of the sample. When the formation fluid sample reaches the surface, it can basically ensure that the sample does not change in physical phase state and keeps the same chemical components in the underground formation.
基金the Promotion of Science(JSPS)for a Grant-in-Aid for Scientific Research A(No.24246148)a Grant-in-Aid for Scientific Research C(No.17K06988).
文摘In a shale gas and oil reservoir,hydrocarbon fluids are stored in organic nanopores with sizes on the order of~1-100 nm.The adsorption,selectivity,and phase behavior of hydrocarbons in the nanopores are crucial for estimating the gas-in-place and predicting the productivity.In this study,to understand the characteristics of the phase behavior of multicomponent hydrocarbon systems in shale reservoirs,the phase behavior of a CH_(4)/n-C_(4)H_(10)binary mixture in graphite nanopores was investigated by Grand Ca-nonical Monte Carlo(GCMC)molecular simulation.The method for determining the dew-point pressure and bubble-point pressure in the nanopores was explored.The condensation phenomenon was observed owing to the difference in the adsorption selectivities of the hydrocarbon molecules on the nanopore surfaces,and hence the dew-point pressure(and bubble-point pressure)of hydrocarbon mixtures in the nanopores significantly shifted.The GCMC simulations reproduced both the higher and lower bubble-point pressures in nanopores in previous studies.This work highlights the crucial role of the selec-tivity in the phase behavior of hydrocarbons in nanopores.