It is difficult to identify and predict non-marine deep water sandstone reservoir facies and thickness,using routine seismic analyses in the Xingma area of the western Liaohe sag,due to low dominant frequencies,low si...It is difficult to identify and predict non-marine deep water sandstone reservoir facies and thickness,using routine seismic analyses in the Xingma area of the western Liaohe sag,due to low dominant frequencies,low signal-to-noise ratios,rapid lateral changes and high frequencies of layered inter-bedding.Targeting this problem,four types of frequency spectral decomposition techniques were tested for reservoir prediction.Among these,the non-orthogonal Gabor-Morlet wavelet frequency decomposition method proved to be the best,was implemented directly in our frequency analysis and proved to be adaptable to non-stationary signals as well.The method can overcome the limitations of regular spectral decomposition techniques and highlights local features of reservoir signals.The results are found to be in good agreement with well data.Using this method and a 3-D visualization technology, the distribution of non-marine deep water sandstone reservoirs can be precisely predicted.展开更多
It is very important to determine the extent of the fractured zone through which water can flow before coal mining under the water bodies.This paper deals with methods to obtain information about overburden rock failu...It is very important to determine the extent of the fractured zone through which water can flow before coal mining under the water bodies.This paper deals with methods to obtain information about overburden rock failure and the development of the fractured zone while coal mining in Xin'an Coal Mine.The risk of water inrush in this mine is great because 40%of the mining area is under the Xiaolangdi reservoir.Numerical simulations combined with geophysical methods were used in this paper to obtain the development law of the fractured zone under different mining conditions.The comprehensive geophysical method described in this paper has been demonstrated to accurately predict the height of the water-flow fractured zone.Results from the new model, which created from the results of numerical simulations and field measurements,were successfully used for making decisions in the Xin'an Coal Mine when mining under the Xiaolangdi Reservoir.Industrial scale experiments at the number 11201,14141 and 14191 working faces were safely carried out.These achievements provide a successful background for the evaluation and application of coal mining under large reservoirs.展开更多
Since the oil production of single well in water flooding reservoir varies greatly and is hard to predict, an oil production prediction method of single well based on temporal convolutional network(TCN) is proposed an...Since the oil production of single well in water flooding reservoir varies greatly and is hard to predict, an oil production prediction method of single well based on temporal convolutional network(TCN) is proposed and verified. This method is started from data processing, the correspondence between water injectors and oil producers is determined according to the influence radius of the water injectors, the influence degree of a water injector on an oil producer in the month concerned is added as a model feature, and a Random Forest(RF) model is built to fill the dynamic data of water flooding. The single well history is divided into 4 stages according to its water cut, that is, low water cut, middle water cut, high water cut and extra-high water cut stages. In each stage, a TCN based prediction model is established, hyperparameters of the model are optimized by the Sparrow Search Algorithm(SSA). Finally, the models of the 4 stages are integrated into one whole-life model of the well for production prediction. The application of this method in Daqing Oilfield, NE China shows that:(1) Compared with conventional data processing methods, the data obtained by this processing method are more close to the actual production, and the data set obtained is more authentic and complete.(2) The TCN model has higher prediction accuracy than other 11 models such as Long Short Term Memory(LSTM).(3) Compared with the conventional full-life-cycle models, the model of integrated stages can significantly reduce the error of production prediction.展开更多
The unfrozen water content(UWC)of rocks at low temperature is an important index for evaluating the stability of the rock engineering in cold regions and artificial freezing engineering.This study addresses a new meth...The unfrozen water content(UWC)of rocks at low temperature is an important index for evaluating the stability of the rock engineering in cold regions and artificial freezing engineering.This study addresses a new method to estimate the UWC of saturated sandstones at low temperature by using the ultrasonic velocity.Ultrasonic velocity variations can be divided into the normal temperature stage(20 to 0℃),quick phase transition stage(0 to-5℃)and slow phase transition stage(-5 to-25℃).Most increment of ultrasonic velocity is completed in the quick phase transition stage and then turns to be almost a constant in the slow phase transition stage.In addition,the UWC is also measured by using nuclear magnetic resonance(NMR)technology.It is validated that the ultrasonic velocity and UWC have a similar change law against freezing and thawing temperatures.The WE(weighted equation)model is appropriate to estimate the UWC of saturated sandstones,in which the parameters have been accurately determined rather than by data fitting.In addition,a linear relationship between UWC and ultrasonic velocity is built based on pore ice crystallization theory.It is evidenced that this linear function can be adopted to estimate the UWC at any freezing temperature by using P-wave velocity,which is simple,practical,and accurate enough compared with the WE model.展开更多
The Huai River Basin is a unique area in P.R.China with the highest densities of population and water projects.It is also subject to the most serious water pollution.We proposed a distributional SWAT(Soil and Water As...The Huai River Basin is a unique area in P.R.China with the highest densities of population and water projects.It is also subject to the most serious water pollution.We proposed a distributional SWAT(Soil and Water Assessment Tool) model coupled with a water quality-quantity balance model to evaluate dam impacts on river flow regimes and water quality in the middle and upper reaches of the Huai River Basin.We calibrated and validated the SWAT model with data from 29 selected cross-sections in four typical years(1971,1981,1991 and 1999) and used scenario analysis to compensate for the unavailability of historical data regarding uninterrupted river flows before dam and floodgate construction,a problem of prediction for ungauged basins.The results indicate that dam and floodgate operations tended to reduce runoff,decrease peak value and shift peaking time.The contribution of water projects to river water quality deterioration in the concerned river system was between 0 to 40%,while pollutant discharge contributed to 60% to 100% of the water pollution.Pollution control should therefore be the key to the water quality rehabilitation in the Huai River Basin.展开更多
During deep water oil well testing, the low temperature environment is easy to cause wax precipitation, which affects the normal operation of the test and increases operating costs and risks. Therefore, a numerical me...During deep water oil well testing, the low temperature environment is easy to cause wax precipitation, which affects the normal operation of the test and increases operating costs and risks. Therefore, a numerical method for predicting the wax precipitation region in oil strings was proposed based on the temperature and pressure fields of deep water test string and the wax precipitation calculation model. And the factors affecting the wax precipitation region were analyzed. The results show that: the wax precipitation region decreases with the increase of production rate, and increases with the decrease of geothermal gradient, increase of water depth and drop of water-cut of produced fluid, and increases slightly with the increase of formation pressure. Due to the effect of temperature and pressure fields, wax precipitation region is large in test strings at the beginning of well production. Wax precipitation region gradually increases with the increase of shut-in time. These conclusions can guide wax prevention during the testing of deep water oil well, to ensure the success of the test.展开更多
A deep learning method for predicting oil field production at ultra-high water cut stage from the existing oil field production data was presented,and the experimental verification and application effect analysis were...A deep learning method for predicting oil field production at ultra-high water cut stage from the existing oil field production data was presented,and the experimental verification and application effect analysis were carried out.Since the traditional Fully Connected Neural Network(FCNN)is incapable of preserving the correlation of time series data,the Long Short-Term Memory(LSTM)network,which is a kind of Recurrent Neural Network(RNN),was utilized to establish a model for oil field production prediction.By this model,oil field production can be predicted from the relationship between oil production index and its influencing factors and the trend and correlation of oil production over time.Production data of a medium and high permeability sandstone oilfield in China developed by water flooding was used to predict its production at ultra-high water cut stage,and the results were compared with the results from the traditional FCNN and water drive characteristic curves.The LSTM based on deep learning has higher precision,and gives more accurate production prediction for complex time series in oil field production.The LSTM model was used to predict the monthly oil production of another two oil fields.The prediction results are good,which verifies the versatility of the method.展开更多
In order to realize the prediction of a chaotic time series of mine water discharge,an approach incorporating phase space reconstruction theory and statistical learning theory was studied.A differential entropy ratio ...In order to realize the prediction of a chaotic time series of mine water discharge,an approach incorporating phase space reconstruction theory and statistical learning theory was studied.A differential entropy ratio method was used to determine embedding parameters to reconstruct the phase space.We used a multi-layer adaptive best-fitting parameter search algorithm to estimate the LS-SVM optimal parameters which were adopted to construct a LS-SVM prediction model for the mine water chaotic time series.The results show that the simulation performance of a single-step prediction based on this LS-SVM model is markedly superior to that based on a RBF model.The multi-step prediction results based on LS-SVM model can reflect the development of mine water discharge and can be used for short-term forecasting of mine water discharge.展开更多
A numerical method is proposed for predicting six degree of freedom ship motion with green water on deck.Numerical results are presented which show the dynamic effect of water on deck on the ship motion.
Water content in output crude oil is hard to measure precisely because of wide range of dielectric coefficient of crude oil caused by injected dehydrating and demulsifying agents.The method to reduce measurement error...Water content in output crude oil is hard to measure precisely because of wide range of dielectric coefficient of crude oil caused by injected dehydrating and demulsifying agents.The method to reduce measurement error of water content in crude oil proposed in this paper is based on switching measuring ranges of on-line water content analyzer automatically.Measuring precision on data collected from oil field and analyzed by in-field operators can be impressively improved by using back propogation （BP） neural network to predict water content in output crude oil.Application results show that the difficulty in accurately measuring water-oil content ratio can be solved effectively through this combination of on-line measuring range automatic switching and real time prediction,as this method has been tested repeatedly on-site in oil fields with satisfactory prediction results.展开更多
The prediction of the stress field of deep-buried tunnels is a fundamental problem for scientists and engineers. In this study, the authors put forward a systematic solution for this problem. Databases from the World ...The prediction of the stress field of deep-buried tunnels is a fundamental problem for scientists and engineers. In this study, the authors put forward a systematic solution for this problem. Databases from the World Stress Map and the Crustal Stress of China, and previous research findings can offer prediction of stress orientations in an engineering area. At the same time, the Andersonian theory can be used to analyze the possible stress orientation of a region. With limited in-situ stress measurements, the Hock-Brown Criterion can be used to estimate the strength of rock mass in an area of interest by utilizing the geotechnical investigation data, and the modified Sheorey＇s model can subsequently be employed to predict the areas＇ stress profile, without stress data, by taking the existing in-situ stress measurements as input parameters. In this paper, a case study was used to demonstrate the application of this systematic solution. The planned Kohala hydropower plant is located on the western edge of Qinghai-Tibet Plateau. Three hydro-fracturing stress measurement campaigns indicated that the stress state of the area is SH - Sh 〉 Sv or SH 〉Sv 〉 Sh. The measured orientation of Sn is NEE （N70.3°-89°E）, and the regional orientation of SH from WSM is NE, which implies that the stress orientation of shallow crust may be affected by landforms. The modified Sheorey model was utilized to predict the stress profile along the water sewage tunnel for the plant. Prediction results show that the maximum and minimum horizontal principal stres- ses of the points with the greatest burial depth were up to 56.70 and 40.14 MPa, respectively, and the stresses of areas with a burial depth of greater than 500 m were higher. Based on the predicted stress data, large deformations of the rock mass surrounding water conveyance tunnels were analyzed. Results showed that the large deformations will occur when the burial depth exceeds 300 m. When the burial depth is beyond 800 m, serious squeezing deformations will occur in the surrounding rock masses, thus requiring more attention in the design and construction. Based on the application efficiency in this case study, this prediction method proposed in this paper functions accurately.展开更多
文摘It is difficult to identify and predict non-marine deep water sandstone reservoir facies and thickness,using routine seismic analyses in the Xingma area of the western Liaohe sag,due to low dominant frequencies,low signal-to-noise ratios,rapid lateral changes and high frequencies of layered inter-bedding.Targeting this problem,four types of frequency spectral decomposition techniques were tested for reservoir prediction.Among these,the non-orthogonal Gabor-Morlet wavelet frequency decomposition method proved to be the best,was implemented directly in our frequency analysis and proved to be adaptable to non-stationary signals as well.The method can overcome the limitations of regular spectral decomposition techniques and highlights local features of reservoir signals.The results are found to be in good agreement with well data.Using this method and a 3-D visualization technology, the distribution of non-marine deep water sandstone reservoirs can be precisely predicted.
基金the National Basic Research Program of China(No.2007CB209401) for its financial support
文摘It is very important to determine the extent of the fractured zone through which water can flow before coal mining under the water bodies.This paper deals with methods to obtain information about overburden rock failure and the development of the fractured zone while coal mining in Xin'an Coal Mine.The risk of water inrush in this mine is great because 40%of the mining area is under the Xiaolangdi reservoir.Numerical simulations combined with geophysical methods were used in this paper to obtain the development law of the fractured zone under different mining conditions.The comprehensive geophysical method described in this paper has been demonstrated to accurately predict the height of the water-flow fractured zone.Results from the new model, which created from the results of numerical simulations and field measurements,were successfully used for making decisions in the Xin'an Coal Mine when mining under the Xiaolangdi Reservoir.Industrial scale experiments at the number 11201,14141 and 14191 working faces were safely carried out.These achievements provide a successful background for the evaluation and application of coal mining under large reservoirs.
基金Major Unified Construction Project of Petro China(2019-40210-000020-02)。
文摘Since the oil production of single well in water flooding reservoir varies greatly and is hard to predict, an oil production prediction method of single well based on temporal convolutional network(TCN) is proposed and verified. This method is started from data processing, the correspondence between water injectors and oil producers is determined according to the influence radius of the water injectors, the influence degree of a water injector on an oil producer in the month concerned is added as a model feature, and a Random Forest(RF) model is built to fill the dynamic data of water flooding. The single well history is divided into 4 stages according to its water cut, that is, low water cut, middle water cut, high water cut and extra-high water cut stages. In each stage, a TCN based prediction model is established, hyperparameters of the model are optimized by the Sparrow Search Algorithm(SSA). Finally, the models of the 4 stages are integrated into one whole-life model of the well for production prediction. The application of this method in Daqing Oilfield, NE China shows that:(1) Compared with conventional data processing methods, the data obtained by this processing method are more close to the actual production, and the data set obtained is more authentic and complete.(2) The TCN model has higher prediction accuracy than other 11 models such as Long Short Term Memory(LSTM).(3) Compared with the conventional full-life-cycle models, the model of integrated stages can significantly reduce the error of production prediction.
基金National Natural Science Foundation of China(Nos.42072300 and 41702291)the Project of Natural Science Foundation of Hubei Province(No.2021CFA094).
文摘The unfrozen water content(UWC)of rocks at low temperature is an important index for evaluating the stability of the rock engineering in cold regions and artificial freezing engineering.This study addresses a new method to estimate the UWC of saturated sandstones at low temperature by using the ultrasonic velocity.Ultrasonic velocity variations can be divided into the normal temperature stage(20 to 0℃),quick phase transition stage(0 to-5℃)and slow phase transition stage(-5 to-25℃).Most increment of ultrasonic velocity is completed in the quick phase transition stage and then turns to be almost a constant in the slow phase transition stage.In addition,the UWC is also measured by using nuclear magnetic resonance(NMR)technology.It is validated that the ultrasonic velocity and UWC have a similar change law against freezing and thawing temperatures.The WE(weighted equation)model is appropriate to estimate the UWC of saturated sandstones,in which the parameters have been accurately determined rather than by data fitting.In addition,a linear relationship between UWC and ultrasonic velocity is built based on pore ice crystallization theory.It is evidenced that this linear function can be adopted to estimate the UWC at any freezing temperature by using P-wave velocity,which is simple,practical,and accurate enough compared with the WE model.
基金Funded by the Key Project of International Cooperation of the Natural Science Foundation of China (No. 40721140020)the Key Project of the Natural Science Foundation of China (No. 40730632)
文摘The Huai River Basin is a unique area in P.R.China with the highest densities of population and water projects.It is also subject to the most serious water pollution.We proposed a distributional SWAT(Soil and Water Assessment Tool) model coupled with a water quality-quantity balance model to evaluate dam impacts on river flow regimes and water quality in the middle and upper reaches of the Huai River Basin.We calibrated and validated the SWAT model with data from 29 selected cross-sections in four typical years(1971,1981,1991 and 1999) and used scenario analysis to compensate for the unavailability of historical data regarding uninterrupted river flows before dam and floodgate construction,a problem of prediction for ungauged basins.The results indicate that dam and floodgate operations tended to reduce runoff,decrease peak value and shift peaking time.The contribution of water projects to river water quality deterioration in the concerned river system was between 0 to 40%,while pollutant discharge contributed to 60% to 100% of the water pollution.Pollution control should therefore be the key to the water quality rehabilitation in the Huai River Basin.
基金Supported by the National Key Basic Research and Development Program(973 Program),China(2015CB251205)
文摘During deep water oil well testing, the low temperature environment is easy to cause wax precipitation, which affects the normal operation of the test and increases operating costs and risks. Therefore, a numerical method for predicting the wax precipitation region in oil strings was proposed based on the temperature and pressure fields of deep water test string and the wax precipitation calculation model. And the factors affecting the wax precipitation region were analyzed. The results show that: the wax precipitation region decreases with the increase of production rate, and increases with the decrease of geothermal gradient, increase of water depth and drop of water-cut of produced fluid, and increases slightly with the increase of formation pressure. Due to the effect of temperature and pressure fields, wax precipitation region is large in test strings at the beginning of well production. Wax precipitation region gradually increases with the increase of shut-in time. These conclusions can guide wax prevention during the testing of deep water oil well, to ensure the success of the test.
基金Supported by China National Science and Technology Major Project(2016ZX05016-006)
文摘A deep learning method for predicting oil field production at ultra-high water cut stage from the existing oil field production data was presented,and the experimental verification and application effect analysis were carried out.Since the traditional Fully Connected Neural Network(FCNN)is incapable of preserving the correlation of time series data,the Long Short-Term Memory(LSTM)network,which is a kind of Recurrent Neural Network(RNN),was utilized to establish a model for oil field production prediction.By this model,oil field production can be predicted from the relationship between oil production index and its influencing factors and the trend and correlation of oil production over time.Production data of a medium and high permeability sandstone oilfield in China developed by water flooding was used to predict its production at ultra-high water cut stage,and the results were compared with the results from the traditional FCNN and water drive characteristic curves.The LSTM based on deep learning has higher precision,and gives more accurate production prediction for complex time series in oil field production.The LSTM model was used to predict the monthly oil production of another two oil fields.The prediction results are good,which verifies the versatility of the method.
基金supported by the Science and Research projects for Ph.D. candidates in the faculty of Xuzhou Normal University (No.08XLR12)Natural Science Foundation of Xuzhou Normal University (No.09XLA10)
文摘In order to realize the prediction of a chaotic time series of mine water discharge,an approach incorporating phase space reconstruction theory and statistical learning theory was studied.A differential entropy ratio method was used to determine embedding parameters to reconstruct the phase space.We used a multi-layer adaptive best-fitting parameter search algorithm to estimate the LS-SVM optimal parameters which were adopted to construct a LS-SVM prediction model for the mine water chaotic time series.The results show that the simulation performance of a single-step prediction based on this LS-SVM model is markedly superior to that based on a RBF model.The multi-step prediction results based on LS-SVM model can reflect the development of mine water discharge and can be used for short-term forecasting of mine water discharge.
文摘A numerical method is proposed for predicting six degree of freedom ship motion with green water on deck.Numerical results are presented which show the dynamic effect of water on deck on the ship motion.
基金Sponsored by the Basic Research Fundation of Beijing Institute of Technology (200705422009)
文摘Water content in output crude oil is hard to measure precisely because of wide range of dielectric coefficient of crude oil caused by injected dehydrating and demulsifying agents.The method to reduce measurement error of water content in crude oil proposed in this paper is based on switching measuring ranges of on-line water content analyzer automatically.Measuring precision on data collected from oil field and analyzed by in-field operators can be impressively improved by using back propogation （BP） neural network to predict water content in output crude oil.Application results show that the difficulty in accurately measuring water-oil content ratio can be solved effectively through this combination of on-line measuring range automatic switching and real time prediction,as this method has been tested repeatedly on-site in oil fields with satisfactory prediction results.
基金provided by the National Natural Science Foundation of China – China (No. 41274100)the Fundamental Research Fund for State Level Scientific Institutes (No. ZDJ2012-20)
文摘The prediction of the stress field of deep-buried tunnels is a fundamental problem for scientists and engineers. In this study, the authors put forward a systematic solution for this problem. Databases from the World Stress Map and the Crustal Stress of China, and previous research findings can offer prediction of stress orientations in an engineering area. At the same time, the Andersonian theory can be used to analyze the possible stress orientation of a region. With limited in-situ stress measurements, the Hock-Brown Criterion can be used to estimate the strength of rock mass in an area of interest by utilizing the geotechnical investigation data, and the modified Sheorey＇s model can subsequently be employed to predict the areas＇ stress profile, without stress data, by taking the existing in-situ stress measurements as input parameters. In this paper, a case study was used to demonstrate the application of this systematic solution. The planned Kohala hydropower plant is located on the western edge of Qinghai-Tibet Plateau. Three hydro-fracturing stress measurement campaigns indicated that the stress state of the area is SH - Sh 〉 Sv or SH 〉Sv 〉 Sh. The measured orientation of Sn is NEE （N70.3°-89°E）, and the regional orientation of SH from WSM is NE, which implies that the stress orientation of shallow crust may be affected by landforms. The modified Sheorey model was utilized to predict the stress profile along the water sewage tunnel for the plant. Prediction results show that the maximum and minimum horizontal principal stres- ses of the points with the greatest burial depth were up to 56.70 and 40.14 MPa, respectively, and the stresses of areas with a burial depth of greater than 500 m were higher. Based on the predicted stress data, large deformations of the rock mass surrounding water conveyance tunnels were analyzed. Results showed that the large deformations will occur when the burial depth exceeds 300 m. When the burial depth is beyond 800 m, serious squeezing deformations will occur in the surrounding rock masses, thus requiring more attention in the design and construction. Based on the application efficiency in this case study, this prediction method proposed in this paper functions accurately.
