In order to accurately predict the productivity of herringbone multilateral well,a new productivity prediction model was founded.And based on this model,orthogonal test and multiple factor variance analysis were appli...In order to accurately predict the productivity of herringbone multilateral well,a new productivity prediction model was founded.And based on this model,orthogonal test and multiple factor variance analysis were applied to study optimization design of herringbone multilateral well.According to the characteristics of herringbone multilateral well,by using pressure superposition and mirror image reflection theory,the coupled model of herringbone multilateral well was developed on the basis of a three-dimensional pseudo-pressure distribution model for horizontal wells.The model was formulated in consideration of friction loss,acceleration loss of the wellbore and mixed loss at the confluence of main wellbore and branched one.After mathematical simulation on productivity of the herringbone multilateral well with the coupled model,the effects of well configuration on productivity were analyzed.The results show that lateral number is the most important factor,length of main wellbore and length of branched wellbore are the secondary ones,angle between main and branched one has the least influence.展开更多
In the early time of oilfield development, insufficient production data and unclear understanding of oil production presented a challenge to reservoir engineers in devising effective development plans. To address this...In the early time of oilfield development, insufficient production data and unclear understanding of oil production presented a challenge to reservoir engineers in devising effective development plans. To address this challenge, this study proposes a method using data mining technology to search for similar oil fields and predict well productivity. A query system of 135 analogy parameters is established based on geological and reservoir engineering research, and the weight values of these parameters are calculated using a data algorithm to establish an analogy system. The fuzzy matter-element algorithm is then used to calculate the similarity between oil fields, with fields having similarity greater than 70% identified as similar oil fields. Using similar oil fields as sample data, 8 important factors affecting well productivity are identified using the Pearson coefficient and mean decrease impurity(MDI) method. To establish productivity prediction models, linear regression(LR), random forest regression(RF), support vector regression(SVR), backpropagation(BP), extreme gradient boosting(XGBoost), and light gradient boosting machine(Light GBM) algorithms are used. Their performance is evaluated using the coefficient of determination(R^(2)), explained variance score(EV), mean squared error(MSE), and mean absolute error(MAE) metrics. The Light GBM model is selected to predict the productivity of 30 wells in the PL field with an average error of only 6.31%, which significantly improves the accuracy of the productivity prediction and meets the application requirements in the field. Finally, a software platform integrating data query,oil field analogy, productivity prediction, and knowledge base is established to identify patterns in massive reservoir development data and provide valuable technical references for new reservoir development.展开更多
In order to overcome the deficiencies of current methods for the prediction of the productivity of shale gas hor-izontal wells after fracturing,a new sophisticated approach is proposed in this study.This new model stem...In order to overcome the deficiencies of current methods for the prediction of the productivity of shale gas hor-izontal wells after fracturing,a new sophisticated approach is proposed in this study.This new model stems from the combination several techniques,namely,artificial neural network(ANN),particle swarm optimization(PSO),Imperialist Competitive Algorithms(ICA),and Ant Clony Optimization(ACO).These are properly implemented by using the geological and engineering parameters collected from 317 wells.The results show that the optimum PSO-ANN model has a high accuracy,obtaining a R2 of 0.847 on the testing.The partial dependence plots(PDP)indicate that liquid consumption intensity and the proportion of quartz sand are the two most sensitive factors affecting the model’s performance.展开更多
Net primary productivity(NPP)is the net accumulation of organic matter by vegetation through photosynthesis and serves as a key indicator for exploring vegetation responses to climate change.Considering the remote and...Net primary productivity(NPP)is the net accumulation of organic matter by vegetation through photosynthesis and serves as a key indicator for exploring vegetation responses to climate change.Considering the remote and local impacts of soil heat capacities on vegetation growth through pathways of atmospheric circulation and land–atmosphere interaction,this paper develops a statistical prediction model for NPP from April to June(AMJ)across the middle-to-high latitudes of Eurasia.The model introduces two physically meaningful predictors:the snow water equivalent(SWE)from February to March(FM)over central Europe and the FM local soil temperature(ST).The positive phase of FM SWE triggers anomalous eastward-propagating Rossby waves,leading to an anomalous low-pressure system and cooling in the middle-to-high latitudes of Eurasia.This effect persists into spring through snow feedback to the atmosphere and affects subsequent NPP changes.The ST is closely related to the AMJ temperature and precipitation.With positive ST anomalies,the AMJ temperature and precipitation exhibit an east–west dipole anomaly distribution in this region.The single-factor prediction scheme using ST as the predictor is much better than using SWE as the predictor.Independent validation results from 2009 to 2014 demonstrate that the ST scheme alone has good predictive performance for the spatial distribution and interannual variability of NPP.The predictive skills of the multi-factor prediction schemes can be improved by about 13%if the ST predictor is included.The findings confirm that local ST is a predictor that must be included for NPP prediction.展开更多
Pressure control in deep shale gas horizontal wells can reduce the stress sensitivity of hydraulic fractures and improve the estimated ultimate recovery(EUR).In this study,a hydraulic fracture stress sensitivity model...Pressure control in deep shale gas horizontal wells can reduce the stress sensitivity of hydraulic fractures and improve the estimated ultimate recovery(EUR).In this study,a hydraulic fracture stress sensitivity model is proposed to characterize the effect of pressure drop rate on fracture permeability.Furthermore,a production prediction model is introduced accounting for a non-uniform hydraulic fracture conductivity distribution.The results reveal that increasing the fracture conductivity leads to a rapid daily production increase in the early stages.However,above 0.50 D·cm,a further increase in the fracture conductivity has a limited effect on shale gas production growth.The initial production is lower under pressure-controlled conditions than that under pressure-release.For extended pressure control durations,the cumulative production initially increases and then decreases.For a fracture conductivity of 0.10 D·cm,the increase in production output under controlled-pressure conditions is~35%.For representative deep shale gas wells(Southern Sichuan,China),if the pressure drop rate under controlled-pressure conditions is reduced from 0.19 to 0.04 MPa/d,the EUR increase for 5 years of pressure-controlled production is 41.0 million,with an increase percentage of~29%.展开更多
Based on regional CBM geological characteristics and drainage data of three typical Coalbed Methane(CBM) wells in the southern Qinshui Basin,history matching,productivity prediction and factor analysis of gas producti...Based on regional CBM geological characteristics and drainage data of three typical Coalbed Methane(CBM) wells in the southern Qinshui Basin,history matching,productivity prediction and factor analysis of gas production control are conducted by using COMET3 reservoir modeling software.The results show that in the next 20 years,the cumulative and average daily gas production of the QN01 well are expected to be 800×104 m3 and 1141.1 m3/d,for the QN02 well 878×104 m3 and 1202.7 m3/d and 97.5×104 m3 and 133.55 m3/d for the QN03 well.Gas content and reservoir pressure are the key factors controlling gas production in the area;coal thickness,permeability and porosity are less important;the Langmuir volume,Langmuir pressure and adsorption time have relatively small effect.In the process of CBM recovery,the material source and driving force are the key features affecting gas productivity,while the permeation process is relatively important and the desorption process has some impact on gas recovery.展开更多
A mathematical model, fully coupling multiple porous media deformation and fluid flow, was established based on the elastic theory of porous media and fluid-solid coupling mechanism in tight oil reservoirs. The finite...A mathematical model, fully coupling multiple porous media deformation and fluid flow, was established based on the elastic theory of porous media and fluid-solid coupling mechanism in tight oil reservoirs. The finite element method was used to determine the numerical solution and the accuracy of the model was verified. On this basis, the model was used to simulate productivity of multistage fractured horizontal wells in tight oil reservoirs. The results show that during the production of tight oil wells, the reservoir region close to artificial fractures deteriorated in physical properties significantly, e.g. the aperture and conductivity of artificial fractures dropped by 52.12% and 89.02% respectively. The simulations of 3000-day production of a horizontal well in tight oil reservoir showed that the predicted productivity by the uncoupled model had an error of 38.30% from that by the fully-coupled model. Apparently, ignoring the influence of fluid-solid interaction effect led to serious deviations of the productivity prediction results. The productivity of horizontal well in tight oil reservoir was most sensitive to the start-up pressure gradient, and second most sensitive to the opening of artificial fractures. Enhancing the initial conductivity of artificial fractures was helpful to improve the productivity of tight oil wells. The influence of conductivity, spacing, number and length of artificial fractures should be considered comprehensively in fracturing design. Increasing the number of artificial fractures unilaterally could not achieve the expected increase in production.展开更多
The Sulige tight sandstone gas reservoir is marked by low permeability,intricate pore structures,and notable lateral heterogeneity,making it difficult to predict the productivity of fractured horizontal wells in the r...The Sulige tight sandstone gas reservoir is marked by low permeability,intricate pore structures,and notable lateral heterogeneity,making it difficult to predict the productivity of fractured horizontal wells in the reservoir.In this study,a productivity prediction model for fractured horizontal wells is developed based on the characteristics of the Sulige gas reservoir,including its high start-up pressure gradient,strong stress sensitivity,obvious non-Darcy flow,and typical slippage and diffusion effects.This new model fully accounts for each hydraulic fracture in the horizontal wells based on the superposition principle and Green's function.This model facilitates efficient productivity calculations and enables rapid quantitative analysis of the influencing factors specific to horizontal wells with hydraulic fractures,fully integrating the specific characteristics of the Sulige gas field.The accuracy of this model is tested against field data from Wells LX1 and LX2 in the Sulige field,indicating good agreement between the predicted values and field data.Well LX2 is used as a case study to analyze the influences of geological and engineering factors on well productivity.The following conclusions are drawn:1)Well productivity is notably influenced by the start-up pressure gradient and stress sensitivity,with a minor impact from non-Darcy effects.2)Productivity linearly decreases with increased hydraulic fracture spacing.3)Productivity increases,and the increment rate gradually decreases,with increases in the length and conductivity of the hydraulic fractures.This model provides valuable guidance on predicting productivity in tight sandstone gas reservoirs,such as that of the Sulige gas field.展开更多
Using current Embedded Discrete Fracture Models(EDFM) to predict the productivity of fractured wells has some drawbacks, such as not supporting corner grid, low precision in the near wellbore zone, and disregarding th...Using current Embedded Discrete Fracture Models(EDFM) to predict the productivity of fractured wells has some drawbacks, such as not supporting corner grid, low precision in the near wellbore zone, and disregarding the heterogeneity of conductivity brought by non-uniform sand concentration. An EDFM is developed based on the corner grid, which enables high efficient calculation of the transmissibility between the embedded fractures and matrix grids, and calculation of the permeability of each polygon in the embedded fractures by the lattice data of the artificial fracture aperture. On this basis, a coupling method of local grid refinement(LGR) and embedded discrete fracture model is designed, which is verified by comparing the calculation results with the Discrete Fracture Network(DFN) method and fitting the actual production data of the first hydraulically fractured well in Iraq. By using this method and orthogonal experimental design, the optimization of the parameters of the first multi-stage fractured horizontal well in the same block is completed. The results show the proposed method has theoretical and practical significance for improving the adaptability of EDFM and the accuracy of productivity prediction of fractured wells, and enables the coupling of fracture modeling and numerical productivity simulation at reservoir scale.展开更多
Clarifying the flow laws of shale gas under high temperature and high pressure is the prerequisite to accurately predicting the productivity of deep shale gas wells.In this paper,a self-diffusion flow model of flow fi...Clarifying the flow laws of shale gas under high temperature and high pressure is the prerequisite to accurately predicting the productivity of deep shale gas wells.In this paper,a self-diffusion flow model of flow field and temperature field coupling(referred to as self-diffusion flow and heat coupling model)was established based on the previously proposed self-diffusion flow model,while considering the influence of the temperature field change.Then,its calculation result was compared with that of the flow model based on Darcy's law and Knudsen diffusion(referred to as modified Darcy flow model).Based on the self-diffusion flow and heat coupling model,the self-diffusion flow behaviors of deep shale gas under the influence of temperature field change were analyzed,and the influence of bottomhole temperature on the degree of reserve recovery of deep shale gas was discussed.Finally,the self-diffusion flow and heat coupling model was applied to simulate the production of one shale-gas horizontal well in the Upper Ordovician Wufeng FormationeLower Silurian Longmaxi Formation in the Changning Block of the Sichuan Basin.And the following research results were obtained.First,at the same parameters,the shale gas production calculated by the selfdiffusion flow and heat coupling model is higher than the result calculated by the modified Darcy flow model.Second,when temperature field change is taken into consideration,the selfedviffusion coefficient profile presents a peak,the gas density profile presents a valley and the data points corresponding to the peak/valley move synchronously to the internal formation,which indicates that the selfediffusion coefficient influences the gas mass transfer rate,and the influence range of near well low temperature on gas self-diffusion increases continuously as the production continues.Third,when the bottomhole temperature is lower than the formation temperature,the selfediffusion coefficient of the gas near the well decreases and the gas is blocked near the well,which reduces the gas well production.Fourth,the production simulation result of the case well shows that the self-diffusion flow and heat coupling model can predict the production of deep shale gas more accurately if temperature field change is taken into consideration.In conclusion,the self-diffusion flow and heat coupling model established in this paper is of higher reliability and accuracy and can be used for productivity simulation and prediction of deep shale gas wells.The conclusion of this paper has certain guiding significance for deep shale gas production and gas well productivity prediction.展开更多
Reservoir identification and production prediction are two of the most important tasks in petroleum exploration and development.Machine learning(ML)methods are used for petroleum-related studies,but have not been appl...Reservoir identification and production prediction are two of the most important tasks in petroleum exploration and development.Machine learning(ML)methods are used for petroleum-related studies,but have not been applied to reservoir identification and production prediction based on reservoir identification.Production forecasting studies are typically based on overall reservoir thickness and lack accuracy when reservoirs contain a water or dry layer without oil production.In this paper,a systematic ML method was developed using classification models for reservoir identification,and regression models for production prediction.The production models are based on the reservoir identification results.To realize the reservoir identification,seven optimized ML methods were used:four typical single ML methods and three ensemble ML methods.These methods classify the reservoir into five types of layers:water,dry and three levels of oil(I oil layer,II oil layer,III oil layer).The validation and test results of these seven optimized ML methods suggest the three ensemble methods perform better than the four single ML methods in reservoir identification.The XGBoost produced the model with the highest accuracy;up to 99%.The effective thickness of I and II oil layers determined during the reservoir identification was fed into the models for predicting production.Effective thickness considers the distribution of the water and the oil resulting in a more reasonable production prediction compared to predictions based on the overall reservoir thickness.To validate the superiority of the ML methods,reference models using overall reservoir thickness were built for comparison.The models based on effective thickness outperformed the reference models in every evaluation metric.The prediction accuracy of the ML models using effective thickness were 10%higher than that of reference model.Without the personal error or data distortion existing in traditional methods,this novel system realizes rapid analysis of data while reducing the time required to resolve reservoir classification and production prediction challenges.The ML models using the effective thickness obtained from reservoir identification were more accurate when predicting oil production compared to previous studies which use overall reservoir thickness.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
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
High strength steel products with good ductility can be produced via Q&P hot stamping process,while the phase transformation of the process is more complicated than common hot stamping since two-step quenching and...High strength steel products with good ductility can be produced via Q&P hot stamping process,while the phase transformation of the process is more complicated than common hot stamping since two-step quenching and one-step carbon partitioning processes are involved.In this study,an integrated model of microstructure evolution relating to Q&P hot stamping was presented with a persuasively predicted results of mechanical properties.The transformation of diffusional phase and non-diffusional phase,including original austenite grain size individually,were considered,as well as the carbon partitioning process which affects the secondary martensite transformation temperature and the subsequent phase transformations.Afterwards,the mechanical properties including hardness,strength,and elongation were calculated through a series of theoretical and empirical models in accordance with phase contents.Especially,a modified elongation prediction model was generated ultimately with higher accuracy than the existed Mileiko’s model.In the end,the unified model was applied to simulate the Q&P hot stamping process of a U-cup part based on the finite element software LS-DYNA,where the calculated outputs were coincident with the measured consequences.展开更多
With the development of information technology,a large number of product quality data in the entire manufacturing process is accumulated,but it is not explored and used effectively.The traditional product quality pred...With the development of information technology,a large number of product quality data in the entire manufacturing process is accumulated,but it is not explored and used effectively.The traditional product quality prediction models have many disadvantages,such as high complexity and low accuracy.To overcome the above problems,we propose an optimized data equalization method to pre-process dataset and design a simple but effective product quality prediction model:radial basis function model optimized by the firefly algorithm with Levy flight mechanism(RBFFALM).First,the new data equalization method is introduced to pre-process the dataset,which reduces the dimension of the data,removes redundant features,and improves the data distribution.Then the RBFFALFM is used to predict product quality.Comprehensive expe riments conducted on real-world product quality datasets validate that the new model RBFFALFM combining with the new data pre-processing method outperforms other previous me thods on predicting product quality.展开更多
Accurate prediction ofmonthly oil and gas production is essential for oil enterprises tomake reasonable production plans,avoid blind investment and realize sustainable development.Traditional oil well production trend...Accurate prediction ofmonthly oil and gas production is essential for oil enterprises tomake reasonable production plans,avoid blind investment and realize sustainable development.Traditional oil well production trend prediction methods are based on years of oil field production experience and expertise,and the application conditions are very demanding.With the rapid development of artificial intelligence technology,big data analysis methods are gradually applied in various sub-fields of the oil and gas reservoir development.Based on the data-driven artificial intelligence algorithmGradient BoostingDecision Tree(GBDT),this paper predicts the initial single-layer production by considering geological data,fluid PVT data and well data.The results show that the GBDT algorithm prediction model has great accuracy,significantly improving efficiency and strong universal applicability.The GBDTmethod trained in this paper can predict production,which is helpful for well site optimization,perforation layer optimization and engineering parameter optimization and has guiding significance for oilfield development.展开更多
In accordance with the fracturing and producing mechanism in coalbed methane well, and combining the knowledge of fluid mechanics, linear elastic fracture mechanics, thermal transfer, computing mathematics and softwar...In accordance with the fracturing and producing mechanism in coalbed methane well, and combining the knowledge of fluid mechanics, linear elastic fracture mechanics, thermal transfer, computing mathematics and software engineering, the three-dimensional hydraulic fracture propagating and dynamical production predicting models for coalbed methane well is put forward. The fracture propagation model takes the variation of rock mechanical properties and in-situ stress distribution into consideration. The dynamic performance prediction model takes the gas production mechanism into consideration. With these models, a three-dimensional hydraulic fracturing optimum design software for coalbed methane well is developed, and its practicality and reliability have been proved by ex-ample computation.展开更多
Considering the phase behaviors in condensate gas reservoirs and the oil-gas two-phase linear flow and boundary-dominated flow in the reservoir,a method for predicting the relationship between oil saturation and press...Considering the phase behaviors in condensate gas reservoirs and the oil-gas two-phase linear flow and boundary-dominated flow in the reservoir,a method for predicting the relationship between oil saturation and pressure in the full-path of tight condensate gas well is proposed,and a model for predicting the transient production from tight condensate gas wells with multiphase flow is established.The research indicates that the relationship curve between condensate oil saturation and pressure is crucial for calculating the pseudo-pressure.In the early stage of production or in areas far from the wellbore with high reservoir pressure,the condensate oil saturation can be calculated using early-stage production dynamic data through material balance models.In the late stage of production or in areas close to the wellbore with low reservoir pressure,the condensate oil saturation can be calculated using the data of constant composition expansion test.In the middle stages of production or when reservoir pressure is at an intermediate level,the data obtained from the previous two stages can be interpolated to form a complete full-path relationship curve between oil saturation and pressure.Through simulation and field application,the new method is verified to be reliable and practical.It can be applied for prediction of middle-stage and late-stage production of tight condensate gas wells and assessment of single-well recoverable reserves.展开更多
This paper presents the development of numerical prediction products(NPP) correction and display system(NPPCDS) for rapid and effective post-processing and displaying of the T213 NPP(numerical prediction products of t...This paper presents the development of numerical prediction products(NPP) correction and display system(NPPCDS) for rapid and effective post-processing and displaying of the T213 NPP(numerical prediction products of the medium range numerical weather prediction spectral model T213L31) through instant correction method. The NPPCDS consists of two modules: an automatic correction module and a graphical display module. The automatic correction module automatically corrects the T213 NPP at regularly scheduled time intervals, while the graphical display module interacts with users to display the T213 NPP and its correction results. The system helps forecasters extract the most relevant information at a quick glance without extensive post-processing. It is simple, easy to use, and computationally efficient, and has been running stably at Huludao Meteorological Bureau in Liaoning Province of China for the past three years. Because of its low computational costs, it is particularly useful for meteorological departments that lack advanced computing capacity and still need to make short-range weather forecasting.展开更多
There has been increased interest in quantifying the manure production of livestock, primarily driven by public authorities, who aim to evaluate the environmental impact of livestock production, but also at the farm l...There has been increased interest in quantifying the manure production of livestock, primarily driven by public authorities, who aim to evaluate the environmental impact of livestock production, but also at the farm level, to manage manure storage and availability of fertilizer for crop production. Moreover, current manure production estimates from intensively reared beef calves are higher than actual production due to changes in farming systems, advances in animal genetics and feed efficiency. This study aims to redefine and update manure production estimates in intensively reared beef calves to predict manure production as a policy and planning tool, as there are no current models available. A trial was conducted to collect data on manure production during the growing-finishing period (243 d) of 54 Limousine calves (from 346.7 to 674.0 kg live weight, LW). Such data were used to develop two models to predict manure excretion: (1) a complex mechanistic model (CompM), and (2) a simplified empirical model (SimpM). Both models were evaluated against an independent dataset including a total of 4,692 animals on 31 farms and 5 breeds. Results from CompM require interpretation because the model does not output a single value but a range of manure production (minimum, medium and maximum), and would therefore be more suitable for professional use. The SimpM could be considered simple, reliable, and versatile for predicting manure excretion at farm level. SimpM could be refined and improved by including data from other studies on beef cattle with distinct characteristics and management.展开更多
基金Project(12521044) supported by Scientific and Technological Research Program of Heilongjiang Provincial Education Department,China
文摘In order to accurately predict the productivity of herringbone multilateral well,a new productivity prediction model was founded.And based on this model,orthogonal test and multiple factor variance analysis were applied to study optimization design of herringbone multilateral well.According to the characteristics of herringbone multilateral well,by using pressure superposition and mirror image reflection theory,the coupled model of herringbone multilateral well was developed on the basis of a three-dimensional pseudo-pressure distribution model for horizontal wells.The model was formulated in consideration of friction loss,acceleration loss of the wellbore and mixed loss at the confluence of main wellbore and branched one.After mathematical simulation on productivity of the herringbone multilateral well with the coupled model,the effects of well configuration on productivity were analyzed.The results show that lateral number is the most important factor,length of main wellbore and length of branched wellbore are the secondary ones,angle between main and branched one has the least influence.
基金supported by the National Natural Science Fund of China (No.52104049)the Science Foundation of China University of Petroleum,Beijing (No.2462022BJRC004)。
文摘In the early time of oilfield development, insufficient production data and unclear understanding of oil production presented a challenge to reservoir engineers in devising effective development plans. To address this challenge, this study proposes a method using data mining technology to search for similar oil fields and predict well productivity. A query system of 135 analogy parameters is established based on geological and reservoir engineering research, and the weight values of these parameters are calculated using a data algorithm to establish an analogy system. The fuzzy matter-element algorithm is then used to calculate the similarity between oil fields, with fields having similarity greater than 70% identified as similar oil fields. Using similar oil fields as sample data, 8 important factors affecting well productivity are identified using the Pearson coefficient and mean decrease impurity(MDI) method. To establish productivity prediction models, linear regression(LR), random forest regression(RF), support vector regression(SVR), backpropagation(BP), extreme gradient boosting(XGBoost), and light gradient boosting machine(Light GBM) algorithms are used. Their performance is evaluated using the coefficient of determination(R^(2)), explained variance score(EV), mean squared error(MSE), and mean absolute error(MAE) metrics. The Light GBM model is selected to predict the productivity of 30 wells in the PL field with an average error of only 6.31%, which significantly improves the accuracy of the productivity prediction and meets the application requirements in the field. Finally, a software platform integrating data query,oil field analogy, productivity prediction, and knowledge base is established to identify patterns in massive reservoir development data and provide valuable technical references for new reservoir development.
基金This study was financially supported by China United Coalbed Methane Corporation,Ltd.(ZZGSSALFGR2021-581),Bin Li received the grant.
文摘In order to overcome the deficiencies of current methods for the prediction of the productivity of shale gas hor-izontal wells after fracturing,a new sophisticated approach is proposed in this study.This new model stems from the combination several techniques,namely,artificial neural network(ANN),particle swarm optimization(PSO),Imperialist Competitive Algorithms(ICA),and Ant Clony Optimization(ACO).These are properly implemented by using the geological and engineering parameters collected from 317 wells.The results show that the optimum PSO-ANN model has a high accuracy,obtaining a R2 of 0.847 on the testing.The partial dependence plots(PDP)indicate that liquid consumption intensity and the proportion of quartz sand are the two most sensitive factors affecting the model’s performance.
基金funded by the National Natural Science Foundation of China[grant numbers 42075115 and 41991285]the Joint Open Project of KLME&CIC-FEMD[grant number KLME201901]。
文摘Net primary productivity(NPP)is the net accumulation of organic matter by vegetation through photosynthesis and serves as a key indicator for exploring vegetation responses to climate change.Considering the remote and local impacts of soil heat capacities on vegetation growth through pathways of atmospheric circulation and land–atmosphere interaction,this paper develops a statistical prediction model for NPP from April to June(AMJ)across the middle-to-high latitudes of Eurasia.The model introduces two physically meaningful predictors:the snow water equivalent(SWE)from February to March(FM)over central Europe and the FM local soil temperature(ST).The positive phase of FM SWE triggers anomalous eastward-propagating Rossby waves,leading to an anomalous low-pressure system and cooling in the middle-to-high latitudes of Eurasia.This effect persists into spring through snow feedback to the atmosphere and affects subsequent NPP changes.The ST is closely related to the AMJ temperature and precipitation.With positive ST anomalies,the AMJ temperature and precipitation exhibit an east–west dipole anomaly distribution in this region.The single-factor prediction scheme using ST as the predictor is much better than using SWE as the predictor.Independent validation results from 2009 to 2014 demonstrate that the ST scheme alone has good predictive performance for the spatial distribution and interannual variability of NPP.The predictive skills of the multi-factor prediction schemes can be improved by about 13%if the ST predictor is included.The findings confirm that local ST is a predictor that must be included for NPP prediction.
基金supported by the Chongqing Natural Science Foundation Innovation and Development Joint Fund(CSTB2023NSCQ-LZX0078)the Science and Technology Research Program of Chongqing Municipal Education Commission(KJQN202201519),which are gratefully acknowledged.
文摘Pressure control in deep shale gas horizontal wells can reduce the stress sensitivity of hydraulic fractures and improve the estimated ultimate recovery(EUR).In this study,a hydraulic fracture stress sensitivity model is proposed to characterize the effect of pressure drop rate on fracture permeability.Furthermore,a production prediction model is introduced accounting for a non-uniform hydraulic fracture conductivity distribution.The results reveal that increasing the fracture conductivity leads to a rapid daily production increase in the early stages.However,above 0.50 D·cm,a further increase in the fracture conductivity has a limited effect on shale gas production growth.The initial production is lower under pressure-controlled conditions than that under pressure-release.For extended pressure control durations,the cumulative production initially increases and then decreases.For a fracture conductivity of 0.10 D·cm,the increase in production output under controlled-pressure conditions is~35%.For representative deep shale gas wells(Southern Sichuan,China),if the pressure drop rate under controlled-pressure conditions is reduced from 0.19 to 0.04 MPa/d,the EUR increase for 5 years of pressure-controlled production is 41.0 million,with an increase percentage of~29%.
基金the National Basic Research Program of China (No.2009 CB219605)the Key Program of the National Natural Science Foundation of China (No.4073042)the Key Program of the National Science and Technology of China (No.2008ZX05034-04)
文摘Based on regional CBM geological characteristics and drainage data of three typical Coalbed Methane(CBM) wells in the southern Qinshui Basin,history matching,productivity prediction and factor analysis of gas production control are conducted by using COMET3 reservoir modeling software.The results show that in the next 20 years,the cumulative and average daily gas production of the QN01 well are expected to be 800×104 m3 and 1141.1 m3/d,for the QN02 well 878×104 m3 and 1202.7 m3/d and 97.5×104 m3 and 133.55 m3/d for the QN03 well.Gas content and reservoir pressure are the key factors controlling gas production in the area;coal thickness,permeability and porosity are less important;the Langmuir volume,Langmuir pressure and adsorption time have relatively small effect.In the process of CBM recovery,the material source and driving force are the key features affecting gas productivity,while the permeation process is relatively important and the desorption process has some impact on gas recovery.
基金Supported by the National Science and Technology Major Project (2017ZX05013-005)。
文摘A mathematical model, fully coupling multiple porous media deformation and fluid flow, was established based on the elastic theory of porous media and fluid-solid coupling mechanism in tight oil reservoirs. The finite element method was used to determine the numerical solution and the accuracy of the model was verified. On this basis, the model was used to simulate productivity of multistage fractured horizontal wells in tight oil reservoirs. The results show that during the production of tight oil wells, the reservoir region close to artificial fractures deteriorated in physical properties significantly, e.g. the aperture and conductivity of artificial fractures dropped by 52.12% and 89.02% respectively. The simulations of 3000-day production of a horizontal well in tight oil reservoir showed that the predicted productivity by the uncoupled model had an error of 38.30% from that by the fully-coupled model. Apparently, ignoring the influence of fluid-solid interaction effect led to serious deviations of the productivity prediction results. The productivity of horizontal well in tight oil reservoir was most sensitive to the start-up pressure gradient, and second most sensitive to the opening of artificial fractures. Enhancing the initial conductivity of artificial fractures was helpful to improve the productivity of tight oil wells. The influence of conductivity, spacing, number and length of artificial fractures should be considered comprehensively in fracturing design. Increasing the number of artificial fractures unilaterally could not achieve the expected increase in production.
基金support from the Natural Science Foundation of Sichuan Province(Grant Nos.2022NSFSC0185 and 2023NSFSC0938)the National Natural Science Foundation of China(Grant No.42172313)are appreciated.
文摘The Sulige tight sandstone gas reservoir is marked by low permeability,intricate pore structures,and notable lateral heterogeneity,making it difficult to predict the productivity of fractured horizontal wells in the reservoir.In this study,a productivity prediction model for fractured horizontal wells is developed based on the characteristics of the Sulige gas reservoir,including its high start-up pressure gradient,strong stress sensitivity,obvious non-Darcy flow,and typical slippage and diffusion effects.This new model fully accounts for each hydraulic fracture in the horizontal wells based on the superposition principle and Green's function.This model facilitates efficient productivity calculations and enables rapid quantitative analysis of the influencing factors specific to horizontal wells with hydraulic fractures,fully integrating the specific characteristics of the Sulige gas field.The accuracy of this model is tested against field data from Wells LX1 and LX2 in the Sulige field,indicating good agreement between the predicted values and field data.Well LX2 is used as a case study to analyze the influences of geological and engineering factors on well productivity.The following conclusions are drawn:1)Well productivity is notably influenced by the start-up pressure gradient and stress sensitivity,with a minor impact from non-Darcy effects.2)Productivity linearly decreases with increased hydraulic fracture spacing.3)Productivity increases,and the increment rate gradually decreases,with increases in the length and conductivity of the hydraulic fractures.This model provides valuable guidance on predicting productivity in tight sandstone gas reservoirs,such as that of the Sulige gas field.
基金Supported by the China National Science and Technology Major Project (2017ZX05030)
文摘Using current Embedded Discrete Fracture Models(EDFM) to predict the productivity of fractured wells has some drawbacks, such as not supporting corner grid, low precision in the near wellbore zone, and disregarding the heterogeneity of conductivity brought by non-uniform sand concentration. An EDFM is developed based on the corner grid, which enables high efficient calculation of the transmissibility between the embedded fractures and matrix grids, and calculation of the permeability of each polygon in the embedded fractures by the lattice data of the artificial fracture aperture. On this basis, a coupling method of local grid refinement(LGR) and embedded discrete fracture model is designed, which is verified by comparing the calculation results with the Discrete Fracture Network(DFN) method and fitting the actual production data of the first hydraulically fractured well in Iraq. By using this method and orthogonal experimental design, the optimization of the parameters of the first multi-stage fractured horizontal well in the same block is completed. The results show the proposed method has theoretical and practical significance for improving the adaptability of EDFM and the accuracy of productivity prediction of fractured wells, and enables the coupling of fracture modeling and numerical productivity simulation at reservoir scale.
基金Project supported by the Youth Science Foundation Project of National Natural Science Foundation of China“Dynamic Evolution Mechanism of Shale Reservoir Stress Field under Multi-well Interference”(No.5190040058)the General Project of National Natural Science Foundation of China“Research on Hydrodynamic Mechanism of Multi-scale Channels in Tight Reservoir”,(No.51874321)the Scientific Research Foundation of China University of Petroleum(Beijing)for Young Top Talent“Multi-scale Characteristics of Fluid Flow in Complex Fracture Network of Shale Gas Reservoirs”(No.2462018YJRC014).
文摘Clarifying the flow laws of shale gas under high temperature and high pressure is the prerequisite to accurately predicting the productivity of deep shale gas wells.In this paper,a self-diffusion flow model of flow field and temperature field coupling(referred to as self-diffusion flow and heat coupling model)was established based on the previously proposed self-diffusion flow model,while considering the influence of the temperature field change.Then,its calculation result was compared with that of the flow model based on Darcy's law and Knudsen diffusion(referred to as modified Darcy flow model).Based on the self-diffusion flow and heat coupling model,the self-diffusion flow behaviors of deep shale gas under the influence of temperature field change were analyzed,and the influence of bottomhole temperature on the degree of reserve recovery of deep shale gas was discussed.Finally,the self-diffusion flow and heat coupling model was applied to simulate the production of one shale-gas horizontal well in the Upper Ordovician Wufeng FormationeLower Silurian Longmaxi Formation in the Changning Block of the Sichuan Basin.And the following research results were obtained.First,at the same parameters,the shale gas production calculated by the selfdiffusion flow and heat coupling model is higher than the result calculated by the modified Darcy flow model.Second,when temperature field change is taken into consideration,the selfedviffusion coefficient profile presents a peak,the gas density profile presents a valley and the data points corresponding to the peak/valley move synchronously to the internal formation,which indicates that the selfediffusion coefficient influences the gas mass transfer rate,and the influence range of near well low temperature on gas self-diffusion increases continuously as the production continues.Third,when the bottomhole temperature is lower than the formation temperature,the selfediffusion coefficient of the gas near the well decreases and the gas is blocked near the well,which reduces the gas well production.Fourth,the production simulation result of the case well shows that the self-diffusion flow and heat coupling model can predict the production of deep shale gas more accurately if temperature field change is taken into consideration.In conclusion,the self-diffusion flow and heat coupling model established in this paper is of higher reliability and accuracy and can be used for productivity simulation and prediction of deep shale gas wells.The conclusion of this paper has certain guiding significance for deep shale gas production and gas well productivity prediction.
文摘Reservoir identification and production prediction are two of the most important tasks in petroleum exploration and development.Machine learning(ML)methods are used for petroleum-related studies,but have not been applied to reservoir identification and production prediction based on reservoir identification.Production forecasting studies are typically based on overall reservoir thickness and lack accuracy when reservoirs contain a water or dry layer without oil production.In this paper,a systematic ML method was developed using classification models for reservoir identification,and regression models for production prediction.The production models are based on the reservoir identification results.To realize the reservoir identification,seven optimized ML methods were used:four typical single ML methods and three ensemble ML methods.These methods classify the reservoir into five types of layers:water,dry and three levels of oil(I oil layer,II oil layer,III oil layer).The validation and test results of these seven optimized ML methods suggest the three ensemble methods perform better than the four single ML methods in reservoir identification.The XGBoost produced the model with the highest accuracy;up to 99%.The effective thickness of I and II oil layers determined during the reservoir identification was fed into the models for predicting production.Effective thickness considers the distribution of the water and the oil resulting in a more reasonable production prediction compared to predictions based on the overall reservoir thickness.To validate the superiority of the ML methods,reference models using overall reservoir thickness were built for comparison.The models based on effective thickness outperformed the reference models in every evaluation metric.The prediction accuracy of the ML models using effective thickness were 10%higher than that of reference model.Without the personal error or data distortion existing in traditional methods,this novel system realizes rapid analysis of data while reducing the time required to resolve reservoir classification and production prediction challenges.The ML models using the effective thickness obtained from reservoir identification were more accurate when predicting oil production compared to previous studies which use overall reservoir thickness.
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 National Natural Science Foundation of China (Grant Nos. 51775336,U1564203)Program of Shanghai Academic Research Leadership (Grant No. 19XD1401900)
文摘High strength steel products with good ductility can be produced via Q&P hot stamping process,while the phase transformation of the process is more complicated than common hot stamping since two-step quenching and one-step carbon partitioning processes are involved.In this study,an integrated model of microstructure evolution relating to Q&P hot stamping was presented with a persuasively predicted results of mechanical properties.The transformation of diffusional phase and non-diffusional phase,including original austenite grain size individually,were considered,as well as the carbon partitioning process which affects the secondary martensite transformation temperature and the subsequent phase transformations.Afterwards,the mechanical properties including hardness,strength,and elongation were calculated through a series of theoretical and empirical models in accordance with phase contents.Especially,a modified elongation prediction model was generated ultimately with higher accuracy than the existed Mileiko’s model.In the end,the unified model was applied to simulate the Q&P hot stamping process of a U-cup part based on the finite element software LS-DYNA,where the calculated outputs were coincident with the measured consequences.
基金supported by the National Science and Technology Innovation 2030 Next-Generation Artifical Intelligence Major Project(2018AAA0101801)the National Natural Science Foundation of China(72271188)。
文摘With the development of information technology,a large number of product quality data in the entire manufacturing process is accumulated,but it is not explored and used effectively.The traditional product quality prediction models have many disadvantages,such as high complexity and low accuracy.To overcome the above problems,we propose an optimized data equalization method to pre-process dataset and design a simple but effective product quality prediction model:radial basis function model optimized by the firefly algorithm with Levy flight mechanism(RBFFALM).First,the new data equalization method is introduced to pre-process the dataset,which reduces the dimension of the data,removes redundant features,and improves the data distribution.Then the RBFFALFM is used to predict product quality.Comprehensive expe riments conducted on real-world product quality datasets validate that the new model RBFFALFM combining with the new data pre-processing method outperforms other previous me thods on predicting product quality.
文摘Accurate prediction ofmonthly oil and gas production is essential for oil enterprises tomake reasonable production plans,avoid blind investment and realize sustainable development.Traditional oil well production trend prediction methods are based on years of oil field production experience and expertise,and the application conditions are very demanding.With the rapid development of artificial intelligence technology,big data analysis methods are gradually applied in various sub-fields of the oil and gas reservoir development.Based on the data-driven artificial intelligence algorithmGradient BoostingDecision Tree(GBDT),this paper predicts the initial single-layer production by considering geological data,fluid PVT data and well data.The results show that the GBDT algorithm prediction model has great accuracy,significantly improving efficiency and strong universal applicability.The GBDTmethod trained in this paper can predict production,which is helpful for well site optimization,perforation layer optimization and engineering parameter optimization and has guiding significance for oilfield development.
文摘In accordance with the fracturing and producing mechanism in coalbed methane well, and combining the knowledge of fluid mechanics, linear elastic fracture mechanics, thermal transfer, computing mathematics and software engineering, the three-dimensional hydraulic fracture propagating and dynamical production predicting models for coalbed methane well is put forward. The fracture propagation model takes the variation of rock mechanical properties and in-situ stress distribution into consideration. The dynamic performance prediction model takes the gas production mechanism into consideration. With these models, a three-dimensional hydraulic fracturing optimum design software for coalbed methane well is developed, and its practicality and reliability have been proved by ex-ample computation.
基金Supported by National Natural Science Foundation of China(52104049)Young Elite Scientist Sponsorship Program by BAST(BYESS2023262)Science Foundation of China University of Petroleum,Beijing(2462022BJRC004).
文摘Considering the phase behaviors in condensate gas reservoirs and the oil-gas two-phase linear flow and boundary-dominated flow in the reservoir,a method for predicting the relationship between oil saturation and pressure in the full-path of tight condensate gas well is proposed,and a model for predicting the transient production from tight condensate gas wells with multiphase flow is established.The research indicates that the relationship curve between condensate oil saturation and pressure is crucial for calculating the pseudo-pressure.In the early stage of production or in areas far from the wellbore with high reservoir pressure,the condensate oil saturation can be calculated using early-stage production dynamic data through material balance models.In the late stage of production or in areas close to the wellbore with low reservoir pressure,the condensate oil saturation can be calculated using the data of constant composition expansion test.In the middle stages of production or when reservoir pressure is at an intermediate level,the data obtained from the previous two stages can be interpolated to form a complete full-path relationship curve between oil saturation and pressure.Through simulation and field application,the new method is verified to be reliable and practical.It can be applied for prediction of middle-stage and late-stage production of tight condensate gas wells and assessment of single-well recoverable reserves.
基金Under the auspices of National Natural Science Foundation of China(No.91125010)
文摘This paper presents the development of numerical prediction products(NPP) correction and display system(NPPCDS) for rapid and effective post-processing and displaying of the T213 NPP(numerical prediction products of the medium range numerical weather prediction spectral model T213L31) through instant correction method. The NPPCDS consists of two modules: an automatic correction module and a graphical display module. The automatic correction module automatically corrects the T213 NPP at regularly scheduled time intervals, while the graphical display module interacts with users to display the T213 NPP and its correction results. The system helps forecasters extract the most relevant information at a quick glance without extensive post-processing. It is simple, easy to use, and computationally efficient, and has been running stably at Huludao Meteorological Bureau in Liaoning Province of China for the past three years. Because of its low computational costs, it is particularly useful for meteorological departments that lack advanced computing capacity and still need to make short-range weather forecasting.
文摘There has been increased interest in quantifying the manure production of livestock, primarily driven by public authorities, who aim to evaluate the environmental impact of livestock production, but also at the farm level, to manage manure storage and availability of fertilizer for crop production. Moreover, current manure production estimates from intensively reared beef calves are higher than actual production due to changes in farming systems, advances in animal genetics and feed efficiency. This study aims to redefine and update manure production estimates in intensively reared beef calves to predict manure production as a policy and planning tool, as there are no current models available. A trial was conducted to collect data on manure production during the growing-finishing period (243 d) of 54 Limousine calves (from 346.7 to 674.0 kg live weight, LW). Such data were used to develop two models to predict manure excretion: (1) a complex mechanistic model (CompM), and (2) a simplified empirical model (SimpM). Both models were evaluated against an independent dataset including a total of 4,692 animals on 31 farms and 5 breeds. Results from CompM require interpretation because the model does not output a single value but a range of manure production (minimum, medium and maximum), and would therefore be more suitable for professional use. The SimpM could be considered simple, reliable, and versatile for predicting manure excretion at farm level. SimpM could be refined and improved by including data from other studies on beef cattle with distinct characteristics and management.
