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
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
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
In recent years,Bohai Oilfield has continuously intensified its adjustment and potential tapping efforts.Adjustment wells have become a key measure for increasing production in offshore oilfields,contributing signific...In recent years,Bohai Oilfield has continuously intensified its adjustment and potential tapping efforts.Adjustment wells have become a key measure for increasing production in offshore oilfields,contributing significantly to output.However,with the persistent rise in water cut and the increasingly complex distribution of remaining oil,the difficulty of predicting the medium-to-long-term production of adjustment wells has increased substantially,severely constraining the accuracy of development planning.To address this issue,this study systematically analyzes the productivity construction patterns of different potential-tapping types in Bohai Oilfield based on dynamic development data and incorporates a key quantitative indicator of productivity construction effectiveness—the Productivity Coefficient(PC).An innovative multi-dimensional evaluation system for the effectiveness of productivity construction in offshore oilfields has been developed,and a methodology for predicting the medium-to-long-term production of adjustment wells has been established.Application of this method in the Bohai BZ Oilfield indicates that the future average annual production scale of adjustment wells is approximately 7×10^(4) tons to 10×10^(4) tons,with prediction results aligning well with actual development dynamics.This research provides a theoretical foundation and decision-making support for medium-tolongterm production planning in Bohai Oilfield.展开更多
Prediction of production decline and evaluation of the adsorbed/free gas ratio are critical for determining the lifespan and production status of shale gas wells.Traditional production prediction methods have some sho...Prediction of production decline and evaluation of the adsorbed/free gas ratio are critical for determining the lifespan and production status of shale gas wells.Traditional production prediction methods have some shortcomings because of the low permeability and tightness of shale,complex gas flow behavior of multi-scale gas transport regions and multiple gas transport mechanism superpositions,and complex and variable production regimes of shale gas wells.Recent research has demonstrated the existence of a multi-stage isotope fractionation phenomenon during shale gas production,with the fractionation characteristics of each stage associated with the pore structure,gas in place(GIP),adsorption/desorption,and gas production process.This study presents a new approach for estimating shale gas well production and evaluating the adsorbed/free gas ratio throughout production using isotope fractionation techniques.A reservoir-scale carbon isotope fractionation(CIF)model applicable to the production process of shale gas wells was developed for the first time in this research.In contrast to the traditional model,this model improves production prediction accuracy by simultaneously fitting the gas production rate and δ^(13)C_(1) data and provides a new evaluation method of the adsorbed/free gas ratio during shale gas production.The results indicate that the diffusion and adsorption/desorption properties of rock,bottom-hole flowing pressure(BHP)of gas well,and multi-scale gas transport regions of the reservoir all affect isotope fractionation,with the diffusion and adsorption/desorption parameters of rock having the greatest effect on isotope fractionation being D∗/D,PL,VL,α,and others in that order.We effectively tested the universality of the four-stage isotope fractionation feature and revealed a unique isotope fractionation mechanism caused by the superimposed coupling of multi-scale gas transport regions during shale gas well production.Finally,we applied the established CIF model to a shale gas well in the Sichuan Basin,China,and calculated the estimated ultimate recovery(EUR)of the well to be 3.33×10^(8) m^(3);the adsorbed gas ratio during shale gas production was 1.65%,10.03%,and 23.44%in the first,fifth,and tenth years,respectively.The findings are significant for understanding the isotope fractionation mechanism during natural gas transport in complex systems and for formulating and optimizing unconventional natural gas development strategies.展开更多
Shale gas wells often face challenges in maintaining continuous and stable production due to their coexistence with high-and low-pressure wells within the same development block,which leads to issues involving mixed-p...Shale gas wells often face challenges in maintaining continuous and stable production due to their coexistence with high-and low-pressure wells within the same development block,which leads to issues involving mixed-pressure flows.Traditional pipeline optimization methods used in conventional gas well blocks fail to address the unique needs of shale gas wells,such as the precise planning of airflow paths,pressure distribution,and compression.This study proposes a pressure-controlled production optimization strategy specifically designed for shale gas wells operating under mixed-pressure flow conditions.The strategy aims to improve production stability and optimize system efficiency.The decline in production and pressure for individual wells over time is forecasted using a predictive model that accounts for key factors of system optimization,such as reservoir depletion,wellbore conditions,and equipment performance.Additionally,the model predicts the timing and impact of liquid loading,which can significantly affect production.The optimization process involves analyzing the existing gathering pipeline network to determine the most efficient flow directions and compression strategies based on these predictions,while the strategy involves adjusting compressor settings,optimizing flow rates,and planning pressure distribution across the network to maximize productivity while maintaining system stability.By implementing these strategies,this study significantly improves gas well productivity and enhances the adaptability and efficiency of the gathering and transportation system.The proposed approach provides systematic technical solutions and practical guidance for the efficient development and stable production of shale gas fields,ensuring more robust and sustainable pipeline operations.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
Accurately predicting the production rate and estimated ultimate recovery(EUR)of shale oil wells is vital for efficient shale oil development.Although numerical simulations provide accurate predictions,their high time...Accurately predicting the production rate and estimated ultimate recovery(EUR)of shale oil wells is vital for efficient shale oil development.Although numerical simulations provide accurate predictions,their high time,data,and labor demands call for a swifter,yet precise,method.This study introduces the DuongeCNNeLSTM(D-C-L)model,which integrates a convolutional neural network(CNN)with a long short-term memory(LSTM)network and is grounded on the empirical Duong model for physical constraints.Compared to traditional approaches,the D-C-L model demonstrates superior precision,efficiency,and cost-effectiveness in predicting shale oil production.展开更多
Hydrocarbon production in oil and gas fields generally progresses through stages of production ramp-up,plateau(peak),and decline during field development,with the whole process primarily modeled and forecasted using l...Hydrocarbon production in oil and gas fields generally progresses through stages of production ramp-up,plateau(peak),and decline during field development,with the whole process primarily modeled and forecasted using lifecycle models.SINOPEC's conventional gas reservoirs are dominated by carbonates,low-permeability tight sandstone,condensate,volcanic rocks,and medium-to-high-permeability sandstone.This study identifies the optimal production forecasting models by comparing the fitting coefficients of different models and calculating the relative errors in technically recoverable reserves.To improve forecast precision,it suggests substituting exponential smoothing method-derived predictions for anomalous data caused by subjective influences like market dynamics and maintenance activities.The preferred models for carbonate gas reservoir production forecasts are the generalized Weng's,Beta,Class-I generalized mathematical,and Hu-Chen models.The Vapor pressure and Beta models are optimal for forecasting the annual productivity of wells(APW)from gas-bearing low-permeability tight sandstone reservoirs.The Wang-Li,Beta,and Yu QT tb models are apt for moderate-to-small-reserves,single low-permeability tight sandstone gas reservoirs.The Rayleigh,Hu-Chen,and generalized Weng's models are suitable for condensate gas reservoirs.For medium-to-high-permeability sandstone gas reservoirs,the lognormal,generalized gamma,and Beta models are recommended.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
Based on an analysis of the limitations of conventional production component methods for natural gas development planning,this study proposes a new one that uses life cycle models for the trend fitting and prediction ...Based on an analysis of the limitations of conventional production component methods for natural gas development planning,this study proposes a new one that uses life cycle models for the trend fitting and prediction of production.In this new method,the annual production of old and new wells is predicted by year first and then is summed up to yield the production for the planning period.It shows that the changes in the production of old wells in old blocks can be fitted and predicted using the vapor pressure model(VPM),with precision of 80%e95%,which is 6.6%e13.2%higher than that of other life cycle models.Furthermore,a new production prediction process and method for new wells have been established based on this life cycle model to predict the production of medium-to-shallow gas reservoirs in western Sichuan Basin,with predication error of production rate in 2021 and 2022 being 6%and 3%respectively.The new method can be used to guide the medium-and long-term planning or annual scheme preparation for gas development.It is also applicable to planning for large single gas blocks that require continuous infill drilling and adjustment to improve gas recovery.展开更多
The shale revolution has turned the United States from an oil importer into an oil exporter.The success of shale oil production in the U.S.has inspired many countries,including China,to begin the exploitation and deve...The shale revolution has turned the United States from an oil importer into an oil exporter.The success of shale oil production in the U.S.has inspired many countries,including China,to begin the exploitation and development of shale oil resources.In this study,the production curves of over 30,000 shale oil wells in the Bakken,Eagle Ford(EF)and Permian are systematically analyzed to provide reference and guidance for future shale oil development.To find out the most suitable decline curve models for shale oil wells,fifteen models and a new fitting method are tested on wells with production history over 6 years.Interestingly,all basins show similar results despite of their varieties in geological conditions:stretched exponential production decline(SEPD)+Arps model provides most accurate prediction of estimated ultimate recovery(EUR)for wells with over 2 years'production,while the Arps model can be used before the two years'switch point.With the EUR calculated by decline curve analysis,we further construct simple regression models for different basins to predict the EUR quickly and early.This work helps us better understand the production of shale oil wells,as well as provide important suggestions for the choices of models for shale oil production prediction.展开更多
Accurately predicting the estimated ultimate recovery(EUR)of shale gas wells is key to formulating a shale gas reservoir development plan.However,in practice,determining the EUR remains challenging due to the complex ...Accurately predicting the estimated ultimate recovery(EUR)of shale gas wells is key to formulating a shale gas reservoir development plan.However,in practice,determining the EUR remains challenging due to the complex dynamic characteristics of shale gas production,which first decreases rapidly and then slowly.In this study,based on material balance theory and equivalent seepage resistance theory and considering crucial factors including primary water,adsorption,and pore effects,a new production model for fractured horizontal shale gas wells is developed.The calculation process is designed by using Newton's iterative method.The shale gas well EUR prediction method is verified,and the factors influencing the EUR are analyzed.The results show that adsorption has a significant effect on production,especially on the Langmuir volume.Moreover,ignoring the influence of primary water,which exists in shale gas reservoirs in the form of bound water,results in an overestimation of the EUR.Furthermore,production positively correlates with the fracture half-length and the number of fractures,but the action mechanisms of these two factors differ.Unlike the number of fractures,which predominantly affects the initial stage of production,the fracture half-length has a more nuanced role.It is capable of altering the stimulated reservoir volume zone,thereby exerting influence over the entire production life cycle.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
Using the meteorological data during 1971- 2013 and lemon growth and yield data during 2003- 2013 in Anyue,the suitability problem of lemon growth and correlation problem between meteorological factors and lemon growt...Using the meteorological data during 1971- 2013 and lemon growth and yield data during 2003- 2013 in Anyue,the suitability problem of lemon growth and correlation problem between meteorological factors and lemon growth in Anyue area were studied. According to relevance between the selected meteorological factors and yield of lemon,meteorological prediction model of lemon yield was established in Anyue,and the prediction accuracy was higher. The research had certain guiding significance for management work of lemon production in Anyue area.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
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
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.
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.
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.
文摘In recent years,Bohai Oilfield has continuously intensified its adjustment and potential tapping efforts.Adjustment wells have become a key measure for increasing production in offshore oilfields,contributing significantly to output.However,with the persistent rise in water cut and the increasingly complex distribution of remaining oil,the difficulty of predicting the medium-to-long-term production of adjustment wells has increased substantially,severely constraining the accuracy of development planning.To address this issue,this study systematically analyzes the productivity construction patterns of different potential-tapping types in Bohai Oilfield based on dynamic development data and incorporates a key quantitative indicator of productivity construction effectiveness—the Productivity Coefficient(PC).An innovative multi-dimensional evaluation system for the effectiveness of productivity construction in offshore oilfields has been developed,and a methodology for predicting the medium-to-long-term production of adjustment wells has been established.Application of this method in the Bohai BZ Oilfield indicates that the future average annual production scale of adjustment wells is approximately 7×10^(4) tons to 10×10^(4) tons,with prediction results aligning well with actual development dynamics.This research provides a theoretical foundation and decision-making support for medium-tolongterm production planning in Bohai Oilfield.
基金supported by the Natural Science Foundation of China(Grant No.42302170)National Postdoctoral Innovative Talent Support Program(Grant No.BX20220062)+3 种基金CNPC Innovation Found(Grant No.2022DQ02-0104)National Science Foundation of Heilongjiang Province of China(Grant No.YQ2023D001)Postdoctoral Science Foundation of Heilongjiang Province of China(Grant No.LBH-Z22091)the Natural Science Foundation of Shandong Province(Grant No.ZR2022YQ30).
文摘Prediction of production decline and evaluation of the adsorbed/free gas ratio are critical for determining the lifespan and production status of shale gas wells.Traditional production prediction methods have some shortcomings because of the low permeability and tightness of shale,complex gas flow behavior of multi-scale gas transport regions and multiple gas transport mechanism superpositions,and complex and variable production regimes of shale gas wells.Recent research has demonstrated the existence of a multi-stage isotope fractionation phenomenon during shale gas production,with the fractionation characteristics of each stage associated with the pore structure,gas in place(GIP),adsorption/desorption,and gas production process.This study presents a new approach for estimating shale gas well production and evaluating the adsorbed/free gas ratio throughout production using isotope fractionation techniques.A reservoir-scale carbon isotope fractionation(CIF)model applicable to the production process of shale gas wells was developed for the first time in this research.In contrast to the traditional model,this model improves production prediction accuracy by simultaneously fitting the gas production rate and δ^(13)C_(1) data and provides a new evaluation method of the adsorbed/free gas ratio during shale gas production.The results indicate that the diffusion and adsorption/desorption properties of rock,bottom-hole flowing pressure(BHP)of gas well,and multi-scale gas transport regions of the reservoir all affect isotope fractionation,with the diffusion and adsorption/desorption parameters of rock having the greatest effect on isotope fractionation being D∗/D,PL,VL,α,and others in that order.We effectively tested the universality of the four-stage isotope fractionation feature and revealed a unique isotope fractionation mechanism caused by the superimposed coupling of multi-scale gas transport regions during shale gas well production.Finally,we applied the established CIF model to a shale gas well in the Sichuan Basin,China,and calculated the estimated ultimate recovery(EUR)of the well to be 3.33×10^(8) m^(3);the adsorbed gas ratio during shale gas production was 1.65%,10.03%,and 23.44%in the first,fifth,and tenth years,respectively.The findings are significant for understanding the isotope fractionation mechanism during natural gas transport in complex systems and for formulating and optimizing unconventional natural gas development strategies.
基金supported by the National Natural Science Foundation of China under Grant 52325402,52274057 and 52074340the National Key R&D Program of China under Grant 2023YFB4104200+1 种基金the Major Scientific and Technological Projects of CNOOC under Grant CCL2022RCPS0397RSN111 Project under Grant B08028.
文摘Shale gas wells often face challenges in maintaining continuous and stable production due to their coexistence with high-and low-pressure wells within the same development block,which leads to issues involving mixed-pressure flows.Traditional pipeline optimization methods used in conventional gas well blocks fail to address the unique needs of shale gas wells,such as the precise planning of airflow paths,pressure distribution,and compression.This study proposes a pressure-controlled production optimization strategy specifically designed for shale gas wells operating under mixed-pressure flow conditions.The strategy aims to improve production stability and optimize system efficiency.The decline in production and pressure for individual wells over time is forecasted using a predictive model that accounts for key factors of system optimization,such as reservoir depletion,wellbore conditions,and equipment performance.Additionally,the model predicts the timing and impact of liquid loading,which can significantly affect production.The optimization process involves analyzing the existing gathering pipeline network to determine the most efficient flow directions and compression strategies based on these predictions,while the strategy involves adjusting compressor settings,optimizing flow rates,and planning pressure distribution across the network to maximize productivity while maintaining system stability.By implementing these strategies,this study significantly improves gas well productivity and enhances the adaptability and efficiency of the gathering and transportation system.The proposed approach provides systematic technical solutions and practical guidance for the efficient development and stable production of shale gas fields,ensuring more robust and sustainable pipeline operations.
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funded by the National Natural Science Foundation of China(No.51974356).
文摘Accurately predicting the production rate and estimated ultimate recovery(EUR)of shale oil wells is vital for efficient shale oil development.Although numerical simulations provide accurate predictions,their high time,data,and labor demands call for a swifter,yet precise,method.This study introduces the DuongeCNNeLSTM(D-C-L)model,which integrates a convolutional neural network(CNN)with a long short-term memory(LSTM)network and is grounded on the empirical Duong model for physical constraints.Compared to traditional approaches,the D-C-L model demonstrates superior precision,efficiency,and cost-effectiveness in predicting shale oil production.
基金funded by the project entitled Technical Countermeasures for the Quantitative Characterization and Adjustment of Residual Gas in Tight Sandstone Gas Reservoirs of the Daniudi Gas Field(P20065-1)organized by the Science&Technology R&D Department of SINOPEC.
文摘Hydrocarbon production in oil and gas fields generally progresses through stages of production ramp-up,plateau(peak),and decline during field development,with the whole process primarily modeled and forecasted using lifecycle models.SINOPEC's conventional gas reservoirs are dominated by carbonates,low-permeability tight sandstone,condensate,volcanic rocks,and medium-to-high-permeability sandstone.This study identifies the optimal production forecasting models by comparing the fitting coefficients of different models and calculating the relative errors in technically recoverable reserves.To improve forecast precision,it suggests substituting exponential smoothing method-derived predictions for anomalous data caused by subjective influences like market dynamics and maintenance activities.The preferred models for carbonate gas reservoir production forecasts are the generalized Weng's,Beta,Class-I generalized mathematical,and Hu-Chen models.The Vapor pressure and Beta models are optimal for forecasting the annual productivity of wells(APW)from gas-bearing low-permeability tight sandstone reservoirs.The Wang-Li,Beta,and Yu QT tb models are apt for moderate-to-small-reserves,single low-permeability tight sandstone gas reservoirs.The Rayleigh,Hu-Chen,and generalized Weng's models are suitable for condensate gas reservoirs.For medium-to-high-permeability sandstone gas reservoirs,the lognormal,generalized gamma,and Beta models are recommended.
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.
基金funded by the project entitled Technical Countermeasures for the Quantitative Characterization and Adjustment of Residual Gas in Tight Sandstone Gas Reservoirs of the Daniudi Gas Field(P20065-1)organized by the Science&Technology R&D Department of Sinopec.
文摘Based on an analysis of the limitations of conventional production component methods for natural gas development planning,this study proposes a new one that uses life cycle models for the trend fitting and prediction of production.In this new method,the annual production of old and new wells is predicted by year first and then is summed up to yield the production for the planning period.It shows that the changes in the production of old wells in old blocks can be fitted and predicted using the vapor pressure model(VPM),with precision of 80%e95%,which is 6.6%e13.2%higher than that of other life cycle models.Furthermore,a new production prediction process and method for new wells have been established based on this life cycle model to predict the production of medium-to-shallow gas reservoirs in western Sichuan Basin,with predication error of production rate in 2021 and 2022 being 6%and 3%respectively.The new method can be used to guide the medium-and long-term planning or annual scheme preparation for gas development.It is also applicable to planning for large single gas blocks that require continuous infill drilling and adjustment to improve gas recovery.
基金funded by the National Natural Science Foundation of China(Grant No.52374043)the Key Program of National Natural Science Foundation of China(Grant No.52234003)。
文摘The shale revolution has turned the United States from an oil importer into an oil exporter.The success of shale oil production in the U.S.has inspired many countries,including China,to begin the exploitation and development of shale oil resources.In this study,the production curves of over 30,000 shale oil wells in the Bakken,Eagle Ford(EF)and Permian are systematically analyzed to provide reference and guidance for future shale oil development.To find out the most suitable decline curve models for shale oil wells,fifteen models and a new fitting method are tested on wells with production history over 6 years.Interestingly,all basins show similar results despite of their varieties in geological conditions:stretched exponential production decline(SEPD)+Arps model provides most accurate prediction of estimated ultimate recovery(EUR)for wells with over 2 years'production,while the Arps model can be used before the two years'switch point.With the EUR calculated by decline curve analysis,we further construct simple regression models for different basins to predict the EUR quickly and early.This work helps us better understand the production of shale oil wells,as well as provide important suggestions for the choices of models for shale oil production prediction.
基金supported by the National Natural Science Foundation of China(Grant Nos.52304024,52274034,and 52204035)the Natural Science Foundation of Chongqing,China(Grant No.CSTB2023NSCQ-MSX0264)+1 种基金the Project of Science and Technology Research Program of Chongqing Municipal Education Commission,China(Grant Nos.KJQN202201517 and KJQN202001501)the Research Foundation of the Chongqing University of Science and Technology,China(Grant No.ckrc2022025).
文摘Accurately predicting the estimated ultimate recovery(EUR)of shale gas wells is key to formulating a shale gas reservoir development plan.However,in practice,determining the EUR remains challenging due to the complex dynamic characteristics of shale gas production,which first decreases rapidly and then slowly.In this study,based on material balance theory and equivalent seepage resistance theory and considering crucial factors including primary water,adsorption,and pore effects,a new production model for fractured horizontal shale gas wells is developed.The calculation process is designed by using Newton's iterative method.The shale gas well EUR prediction method is verified,and the factors influencing the EUR are analyzed.The results show that adsorption has a significant effect on production,especially on the Langmuir volume.Moreover,ignoring the influence of primary water,which exists in shale gas reservoirs in the form of bound water,results in an overestimation of the EUR.Furthermore,production positively correlates with the fracture half-length and the number of fractures,but the action mechanisms of these two factors differ.Unlike the number of fractures,which predominantly affects the initial stage of production,the fracture half-length has a more nuanced role.It is capable of altering the stimulated reservoir volume zone,thereby exerting influence over the entire production life cycle.
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.
基金Supported by Government Science Research Item of Anyue County,China(2013-17)
文摘Using the meteorological data during 1971- 2013 and lemon growth and yield data during 2003- 2013 in Anyue,the suitability problem of lemon growth and correlation problem between meteorological factors and lemon growth in Anyue area were studied. According to relevance between the selected meteorological factors and yield of lemon,meteorological prediction model of lemon yield was established in Anyue,and the prediction accuracy was higher. The research had certain guiding significance for management work of lemon production in Anyue area.
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.
