In the context of post-stimulation shale gas wells,the terms“shut-in”and“flowback”refer to two critical phases that occur after hydraulic fracturing(fracking)has been completed.These stages play a crucial role in ...In the context of post-stimulation shale gas wells,the terms“shut-in”and“flowback”refer to two critical phases that occur after hydraulic fracturing(fracking)has been completed.These stages play a crucial role in determining both the well’s initial production performance and its long-term hydrocarbon recovery.By establishing a comprehensive big data analysis platform,the flowback dynamics of over 1000 shale gas wells were analyzed in this work,leading to the development of an index system for evaluating flowback production capacity.Additionally,a shut-in chart was created for wells with different types of post-stimulation fracture networks,providing a structured approach to optimizing production strategies.A dynamic analysis method for flowback was also developed,using daily pressure drop and artificial fracture conductivity as key indicators.This method offers a systematic and effective approach to managing the shut-in and flowback processes for gas wells.Field trials demonstrated significant improvements:the probability of sand production was reduced,gas breakthrough time was extended,artificial fracture conductivity was enhanced,and the average estimated ultimate recovery(EUR)per well increased.展开更多
In this article, a partially linear single-index model /or longitudinal data is investigated. The generalized penalized spline least squares estimates of the unknown parameters are suggested. All parameters can be est...In this article, a partially linear single-index model /or longitudinal data is investigated. The generalized penalized spline least squares estimates of the unknown parameters are suggested. All parameters can be estimated simultaneously by the proposed method while the feature of longitudinal data is considered. The existence, strong consistency and asymptotic normality of the estimators are proved under suitable conditions. A simulation study is conducted to investigate the finite sample performance of the proposed method. Our approach can also be used to study the pure single-index model for longitudinal data.展开更多
Probabilistic seismic hazard assessment (PSHA) takes into account as much data as possible for defining the initial seismic source zone model. In response to this, an algorithm has been developed for integration of ge...Probabilistic seismic hazard assessment (PSHA) takes into account as much data as possible for defining the initial seismic source zone model. In response to this, an algorithm has been developed for integration of geological, geophysical and seismological data through a spatial index showing the presence or absence of a potential seismic source feature in the input data. The spatial matching index (SMI) is calculated to define the coincidence of independent data showing any indications for existence of a fault structure. It is applied for hazard assessment of Bulgaria through quantification of the seismic potential of 416 square blocks, 20 × 20 km in size covering the entire territory of Bulgaria and extended by 20 km outside of the country borders. All operations are carried out in GIS environment using its capabilities to work with different types of georeferenced spatial data. Results show that the highest seismic potential (largest SMI) is observed in 56 block elements (13% of the territory) clearly delineating cores of the source zones. Partial match is registered in 98 block elements when one of the features is missing. Not any evidence for earthquake occurrence is predicted by our calculation in 117 elements, comprising 28% of the examined area. The quantitative parameter for spatial data integration which is obtained in the present research may be used to analyze information regardless of its type and purpose.展开更多
基金PetroChina Research Applied Science and Technology Project,“Shale Gas Scale Increase Production and Exploration andDevelopment Technology-Research and Application of Key Technology of Deep Shale Gas Scale Production”(No.2023ZZ21YJ01).
文摘In the context of post-stimulation shale gas wells,the terms“shut-in”and“flowback”refer to two critical phases that occur after hydraulic fracturing(fracking)has been completed.These stages play a crucial role in determining both the well’s initial production performance and its long-term hydrocarbon recovery.By establishing a comprehensive big data analysis platform,the flowback dynamics of over 1000 shale gas wells were analyzed in this work,leading to the development of an index system for evaluating flowback production capacity.Additionally,a shut-in chart was created for wells with different types of post-stimulation fracture networks,providing a structured approach to optimizing production strategies.A dynamic analysis method for flowback was also developed,using daily pressure drop and artificial fracture conductivity as key indicators.This method offers a systematic and effective approach to managing the shut-in and flowback processes for gas wells.Field trials demonstrated significant improvements:the probability of sand production was reduced,gas breakthrough time was extended,artificial fracture conductivity was enhanced,and the average estimated ultimate recovery(EUR)per well increased.
基金Supported by the National Natural Science Foundation of China (10571008)the Natural Science Foundation of Henan (092300410149)the Core Teacher Foundationof Henan (2006141)
文摘In this article, a partially linear single-index model /or longitudinal data is investigated. The generalized penalized spline least squares estimates of the unknown parameters are suggested. All parameters can be estimated simultaneously by the proposed method while the feature of longitudinal data is considered. The existence, strong consistency and asymptotic normality of the estimators are proved under suitable conditions. A simulation study is conducted to investigate the finite sample performance of the proposed method. Our approach can also be used to study the pure single-index model for longitudinal data.
文摘Probabilistic seismic hazard assessment (PSHA) takes into account as much data as possible for defining the initial seismic source zone model. In response to this, an algorithm has been developed for integration of geological, geophysical and seismological data through a spatial index showing the presence or absence of a potential seismic source feature in the input data. The spatial matching index (SMI) is calculated to define the coincidence of independent data showing any indications for existence of a fault structure. It is applied for hazard assessment of Bulgaria through quantification of the seismic potential of 416 square blocks, 20 × 20 km in size covering the entire territory of Bulgaria and extended by 20 km outside of the country borders. All operations are carried out in GIS environment using its capabilities to work with different types of georeferenced spatial data. Results show that the highest seismic potential (largest SMI) is observed in 56 block elements (13% of the territory) clearly delineating cores of the source zones. Partial match is registered in 98 block elements when one of the features is missing. Not any evidence for earthquake occurrence is predicted by our calculation in 117 elements, comprising 28% of the examined area. The quantitative parameter for spatial data integration which is obtained in the present research may be used to analyze information regardless of its type and purpose.
