The cyclic hydraulic stimulation(CHS) has proven as a prospective technology for enhancing the permeability of unconventional formations such as coalbeds. However, the effects of CHS on the microstructure and gas sorp...The cyclic hydraulic stimulation(CHS) has proven as a prospective technology for enhancing the permeability of unconventional formations such as coalbeds. However, the effects of CHS on the microstructure and gas sorption behavior of coal remain unclear. In this study, laboratory tests including the nuclear magnetic resonance(NMR), low-temperature nitrogen sorption(LTNS), and methane sorption isotherm measurement were conducted to explore changes in the pore structure and methane sorption characteristics caused by CHS on an anthracite coal from Qinshui Basin, China. The NMR and LTNS tests show that after CHS treatment, meso- and macro-pores tend to be enlarged, whereas micropores with larger sizes and transition-pores may be converted into smaller-sized micro-pores. After the coal samples treated with 1, 3, 5 and 7 hydraulic stimulation cycles, the total specific surface area(TSSA)decreased from 0.636 to 0.538, 0.516, 0.505, and 0.491 m^(2)/g, respectively. Fractal analysis based on the NMR and LTNS results show that the surface fractal dimensions increase with the increase in the number of hydraulic stimulation cycles, while the volume fractal dimensions exhibit an opposite trend to the surface fractal dimensions, indicating that the pore surface roughness and pore structure connectivity are both increased after CHS treatment. Methane sorption isothermal measurements show that both the Langmuir volume and Langmuir pressure decrease significantly with the increase in the number of hydraulic stimulation cycles. The Langmuir volume and the Langmuir pressure decrease from 33.47 cm^(3)/g and 0.205 MPa to 24.18 cm^(3)/g and 0.176 MPa after the coal samples treated with 7 hydraulic stimulation cycles, respectively. The increments of Langmuir volume and Langmuir pressure are positively correlated with the increment of TSSA and negatively correlated with the increments of surface fractal dimensions.展开更多
Well production optimization is a complex and time-consuming task in the oilfield development.The combination of reservoir numerical simulator with optimization algorithms is usually used to optimize well production.T...Well production optimization is a complex and time-consuming task in the oilfield development.The combination of reservoir numerical simulator with optimization algorithms is usually used to optimize well production.This method spends most of computing time in objective function evaluation by reservoir numerical simulator which limits its optimization efficiency.To improve optimization efficiency,a well production optimization method using streamline features-based objective function and Bayesian adaptive direct search optimization(BADS)algorithm is established.This new objective function,which represents the water flooding potential,is extracted from streamline features.It only needs to call the streamline simulator to run one time step,instead of calling the simulator to calculate the target value at the end of development,which greatly reduces the running time of the simulator.Then the well production optimization model is established and solved by the BADS algorithm.The feasibility of the new objective function and the efficiency of this optimization method are verified by three examples.Results demonstrate that the new objective function is positively correlated with the cumulative oil production.And the BADS algorithm is superior to other common algorithms in convergence speed,solution stability and optimization accuracy.Besides,this method can significantly accelerate the speed of well production optimization process compared with the objective function calculated by other conventional methods.It can provide a more effective basis for determining the optimal well production for actual oilfield development.展开更多
基金financially supported by the National Natural Science Foundation of China (51904319)the Fundamental Research Funds for the Central Universities (21CX06029A)。
文摘The cyclic hydraulic stimulation(CHS) has proven as a prospective technology for enhancing the permeability of unconventional formations such as coalbeds. However, the effects of CHS on the microstructure and gas sorption behavior of coal remain unclear. In this study, laboratory tests including the nuclear magnetic resonance(NMR), low-temperature nitrogen sorption(LTNS), and methane sorption isotherm measurement were conducted to explore changes in the pore structure and methane sorption characteristics caused by CHS on an anthracite coal from Qinshui Basin, China. The NMR and LTNS tests show that after CHS treatment, meso- and macro-pores tend to be enlarged, whereas micropores with larger sizes and transition-pores may be converted into smaller-sized micro-pores. After the coal samples treated with 1, 3, 5 and 7 hydraulic stimulation cycles, the total specific surface area(TSSA)decreased from 0.636 to 0.538, 0.516, 0.505, and 0.491 m^(2)/g, respectively. Fractal analysis based on the NMR and LTNS results show that the surface fractal dimensions increase with the increase in the number of hydraulic stimulation cycles, while the volume fractal dimensions exhibit an opposite trend to the surface fractal dimensions, indicating that the pore surface roughness and pore structure connectivity are both increased after CHS treatment. Methane sorption isothermal measurements show that both the Langmuir volume and Langmuir pressure decrease significantly with the increase in the number of hydraulic stimulation cycles. The Langmuir volume and the Langmuir pressure decrease from 33.47 cm^(3)/g and 0.205 MPa to 24.18 cm^(3)/g and 0.176 MPa after the coal samples treated with 7 hydraulic stimulation cycles, respectively. The increments of Langmuir volume and Langmuir pressure are positively correlated with the increment of TSSA and negatively correlated with the increments of surface fractal dimensions.
基金supported partly by the National Science and Technology Major Project of China(Grant No.2016ZX05025-001006)Major Science and Technology Project of CNPC(Grant No.ZD2019-183-007)
文摘Well production optimization is a complex and time-consuming task in the oilfield development.The combination of reservoir numerical simulator with optimization algorithms is usually used to optimize well production.This method spends most of computing time in objective function evaluation by reservoir numerical simulator which limits its optimization efficiency.To improve optimization efficiency,a well production optimization method using streamline features-based objective function and Bayesian adaptive direct search optimization(BADS)algorithm is established.This new objective function,which represents the water flooding potential,is extracted from streamline features.It only needs to call the streamline simulator to run one time step,instead of calling the simulator to calculate the target value at the end of development,which greatly reduces the running time of the simulator.Then the well production optimization model is established and solved by the BADS algorithm.The feasibility of the new objective function and the efficiency of this optimization method are verified by three examples.Results demonstrate that the new objective function is positively correlated with the cumulative oil production.And the BADS algorithm is superior to other common algorithms in convergence speed,solution stability and optimization accuracy.Besides,this method can significantly accelerate the speed of well production optimization process compared with the objective function calculated by other conventional methods.It can provide a more effective basis for determining the optimal well production for actual oilfield development.
