Permeability is a key parameter of rock reservoirs, suggesting the flow characteristics of rock reservoirs. Permeability prediction of carbonate reservoirs is still a great challenge due to its complex pore network an...Permeability is a key parameter of rock reservoirs, suggesting the flow characteristics of rock reservoirs. Permeability prediction of carbonate reservoirs is still a great challenge due to its complex pore network and wide range permeability. This work is to establish a digital quantum mechanism-based neural network(DQNN) to study the permeability using the digital porosity,coordination number and pore network size. Experiments and artificial neural network methods(ANN) are applied to validate the accuracy of the proposed DQNN method. In these methods, the pore-scale variables extracted from the micro-CT images of200 carbonate samples are applied. Results show that the permeabilities obtained from experimental, artificial neural network and DQNN methods agree well with each other. Digital pore size, pore throat size and length are better parameters, while coordination number and porosity are relatively secondary parameters for permeability descriptions of carbonate reservoirs.Compared with the ANN method, the proposed DQNN method is superior in low computation time and high ability for complicated problems.展开更多
基金supported by the Fundamental Research Funds for the Central Universities(Grant No.2042021kf0058)the National Natural Science Foundation of China(Grant Nos.52027814,51839009 and51679017)。
文摘Permeability is a key parameter of rock reservoirs, suggesting the flow characteristics of rock reservoirs. Permeability prediction of carbonate reservoirs is still a great challenge due to its complex pore network and wide range permeability. This work is to establish a digital quantum mechanism-based neural network(DQNN) to study the permeability using the digital porosity,coordination number and pore network size. Experiments and artificial neural network methods(ANN) are applied to validate the accuracy of the proposed DQNN method. In these methods, the pore-scale variables extracted from the micro-CT images of200 carbonate samples are applied. Results show that the permeabilities obtained from experimental, artificial neural network and DQNN methods agree well with each other. Digital pore size, pore throat size and length are better parameters, while coordination number and porosity are relatively secondary parameters for permeability descriptions of carbonate reservoirs.Compared with the ANN method, the proposed DQNN method is superior in low computation time and high ability for complicated problems.
