Electromagnetic logging while drilling(LWD)is one of the key technologies of the geosteering and formation evaluation for high-angle and horizontal wells.In this paper,we solve the dipole source-generated magnetic/ele...Electromagnetic logging while drilling(LWD)is one of the key technologies of the geosteering and formation evaluation for high-angle and horizontal wells.In this paper,we solve the dipole source-generated magnetic/electric fields in 2D formations efficiently by the 2.5D finite diff erence method.Particularly,by leveraging the field’s rapid attenuation in spectral domain,we propose truncated Gauss–Hermite quadrature,which is several tens of times faster than traditional inverse fast Fourier transform.By applying the algorithm to the LWD modeling under complex formations,e.g.,folds,fault and sandstone pinch-outs,we analyze the feasibility of the dimension reduction from 2D to 1D.For the formations with smooth lateral changes,like folds,the simplified 1D model’s results agree well with the true responses,which indicate that the 1D simplification with sliding window is feasible.However,for the formation structures with drastic rock properties changes and sharp boundaries,for instance,faults and sandstone pinch-outs,the simplified 1D model will lead to large errors and,therefore,2.5D algorithms should be applied to ensure the accuracy.展开更多
We propose a novel workflow for fast forward modeling of well logs in axially symmetric 2D models of the nearwellbore environment.The approach integrates the finite element method with deep residual neural networks to...We propose a novel workflow for fast forward modeling of well logs in axially symmetric 2D models of the nearwellbore environment.The approach integrates the finite element method with deep residual neural networks to achieve exceptional computational efficiency and accuracy.The workflow is demonstrated through the modeling of wireline electromagnetic propagation resistivity logs,where the measured responses exhibit a highly nonlinear relationship with formation properties.The motivation for this research is the need for advanced modeling al-gorithms that are fast enough for use in modern quantitative interpretation tools,where thousands of simulations may be required in iterative inversion processes.The proposed algorithm achieves a remarkable enhancement in performance,being up to 3000 times faster than the finite element method alone when utilizing a GPU.While still ensuring high accuracy,this makes it well-suited for practical applications when reliable payzone assessment is needed in complex environmental scenarios.Furthermore,the algorithm’s efficiency positions it as a promising tool for stochastic Bayesian inversion,facilitating reliable uncertainty quantification in subsurface property estimation.展开更多
Petroleum production logging needs to determine the interpretation models first and flow pattern identification is the foundation, but traditional flow pattern identification methods have some limitations. In this pap...Petroleum production logging needs to determine the interpretation models first and flow pattern identification is the foundation, but traditional flow pattern identification methods have some limitations. In this paper, a new method of flow pattern identification in oil wells by electromagnetic image logging is proposed. First, the characteristics of gas-water and oil-water flow patterns in horizontal and vertical wellbores are picked up. Then, the continuous variation of the two phase flow pattern in the vertical and horizontal pipe space is discretized into continuous fluid distribution models in the pipeline section. Second, the electromagnetic flow image measurement responses of all the eight fluid distribution models are simulated and the characteristic vector of each response is analyzed in order to distinguish the fluid distribution models. Third, the time domain changes of the fluid distribution models in the pipeline section are used to identify the flow pattern. Finally, flow simulation experiments using electromagnetic flow image logging are operated and the experimental and simulated data are compared. The results show that the method can be used for flow pattern identification of actual electromagnetic image logging data.展开更多
基金the National Natural Science Foundation of China (41674131,41574118,41974146,41904109)the Fundamental Research Funds for the Central Universities (17CX06041,17CX06044)the China National Science and Technology Major Project (2016ZX05007-004,2017ZX05072-002)
文摘Electromagnetic logging while drilling(LWD)is one of the key technologies of the geosteering and formation evaluation for high-angle and horizontal wells.In this paper,we solve the dipole source-generated magnetic/electric fields in 2D formations efficiently by the 2.5D finite diff erence method.Particularly,by leveraging the field’s rapid attenuation in spectral domain,we propose truncated Gauss–Hermite quadrature,which is several tens of times faster than traditional inverse fast Fourier transform.By applying the algorithm to the LWD modeling under complex formations,e.g.,folds,fault and sandstone pinch-outs,we analyze the feasibility of the dimension reduction from 2D to 1D.For the formations with smooth lateral changes,like folds,the simplified 1D model’s results agree well with the true responses,which indicate that the 1D simplification with sliding window is feasible.However,for the formation structures with drastic rock properties changes and sharp boundaries,for instance,faults and sandstone pinch-outs,the simplified 1D model will lead to large errors and,therefore,2.5D algorithms should be applied to ensure the accuracy.
基金financially supported by the Russian federal research project No.FWZZ-2022-0026“Innovative aspects of electro-dynamics in problems of exploration and oilfield geophysics”.
文摘We propose a novel workflow for fast forward modeling of well logs in axially symmetric 2D models of the nearwellbore environment.The approach integrates the finite element method with deep residual neural networks to achieve exceptional computational efficiency and accuracy.The workflow is demonstrated through the modeling of wireline electromagnetic propagation resistivity logs,where the measured responses exhibit a highly nonlinear relationship with formation properties.The motivation for this research is the need for advanced modeling al-gorithms that are fast enough for use in modern quantitative interpretation tools,where thousands of simulations may be required in iterative inversion processes.The proposed algorithm achieves a remarkable enhancement in performance,being up to 3000 times faster than the finite element method alone when utilizing a GPU.While still ensuring high accuracy,this makes it well-suited for practical applications when reliable payzone assessment is needed in complex environmental scenarios.Furthermore,the algorithm’s efficiency positions it as a promising tool for stochastic Bayesian inversion,facilitating reliable uncertainty quantification in subsurface property estimation.
文摘Petroleum production logging needs to determine the interpretation models first and flow pattern identification is the foundation, but traditional flow pattern identification methods have some limitations. In this paper, a new method of flow pattern identification in oil wells by electromagnetic image logging is proposed. First, the characteristics of gas-water and oil-water flow patterns in horizontal and vertical wellbores are picked up. Then, the continuous variation of the two phase flow pattern in the vertical and horizontal pipe space is discretized into continuous fluid distribution models in the pipeline section. Second, the electromagnetic flow image measurement responses of all the eight fluid distribution models are simulated and the characteristic vector of each response is analyzed in order to distinguish the fluid distribution models. Third, the time domain changes of the fluid distribution models in the pipeline section are used to identify the flow pattern. Finally, flow simulation experiments using electromagnetic flow image logging are operated and the experimental and simulated data are compared. The results show that the method can be used for flow pattern identification of actual electromagnetic image logging data.
