Numerical simulation and dimension reduction analysis of electromagnetic logging while drilling of horizontal wells in complex structures

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.

Flow pattern identification in oil wells by electromagnetic image logging

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.