Forward Modeling Research Of Borehole To Surface Electrical Resistivity Method In Shale Gas Reservoir

In today’s rapid socio-economic development, the contradiction between supplyand demand of oil and gas resources have become a bottleneck restricting economicsustainable development in worldwide. Industrialized exploitation in the United Statesmakes shale gas becoming a new focal point for resource development worldwide.Shale gas resource is abundant in China; the marine shale which mainly distributes inthe southern region is about three-fourths of the total. The South complex surfaceconditions lead to severe restrictions on conventional seismic exploration method.Resistivity is one of the important parameters of reservoir evaluation. Electricalsurveying is a geophysical exploration method which is based on the formationresistivity difference and plays an important role in the exploration of oil and gasresources. The borehole to surface electrical resistivity method is one of electricalsurveying methods. It places an electric current electrode in a drill hole and measuresthe distribution of electric potential induced by the resistivity anomaly on the surfaceof the earth. Using the forward modeling and inversion imaging to study boreholecircumferential reservoir resistivity distribution, further understand shale gas reservoirdistribution characteristics.This paper introduces the basic principle and the research present situation ofborehole to surface electrical resistivity method briefly, infers the equations of3Delectric field caused by point power supply. We use finite difference method tocalculate the3D forward of point source for borehole to surface electrical resistivitymethod and study the formula of the grid node. Based on MATLAB, we develop theforward numerical modeling program of point source. In order to avoid the problemof the computation load oversized as well as the high computer memory, we chose themethod of incomplete Cholesky conjugate gradient. At the same time, we adopt thecompressed row storage method according to the sparsity and symmetry of coefficientmatrix to raise the operation efficiency and save the computer memory further. Based on the reliability verification of the program, we establish low resistivityabnormality body models. Then, we study the potential difference caused by thechanging of the depth of point source, the size of the abnormality body. It turns outthat the borehole to surface electrical resistivity method can recognize the lowresistivity abnormality body efficient and it can recognize the boundary of the lowresistivity abnormality body when the point power supply is in the middle of the lowresistivity abnormality body. At last, for the feature of shale reservoirs in JunlianCountry, Sichuan Province, we establish models with five electric horizons and twoshale reservoirs. The forward modelings results show that the borehole to surfaceelectrical resistivity method can recognize the shale reservoir well, but it is not goodat recognize multiple shale reservoirs.