SVR-based weighted processing method for electrical imaging logging in oil-based mud

Electrical imaging logging in high-resistivity oil-based mud (OBM) has been one of the hotspots in petroleum exploration and development recently. Compared with the traditional electrical imaging logging in water-based mud (WBM), the core issue restricting the application of electrical imaging logging in OBM is that the mud-layer with high impedance prevents the emitted current from flowing into the formation and seriously disturbs the measurement of formation properties. Here, a SVR (support vector machine regression)-based weighted processing method is proposed to resolve the intractable problem and three key parameters are obtained simultaneously. First, the equivalent circuit model of electrical imaging logging in OBM is established and the drawback especially occurring in low-resistivity formation is analyzed. Then, a weighted coefficient applied to the mud-layer impedance is introduced for the quantitative calculation of formation resistivity, and the change of relative formation permittivity and mud-layer thickness can also be depicted with weighted processing. Next, a SVR model is established to inverse the weighted coefficient and then figure out the inversed parameters including the formation resistivity, the relative formation permittivity, and the mud-layer thickness. A stochastic parameter model is adopted to verify the validity of SVR model. Finally, two deviated formation models further test the advantage and robustness of SVR model. The results demonstrate that the SVR-based weighted processing method possesses a terrific performance and all the three parameters can be calculated simultaneously regardless of the borehole wall condition. This study will provide a new approach to the data processing and interpretation of electrical imaging logging in OBM. •A novel weighted method is proposed in electrical imaging logging in OBM.•A SVR model is optimized to obtain the weighted coefficient with measured data.•Three key parameters are inversed simultaneously with the SVR-based model.