CAS Engineering Laboratory for Deep Resources Equipment and Technology, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing, China
CAS Engineering Laboratory for Deep Resources Equipment and Technology, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing, China
Wenxuan Chen
CAS Engineering Laboratory for Deep Resources Equipment and Technology, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing, China
Wenxiu Zhang
CAS Engineering Laboratory for Deep Resources Equipment and Technology, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing, China
Ranming Liu
CAS Engineering Laboratory for Deep Resources Equipment and Technology, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing, China
Xinghan Li
CAS Engineering Laboratory for Deep Resources Equipment and Technology, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing, China
An electromagnetic (EM) logging while drilling (LWD) tool is proposed for deep reading detection ahead of the drill bit. The EM LWD tool consists of one coil transmitter sub and two separated coil receiver subs. We utilize the attenuations of measured voltage signals as indicators for detecting the boundary ahead of the drill bit. The effects of frequency and the tool length on the detection capability of the tool are discussed. Also, the attenuations of coaxial voltage signals and coplanar voltage signals proactively respond to the anisotropic formation boundary ahead of the drill bit. The attenuations of coaxial and coplanar signals are sensitive to variations of horizontal conductivity of a conductive anisotropic target layer, but not sensitive to variations in its vertical component. Moreover, an air hang test verifies that the depth of detection of the tool could reach more than 30 m, which is also confirmed by the numerical simulations with an air-seawater-sediment model. The proposed EM LWD tool for detecting the formation boundary ahead of the drill bit is promising for applications of deep reading detections.
