Petroleum Science >2019, Issue 5: 1-13 DOI: https://doi.org/10.1007/s12182-019-00374-4
Detection performance and inversion processing of logging-while-drilling extra-deep azimuthal resistivity measurements Open Access
文章信息
作者:Lei Wang, Shao-Gui Deng, Pan Zhang, Ying-Chang Cao, Yi-Ren Fan, Xi-Yong Yuan
作者单位:
School of Geosciences, China University of Petroleum, Qingdao, China; Laboratory for Marine Mineral Resources, Qingdao National Laboratory for Marine Science and Technology, Qingdao, China; CNPC Key Laboratory for Well Logging, China University of Petroleum, Qingdao, China.
投稿时间:2018-11-09
引用方式:Wang, L., Deng, SG., Zhang, P. et al. Pet. Sci. (2019). https://doi.org/10.1007/s12182-019-00374-4
文章摘要
We present systematic investigations on the physics, detection performance and inversion of logging-while-drilling extra-deep azimuthal resistivity measurements (EDARM). First, the definitions of EDRAM measurements are discussed, followed by the derivation of the attenuation and phase-shift geometrical factors to illustrate the relative contributions of formation units to the observed signals. Then, a new definition of detection depth, which considers the uncertainty of inversion results caused by the data noise, is proposed to quantify the detection capability of EDARM. Finally, the Bayesian theory associated with Markov chain Monte Carlo sampling is introduced for fast processing of EDARM data. Numerical results show that EDARM is capable of detecting the azimuth and distance of remote bed boundaries, and the detection capability increases with increasing spacing and resistivity contrast. The EDARM tool can accommodate a large range of formation resistivity and is able to provide the resistivity anisotropy at arbitrary relative dipping angles. In addition, multiple bed boundaries and reservoir images near the borehole are readily obtained by using the Bayesian inversion.
关键词
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Extra-deep azimuthal resistivity measurements (EDARM), Detection performance, Inversion method, Reservoir imaging, Detection of multiple bed boundaries