A LWD gas-reservoir recognition method based on the inelastic gamma information of the drill collar

Neutron logging technology can identify gas reservoirs by detecting thermal neutron or secondary gamma information, which is of great significance for real-time reservoir evaluation in the logging while drilling (LWD). Compared to other methods, the method using inelastic gamma information is more promising in the gas-layer identification, because the inelastic gamma information overcomes the shortcoming that thermal neutron and capture gamma information are easily affected by the content of elements with the high capture cross-section. However, it is still influenced by formation density, especially in the more complex LWD environments. To solve this problem, a new LWD gas-reservoir recognition method based on inelastic gamma information of drill collar is proposed. Based on Monte Carlo method, a set of LWD instrument-formation models of controllable source neutron logging is built to simulate the inelastic gamma information. Through the analysis of the inelastic gamma spectrum in the LWD, the inelastic gamma information of drill collar is extracted to conduct gas-reservoir recognition, and the influence of environment factors on the new method is studied. The study shows that the drill collar inelastic gamma is linearly related with the fast neutron, which means the drill collar inelastic gamma inherits the advantages of the fast neutron and has higher detection efficiency. Compared with the total inelastic gamma, the inelastic gamma of drill collar is less affected by formation density decay and has higher sensitivity. In terms of environmental factors, the drill collar inelastic gamma is related with formation lithology, wellbore fluid, wellbore size, and it is not affected by formation water salinity basically. Ferrous shale formations can increase the inelastic gamma yield of drill collar and affect the recognition result. In general, this gas reservoir recognition method has high sensitivity and is less affected by environmental factors, and has broad application prospects in LWD.

Automated summary

Neutron logging technology can identify gas reservoirs by detecting thermal neutron or secondary gamma information, with the method using inelastic gamma information being more promising. A new LWD gas-reservoir recognition method based on inelastic gamma information of drill collar is proposed, which is less affected by formation density decay and has higher detection efficiency. This recognition method has high sensitivity and broad application prospects in LWD.