An improved pulsed neutron method for gas reservoirs identification in the complex buried hill formation of the Bohai Sea

Recent discoveries revealing the substantial production potential of buried hill reservoirs have spurred significant research interest in their exploration. Pulse neutron logging offers significant advantages for evaluating conventional gas reservoirs. The fast neutron elastic scattering cross–section (FNXS) method combines gamma count rates from detectors at varying distances to effectively identify gas reservoirs. However, the complex lithology and low effective porosity of the Bohai Sea buried hill reservoirs–composed of quartz, feldspar, biotite, calcite, dolomite, and illite–result in significantly reduced accuracy of FNXS–based gas identification. The FNXS equation for a single lithology cannot accurately reflect the comprehensive FNXS of the formation, making it ineffective in indicating gas reservoirs responses.

Therefore, this paper employs the logged wells in the Bohai Sea buried hill as representative study cases. Based on geological information and gamma counts from a pulsed neutron tool, an improved FNXS–based method for gas reservoirs identification in the complex formation is developed through theoretical analysis and numerical simulations. Firstly, key mineral information provided by the XRF–calibrated lithology profile is used to construct an inversion model database for single–mineral FNXS. Based on the mineral content of different intervals, the FNXS inversion formula is adaptively constructed from the database to accurately calculate the comprehensive formation FNXS (FNXS_lith). Simulation results verified that the error between FNXS_lith and the theoretical value is within 1%, which is significantly lower than that of the conventional method. Secondly, to eliminate the interference of matrix FNXS variations on gas reservoirs identification, the comprehensive FNXS of the water–saturated formation (FNXS_sum^w) is calculated using the effective porosity curve. Gas reservoirs are dynamically identified by depth-dependent comparison between these two curves.

The method is validated using data from two wells (X and Y) in the Bohai Sea buried hill, which contain both carbonate formation from the Paleozoic and granitic gneiss formation from the Archean. The proposed dynamic gas reservoirs identification method can effectively indicate gas reservoirs. The identification results are consistent with gas logging curves and DST test results, verifying the reliability of the method.