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.

With the increasing demand for oil exploration and subsurface resource development, density imaging plays an increasingly important role in identifying thin layers. However, conventional density imaging tools are limited by poor vertical resolution and therefore suffer from errors in accurately estimating the thickness and relative dip angle of thin layers. This affects the accurate evaluation of thin layer oil and gas reserves. To address this issue, this study evaluates the feasibility of employing novel methods based on advanced tool design. First, an electronically controllable X-ray source is selected to replace the traditional Cs-137 source, aiming to improve the tool's vertical resolution while reducing the radioactive risks commonly associated with chemical sources. Simulation results show that the X-ray tool provides sufficient depth of investigation with better vertical resolution while maintaining the same level of measurement sensitivity. Once the tool design is established, Fisher's optimal segmentation method is improved to enhance the estimation of thin layer thickness and relative dip angle. This is completed by transforming identifying thin layer interface into a mathematical clustering problem. The thin layer interface is fitted using the nonlinear least squares method, which enables the calculation of its parameters. The results demonstrate a 38.5% reduction in RMSE (root mean square error) for thin layer thickness and a 33.7% reduction in RMSE for relative dip angle, demonstrating the superior performance of enhanced X-ray tool in thin layer identification. This study provides a new perspective on the design of density imaging tools and assessment of thin layer, which can help in future thin layer hydrocarbon reserves evaluation and development decisions.

In the evaluation of cementing quality, quantitatively assessing cement density is crucial along with identifying the cementation degree at the interface using acoustic logging. While the ¹³⁷Cs-based formation density logging method is well-suited for density calculation, its reliance on open-hole environmental measurements poses challenges when inspecting cement density. This work focuses on the quantitative calculation of cement density while considering the radioactive hazards to the environment caused by ¹³⁷Cs source. The proposed approach utilizes a measurement system consisting of an X-Ray source and four gamma detectors. The gamma spectrum characteristics of each detector are analyzed, and the energy spectrum recorded by each detector is distinguished by different energy windows. A forward model is established to relate the gamma counts of each energy window to the formation and cement parameters. By employing a regularized Newton's method based on optimization technique, cement density can be calculated with a controllable error margin of within 0.015 g/cm³. Furthermore, even though X-Ray detection has lower sensitivity to formation parameters compared to ¹³⁷Cs, this method is capable of estimating formation density. Overall, the proposed approach enables the quantitative calculation of cement density and semi-quantitative calculation of formation density, therefore is of significance to the comprehensive evaluation of cementing quality.