Shale oil reservoirs are characterized by complex lithology, complex mineral composition and strong heterogeneity. This causes great difficulty in lithologic evaluation. In this paper, a method of lithology identification is proposed by means of intersection plot method and machine learning method, and lithology evaluation is carried out by combining the calculation of mineral content with a multi-mineral optimization model. The logging response characteristics of five lithologies are analyzed by using the logging curves selected by principal component analysis (PCA) discriminant analysis. In lithology identification, the system clustering algorithm is selected to identify shale oil reservoir lithology through layer-by-layer subdivision of sample lithology classification. Logging data has high vertical resolution and good continuity, and mineral prediction using logging data can ensure high accuracy. In this paper, the method of calculating mineral content by using multi-mineral optimization model has achieved good results in practice.

In mature multilayer gas reservoirs, it is a common problem to evaluate whether a potential reservoir is still productive after casing. Monitoring of unproduced or developing gas formations can guide production action plans. Intervention in unperforated zones with high gas density can increase gas production, whereas intervention in zones with low gas density is uneconomical. An RPM instrument stratum model was established by using the Monte Carlo method in this study. The model was used to simulate the response characteristics of pulsed neutron logging in a sandstone reservoir with 100% gas saturation to different reservoir gas densities, formation porosity, and shale content. The data obtained from the simulation were interpolated to construct a capture gamma ratio plate and a nonbounce gamma ratio plate. Using the constructed chart, the gas density of the reservoir can be quantitatively calculated, and then, the recovery value of the potential gas layer can be evaluated.

Deep and ultra-deep oil, which are unconventional oil reservoirs have become an important field for increasing reserves and production in China’s oil and gas exploration and development, and pose higher requirements for ultra-deep logging technology and equipment. For addressing the technical challenges faced in the development of 260 °C/210 MPa ultra-high temperature and high-pressure logging equipment, based on the CPLog fast and imaging series instruments, this article originally proposes key core technologies and solutions such as 260 °C/210 MPa electromechanical sensors, 13,000 m cable communication, and large borehole correction processing. Through the design indoor testing of a BS-PT-based ultra-high temperature acoustic transducer, 260 °C/210 MPa instrument pressure-bearing housing components, and field testing of large borehole environment testing and 13,000-m cable 8000-m deep well data transmission, it will help develop a prototype of 260 °C/210 MPa ultra-high temperature and high pressure logging equipment. The key technology research of 260 °C/210 MPa logging equipment has laid the foundation for the development of ultra-deep logging equipment and provided technical support for the exploration and development of ultra-deep oil and gas at a depth of 10,000 m.