Mineral content logging evaluation can provide important reference data for the lithofacies classification of oil and gas reservoirs and the formulation of production construction plans. In the era of increasingly advanced big data technology, replacing the traditional logging interpretation work mode based on statistical laws with the mineral content joint inversion technology of conventional logging data is an inevitable development direction of conventional logging interpretation theory. This technology can not only fully utilize a large amount of data from new and old oilfields, improve the interpretation accuracy of complex mineral content, but also greatly improve work efficiency. However, the current joint inversion method for mineral content from conventional logging data still has many problems, such as limitations in core mineral content experimental measurement techniques, incomplete interpretation models, and poor numerical stability of inversion algorithms. This article focuses on the above three issues and conducts in-depth research one by one, proposing a log response model based on mass ratio, and providing a method for obtaining model parameters. At the same time, the VRH model is introduced into the inversion equations, and a high-precision and fast algorithm is designed. Finally, a theoretically feasible solution to the difficulty of poor numerical stability in joint inversion is proposed.