In order to improve reservoir fluid recognition, the sensitivity of array resistivity response to the difference of the invasion properties in both oil-bearing layers and water layers is analyzed. Then the primary inversion is carried out based on the array resistivity log. The mud invasion process is numerically simulated based on the oil-water flow equation and water convection diffusion equation. The results show that the radial resistivity of a fresh mud-invaded oil-bearing layer presents complex distribution characteristics, such as nonlinear increase, increasing to decreasing and low resistivity annulus, and the resistive invasion profile of a water layer is monotonic. Under specific conditions, array resistivity log can reflect these changes and the array induction log is more sensitive. Nevertheless, due to the effect of factors like large invasion depth, reservoir physical and oil-bearing properties, the measured apparent resistivity may differ greatly from the actual mud filtrate invasion profile in an oil-bearing layer. We proposed a five-parameter formation model to simulate the complex resistivity distribution of fresh mud-invaded formation. Then, based on the principle of non-linear least squares, the measured array resistivity log is used for inversion with the Marquardt method. It is demonstrated that the inverted resistivity is typically non-monotonic in oil-bearing layers and is monotonic in water layers. Processing of some field data shows that this is helpful in achieving efficient reservoir fluid recognition.
In classical regression analysis, the error of independent variable is usually not taken into account in regression analysis. This paper presents two solution methods for the case that both the independent and the dependent variables have errors. These methods are derived from the condition-adjustment and indirect-adjustment models based on the Total-Least-Squares principle. The equivalence of these two methods is also proven in theory.
Ultra-low porosity and permeability, inhomogeneous fracture distribution, and complex storage space together make the effectiveness evaluation of tight carbonate reservoirs difficult. Aiming at the carbonate reservoirs of the Da＇anzhai Formation in the Longgang area of the Sichuan Basin, based on petrophysical experiments and logging response characteristics, we investigated the storage properties of matrix pores and the characteristics of fracture development to establish a method for the characterization of effectiveness of tight reservoirs. Mercury injection and nuclear magnetic resonance （NMR） experiments show that the conventional relationship between porosity and permeability cannot fully reflect the fluid flow behavior in tight matrix pores. Under reservoir conditions, the tight reservoirs still possess certain storage space and permeability, which are controlled by the characteristic structures of the matrix porosity. The degree of fracture development is crucial to the productivity and quality of tight reservoirs. By combining the fracture development similarity of the same type of reservoirs and the fracture development heterogeneity in the same block, a three-level classification method of fracture development was established on the basis of fracture porosity distribution and its cumulative features. According to the actual production data, based on the effectiveness analysis of the matrix pores and fast inversion of fracture parameters from dual laterolog data, we divided the effective reservoirs into three classes： Class I with developed fractures and pores, and high-intermediate productivity; Class II with moderately developed fractures and pores or of fractured type, and intermediate-low productivity; Class III with poorly developed fractures and matrix pores, and extremely low productivity. Accordingly log classification standards were set up. Production data shows that the classification of effective reservoirs is highly consistent with the reservoir productivity level, providing a new ap
