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Log interpretation and evaluation of tight sandstone reservoir in Chang 8 Member of Longdong West area, Ordos Basin, China, are facing great challenges due to the co-development of normal oil pay and resistivity low-contrast oil pay. To better guide the exploration and development of oil resources in this area, the reservoir characteristics and control mechanism of resistivity low-contrast oil pay were studied. Firstly, the reservoirs were divided into resistivity low-contrast oil pay (RLP) and normal oil pay (NP) based on the relative value of the apparent resistivity increase rate. Then, the difference of reservoir characteristics between RLP and NP is analyzed by comparing a series of experimental data and real logging data in those two reservoir types. Finally, the control mechanism of RLP was studied from reservoir micro-factors and regional macro-factors, respectively. It is found that the chlorite and illite are the most abundant clay minerals in RLP and NP, respectively. Compared with NP reservoir, the average porosity of RLP is better, but the pore space is mainly composed of micropores, which lead to smaller average pore throat radius and poor pore structure. The high irreducible water saturation and high formation water salinity reduced the reservoir resistivity from micro-aspect. Besides, the difference of hydrocarbon expulsion capacity of source rock and the regional difference of formation water salinity controlled the distribution of RLP and NP. Comprehensive consideration of the reservoir micro-factors and regional macro-factors is important to carry out effective logging interpretation and evaluation.

Resistivity low-contrast oil pays are a kind of unconventional oil resource with no obvious difference in physical and electrical properties from water layers, which makes it difficult to be identified based on the characteristics of the geophysical well logging response. In this study, the support vector machine (SVM) technology was used to interpret the resistivity low-contrast oil pays in Chang 8 tight sandstone reservoir of Huanxian area, Ordos Basin. First, the input data sequences of logging curves were selected by analyzing the relationship between reservoir fluid types and logging data. Then, the SVM classification model for fluid identification and SVR regression model for reservoir parameter prediction were constructed. Finally, these two models were applied to interpret the resistivity low-contrast oil pays in the study area. The application results show that the fluid recognition accuracy of the SVM classification model is higher than that of the logging cross plot method, back propagation neural network method and radial basis function neural network method. The calculation accuracy of permeability and water saturation predicted by the SVR regression model is higher than that based on the experimental fitting model, which indicates that it is feasible to carry out logging interpretation and evaluation of the resistivity low-contrast oil pays by the SVM method. The research results not only provide an important reference and basis for the review of old wells but also provide technical support for the exploration and development of new strata.

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