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At present, tight oil reservoir is a hot spot in the field of oil and gas development, and the effective development of such reservoir needs reservoir evaluation as a guide. Based on the experimental research on the core of tight oil reservoir of Chang7 member in Zhidan area, combined with the method of statistics and numerical simulation, eight parameters of “sand thickness, crude oil viscosity, main throat radius, starting pressure gradient, clay mineral content, rock brittleness index, percentage of movable fluid and reservoir pressure coefficient” are selected, and then, the new method of tight oil reservoir evaluation is obtained after normalizing the each parameter. Through this method, the tight oil reservoir of Chang7 member in Zhidan area can be divided into Class I, Class II and Class III, of which Class I and Class II reservoirs currently have certain development value. When development this kind of tight oil reservoir, large-scale hydraulic fracturing is required, which may affect or destroy the existing original forest protection area, water resource protection area and farmland. Therefore, after considering the environmental safety factors, the reservoir is classified and evaluated, and the development block is selected.

Spontaneous imbibition is an important mechanism in naturally fractured reservoirs. In our previous studies on the effect of imbibition efficiency of ultra-low permeability reservoirs, we mostly focused on the relationship between macroscopic core recovery rate and influential factors. Additionally, we also mainly focused on the factors that control the final imbibition recovery for ultra-low permeability reservoirs. Through a large number of experiments, it was found that the factors affecting imbibition are different in separate stages. However, the relative importance of those factors in different imbibition stages was hardly studied. In this work, we tested six key factors, i.e., the core length, RQI, salinity, interfacial characteristics, initial oil saturation, and oil viscosity, in natural sandstone samples from Chang 6 in the Zichang area. Based on experimental results, we divided the imbibition process into three stages (i.e., the early stage, the middle stage, and the late stage) to quantify the effects of the controlling factors. The results show that the relative importance of the controlling factors is changing during the imbibition process. The weight of importance is obtained for those factors at each stage. In addition, a comparative model is established for the dual-porosity media from Chang 6 formation. The results show that the increase of the rock size can extend the imbibition period for the early and middle stages. Moreover, the weight of importance for the initial oil saturation, interfacial characteristics, and salinity are also analyzed in three imbibition stages. This study provides theoretical support to guide water injection in ultra-low-permeability reservoirs and to understand the formation of energy supplements and oil recovery during the imbibition process.

The sulfur-solubility decreases as a result of the decrease in gas reservoir pressure, leading to the solid-phase sulfur deposition. As a consequence, both the reservoir porosity and permeability decrease, subsequently influencing gas well water breakthrough time (GWWBT) in high sulfur gas reservoirs (HSGRs) with edge/bottom water. To acquire the value of GWWBT, a GWWBT prediction model for HSGRs should be established while taking into account sulfur deposition. Accordingly, based on gas Non-Darcy seepage law and sulfur deposition theory in porous media, a novel GWWBT in high sulfur gas reservoir with bottom water was developed in this study. The effect of dynamic factors (e.g., sulfur deposition, gas Non-Darcy flow, irreducible water saturation, and residual gas saturation) on GWWBT was involved in this model. The GWWBT was calculated via the proposed method and three classical models in five field basic parameters, and was compared with five filed values, respectively. This result indicates that the calculation of proposed method is in closer agreement with the field production data, and illustrates that the new proposed model is more reliable. In addition, the influence of dynamic factors was further discussed in detail by this proposed model.