Tight sand is an important unconventional reservoir. Aiming at the problem of large unused reserves and the poor development effect of Chang 63 reservoir, this paper researches reunderstanding reservoir and evaluating unused reserves. Employing rock slice and scanning electron microscope (SEM), the experiment of low-field nuclear magnetic resonance (NMR), water-oil relative permeability experiment, reservoir space, movable fluid, and oil-water seepage characteristics was studied. The factors affecting NMR T 2 cutoff, controlling factors of movable fluids, and controlling factors of displacing efficiency in tight sandstone reservoirs are discussed. The study demonstrates that (1) the mean pore volume and permeability are 4.4% and 0.068 mD, respectively. The reservoir pertains to tight sandstone, mainly intergranular pore and dissolution pore, and the intercalated materials are mainly chlorite and illite. (2) The characteristic of the NMR T 2 spectrum has bimodal characteristics and can be subdivided into two classes: left peak dominant and right peak dominant. The mean value of mobile fluid saturation was 17.9%. (3) According to the relative permeability curve pattern, it is divided into four categories, and the mean bound water saturation is 29.9%. The average irreducible oil saturation was 40.6%. The mean oil flooding efficiency was 40.2%. (4) The better the pore-throat relationship, the lower the T 2 cutoff, the stronger ability of the fluid migration ability, and the higher of a percentage of active fluid. The percentage of active fluid in a low-permeability reservoir is affected by the reservoir’s physical property and pore structure.

Proven oil and gas reserves in carbonate rocks comprise a high proportion of oil and gas fields, but these reservoirs have high heterogeneity. It is of great importance to study the micropore structures and percolation characteristics of carbonate rocks for the development of oilfields. In this paper, reservoirs are studied by means of casting sections, high-pressure mercury injection, and water and gas flooding oil phase permeability experiments. Reservoirs are classified into three categories, I, II, and III, by the k-means cluster analysis method. The results show that class I reservoirs are mainly composed of biolimestone with strong dissolution, displacement pressure of 0.016 MPa, median pressure of 0.135 MPa, mercury removal efficiency of 17.15%, well-developed pore throats, and good connectivity. They have the highest reservoir quality index and strong percolation ability. Class II reservoirs are mainly biogenic limestone and granular limestone with intergranular pores, a displacement pressure of 0.098 MPa, a median pressure of 6.026 MPa, and a mercury removal efficiency of 25.82%. The pore throat class is complex, and the sorting is poor. Class III reservoirs are mainly clastic limestone with residual intergranular pores, poor connectivity, displacement pressure of 0.403 MPa, median pressure of 3.77 MPa, mercury removal efficiency of 14.01%, small median radii, and good sorting performance. Relative permeability experiments show that water drive permeability at the isopermeability point is (0.049 10 −3 μm 2 ) higher than that of gas drive (0.041 10 −3 μm 2 ). The permeability of oil and water phases in class I reservoirs is obviously higher than those of class II and III reservoirs. When gas flooding is used, the phase permeability characteristics of class I and II reservoirs are no different than when water flooding is used. The permeability of gas flooding is slightly lower than that of water flooding. Because of the high proportion of micropores in class III reservoirs, gas can easily enter the pores, so the relative permeability of the gas phase increases rapidly. With increases in injection volume, the ultimate oil displacement efficiency of class I reservoirs can reach 53.2%, while those of class II and III reservoirs are 50.7 and 46.1%, respectively. This study provides important guidance for formulating oilfield development plans.

The accurate evaluation of shale oil and gas reservoirs is of great significance to the integrated development of geology and engineering. Based on the core analysis, conventional logging, and array acoustic logging data, the total organic carbon, hydrocarbon generation potential, brittleness, and anisotropy of shale reservoirs were calculated. The p-wave time difference curves calculated by the artificial neural network (ANN) method and the conventional logging curve fitting method were compared. The multiresolution graph-based clustering (MRGC) method was used to classify shale reservoirs into three categories and evaluate the classification results. Brittle minerals such as quartz and feldspar were mainly found to be present in shale reservoirs and clay minerals which mainly consisted of illite. The Chang 73 reservoir is rich in organic matter and has great potential for survival. The p-wave time difference calculated by the fitting formula of the shear wave time difference meter demonstrated high accuracy and did not require a complex ANN model. MRGC method can well classify shale reservoir types. The classification results reduce the interference of human factors and are more scientific and reasonable. This research method is of great significance for the scientific classification and evaluation of shale reservoirs.