The proportion of different lithofacies is essential to determine the net-gross ratio, which directly affects the calculation of oil and gas reserves. Under the condition of few wells, the reliability of different lithofacies proportions obtained using well data is poor because it is difficult to estimate the uncertainty variation range of different lithofacies proportions. The present study overcomes this problem using an uncertainty evaluation method for reservoir lithofacies proportion. First, different lithofacies proportions of the reservoir are determined using the well and seismic data and are considered as the most likely estimated. Based on the lithofacies proportion, the lithofacies models of the study area are developed. Second, uniform and random methods are applied to sample the lithofacies models, and then, the probability distribution functions of different lithofacies proportions are derived. Third, the three levels of lithofacies proportions in the study area are calculated using the method of quantile and discretization of the probability distribution. Finally, we take M gas field as a sample and compare the test results of the fixed and random well structures. The results show that the sampling results of the fixed well structure model significantly deviate under the influence of the extension direction of the channel and well pattern distribution. Therefore, it is suggested to use random well structure to determine the uncertainty distribution range of lithofacies proportion.

Bar top hollows (BTHs) are morphological elements recognized in modern braided rivers; however, information regarding their depositional features and types of filling units in ancient strata is unclear. This is an important reason behind why it is difficult to identify BTH units in subsurface reservoirs. A Middle Jurassic dryland sandy braided river outcrop in northwestern China is characterized in this study through the application of an unmanned aerial vehicle (UAV) surveying and mapping, and ground penetrating radar (GPR). A workflow of UAV data processing has been established, and a 3D digital outcrop model has been built. By observation and measurement of the outcrop model and GPR profiles, two types of BTH filled units were found: a) sandstone-filled, and b) mudstone-filled hollows. Both of these units were located between two adjacent bar units in an area that is limited to a compound bar, and were developed in the upper part of a braided bar depositional sequence. The ellipse-shaped, sandstone-filled unit measures 10 m × 27 m in map view and reaches a maximum thickness of 5 m. The transversal cross-section across the BTHs displays a concave upward basal surface, while the angle of the inclined structures infilling the BTHs decreases up-section. The GPR data show that, in the longitudinal profile, the basal surface is relatively flat, and low-angle, inclined layers can be observed in the lower- and middle part of the sandstone-filled BTHs. In contrast, no obvious depositional structures were observed in the mudstone-filled BTH in outcrop. The new understanding of BTH has a wide application, including the optimization of CO2 storage sites, fresh water aquifers exploration, and oil and gas reservoir characterization.

Generally, the favorable diagenetic facies belt of the reservoirs is the dessert for hydrocarbon exploration. Traditionally, the research of diagenetic facies is based on core analysis, as a result, it is difficult to make prediction under the condition of sparse wells and less cores. In order to solve this difficulty, the new method to predict the diagenetic facies should be researched. In this research, the low permeability sandstone reservoirs of East China Offshore Gas Fields are selected as the research area, and three research steps have been done. First, by using the core analysis, ACR (Apparent Compaction Ratio) and ADR (Apparent Dissolution Ratio) are selected as the discriminant parameters for the diagenesis, and the quantitative standards are established. Second, the rock-electric relationships according to the diagenetic facies are established, the relationship of well logging parameters (GR, DT, CNCF, RT, DEN) vs. ACR, and the same well logging parameters vs. ADR can be matched by using BP neural network method. Accordingly, the ACR and ADR of the whole well can be obtained, and the diagenetic facies in accordance with the ACR and ADR can be predicted. Third, the relationship of well-log and seismic data can be established. After that, the relationship of seismic attributes (Ip - p wave impedance, Is - s wave impedance, Vp/Vs - p and s wave velocity ratio) vs. ACR, and the same seismic attributes vs. ADR can be matched. After the three steps, the favorable diagenetic facies belt can be predicted by using seismic attributes under the condition of sparse wells and less cores. By using the above methods, the diagenetic facies in the research area can be divided into six types: mid dissolution and mid compaction; mid dissolution and mid-strong compaction; strong dissolution and strong compaction; mid-strong dissolution and strong compaction; shale-silty strong compaction; strong cementation. The previous three of the six types are selected as the favorable diagenetic facies. According to the quantitative standards of ACR & ADR, and the relationship of the seismic attributes vs. ACR & ADR, the diagenetic facies in the plane and vertical can be predicted by using seismic attributes. In the research area, three layers of Huagang formation have been selected by fitting the favorable diagenetic facies: Layer 1 (Shallow) corresponding to mid dissolution and mid compaction; Layer 2 (Mid) corresponding to mid dissolution and mid-strong compaction; Layer 3 (Deep) corresponding to strong dissolution and strong compaction. As a result, these three layers are considered to be the desserts for exploration and development. This paper presents a new quantitative characterizing system for diagenetic facies by combing core-logging-seismic. Furthermore, a new method to quantitative characterizing diagenetic facies by using seismic attributes is established for the first time. The innovative content presented in this paper can provide theoretical references for exploration of sandstone reservoirs under the condition of sparse wells, and also provide complement for the diagenetic facies characterizing theories.

The seepage mechanism plays a crucial role in low-permeability gas reservoirs. Compared with conventional gas reservoirs, low-permeability sandstone gas reservoirs are characterized by low porosity, low permeability, strong heterogeneity, and high water saturation. Moreover, their percolation mechanisms are more complex. The present work describes a series of experiments conducted considering low-permeability sandstone cores under pressuredepletion conditions (from the Xihu Depression in the East China Sea Basin). It is shown that the threshold pressure gradient of a low-permeability gas reservoir in thick layers is positively correlated with water saturation and negatively correlated with permeability and porosity. The reservoir stress sensitivity is related to permeability and rock composition. Stress sensitivity is generally low when permeability is high or in the early stage of gas reservoir development. It is also shown that in sand conglomerates, especially the more sparsely filled parts, the interstitial materials among the conglomerates can be rapidly dislodged from the skeleton particles under stress. This material can therefore disperse, migrate, and block the pore throat producing serious, stress-sensitive damage.