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The reservoir space of tight reservoir with low-permeability is controlled by micro and nano scale pores, making the influence of capillary force significantly enhanced. Therefore, understanding the microscopic charging characteristics of oil and gas is the basis for analyzing the migration and accumulation of reservoirs. In this paper, the self-developed on-line three-dimensional microscopic imaging system for core fluid displacement is used to observe the oil charging process of two tight reservoir samples, and core-level and pore-level quantitative analysis methods for oil content characteristics are proposed. Taking the on-line nuclear magnetic resonance testing with the same process of displacement as a contrast, it is revealed that the average difference of on-line 2D DR (Digital Radiography) images at different times can be used to evaluate the overall oil content change of the sample. The calculation method of pore level fluid saturation based on high-precision pore network extraction algorithm realizes the quantitative evaluation of oil charging degree of the CT resolved pores and pore throats. The combination of multi-level data and different methods can meet the different needs of different researches on dynamic feature capture, pore resolution and imaging field of vision. The analysis results show that the oil saturation of two rock samples from different tight reservoirs in the Ordos Basin increases rapidly at the beginning and slows down later with oil injection increasing. At the same injection flow rate, the oil saturation of the sample with higher permeability increases faster at the initial oil charging stage, making its final oil saturation higher. With the increase of oil injection, the oil saturation of macropores in the sample with higher permeability continuously increases, while that of macropores in the sample with lower permeability shows a U-shaped change, showing the characte-ristics of repeated occupation of pores by oil and water.

The strong heterogeneity characteristics of deep-buried clastic low-permeability reservoirs may lead to great risks in hydrocarbon exploration and development, which makes the accurate identification of reservoir lithofacies crucial for improving the obtained exploration results. Due to the very limited core data acquired from deep drilling, lithofacies logging identification has become the most important method for comprehensively obtaining the rock information of deep-buried reservoirs and is a fundamental task for carrying out reservoir characterization and geological modeling. In this study, a machine learning method is introduced to lithofacies logging identification, to explore an accurate lithofacies identification method for deep fluvial-delta sandstone reservoirs with frequent lithofacies changes. Here Sangonghe Formation in the Central Junggar Basin of China is taken as an example. The K-means-based synthetic minority oversampling technique (K-means SMOTE) is employed to solve the problem regarding the imbalanced lithofacies data categories used to calibrate logging data, and a probabilistic calibration method is introduced to correct the likelihood function. To address the situation in which traditional machine learning methods ignore the geological deposition process, we introduce a depositional prior for controlling the vertical spreading process based on a Markov chain and propose an improved Bayesian inversion process for training on the log data to identify lithofacies. The results of a series of experiments show that, compared with the traditional machine learning method, the new method improves the recognition accuracy by 20%, and the predicted petrographic vertical distribution results are consistent with geological constraints. In addition, SMOTE and probabilistic calibration can effectively handle data imbalance problems so that different categories can be adequately learned. Also the introduction of geological prior has a positive impact on the overall distribution, which significantly improves the accuracy and recall rate of the method. According to this comprehensive analysis, the proposed method greatly enhanced the identification of the lithofacies distributions in the Sangonghe Formation. Therefore, this method can provide a tool for logging lithofacies interpretation of deep and strongly heterogeneous clastic reservoirs in fluvial-delta and other depositional environments.

It is of great significance to study the spatial-temporal variations of the thickness of the gas hydrate stability zone (GHSZ) to understand the decomposition, migration, accumulation and dissipation of gas hydrate, the corresponding relationship between bottom-simulating reflectors (BSRs) and gas hydrate, and the distribution of heterogeneous gas hydrate. We selected the Dongsha region in the South China Sea (SCS) as the research object to calculate the spatial-temporal variation of the GHSZ since 10 Ma, analyzed the main factors affecting the thickness of the GHSZ, discussed the dynamic accumulation processes of gas hydrate, and proposed an accumulation model of gas hydrate in the Dongsha region. The results show that the thicknesses of the GHSZ in the study area were between 0 m and 100 m from 10 to 5.11 Ma, and the relatively higher bottom water temperature (BWT) was the key factor leading to the thinner thickness of the GHSZ during this period. From 5.11-0 Ma, the thickness of the GHSZ gradually increased but showed several fluctuations in thickness due to changes in the geothermal gradient, seawater depth, BWT, and other factors. The decrease in the BWT was the main factor leading to GHSZ thickening from 5.11 to 0 Ma. The thicknesses of the GHSZ are between 110 m and 415 m at present. The present spatial distribution features show the following characteristics. The GHSZ in the deep canyon area is relatively thick, with thicknesses generally between 225 m and 415 m, while the GHSZ in other areas is relatively thin, with thicknesses between 110 m and 225 m. Based on the characteristics of the GHSZ, two hydrate accumulation models are proposed: a double-BSRs model due to thinning of the GHSZ and a multilayer hydrate model due to thickness changes of the GHSZ, with single or multiple BSRs.

The current study examines the methane gas hydrate stability zone (GHSZ) in the Shenhu area in the northern South China Sea (SCS) as an example to calculate the thickness of the GHSZ and reconstruct its evolution since 8.2 Ma. Two mechanisms for typical double BSRs in the Shenhu area are shown, and the relationship between the evolving thickness of the GHSZ and the dynamic accumulation of NGHs at typical stations in the Shenhu area is clarified. The results show that the thickness of the GHSZ varies over time with overall thickening in the Shenhu area. The current thickness of the GHSZ is between 160.98 and 267.94 m. Two mechanisms of double BSRs in the Shenhu area are summarized: the double BSRs pattern based on changes in formation temperature, pressure and other conditions and the double BSRs pattern based on differences in gas source and composition. The formation process and occurrence characteristics of double BSRs and hydrate at site SH-W07-2016 in the Shenhu area are also closely related to the changes in thickness of the GHSZ. In addition, the age when gas source first enters the GHSZ has a considerable influence on the dynamic accumulation process of hydrate. Since the formation of hydrate above the BSR at site SH-W07-2016, the GHSZ has experienced up to two periods of thickening and two periods of thinning at this site. With the changes in the thickness of the GHSZ, up to two stages of hydrate formation and at most two stages of hydrate decomposition have occurred. This paper is of great value for understanding the formation of multiple bottom-simulating reflectors (BSRs) as well as the migration, accumulation and dissipation of natural gas hydrate (NGH) during the dynamic accumulation process.

Subsurface pore pressure affects the direction of hydrocarbon migration, determines the distribution of the hydrocarbon reservoir, and provides scientific reference for drilling planning. Overpressures are widespread in the Paleogene Shahejie Formation in the Linnan Sag, which is closely related to the distribution of oil reservoir. However, the overpressure generation mechanisms are undefined, let alone the relationship between the evolution of paleo-overpressure and hydrocarbon migration in the Linnan Sag, which brings great challenges for the understanding of oil accumulation and future oil exploration. Basin modeling was carried out to solve the issue of quantitative evaluation of overpressure mechanisms and to restore the overpressure evolution of the Paleogene source rocks. The implications for the pore pressure prediction and oil migration in the Linnan Sag were further discussed. The modeling results show that the disequilibrium compaction of mudstones is a dominated overpressure mechanism of source rocks in the Linnan Sag, which accounts for approximately 90% of the measured overpressure in the region. The remainder part of overpressure was generated by hydrocarbon generation; however, the effects of hydrocarbon generation on overpressure evolution were limited in the intervals deeper than 4000 m. The significance of the overpressure mechanism is that the porosity-dependent method will give a satisfactory pressure prediction result in the current exploration depth range (3800–4300 m). The overpressure evolution of the source rock has undergone a cycle of “accumulation-dissipation-reaccumulation,” which corresponds to the age of 45.5–24.0 Ma (Es3-Ed period), 24.6–14.0 Ma (Ed period), and 14.0–0 Ma (Ng-Qp period). The oil potential of the Es3l shows good inheritance with the overpressure in the source rock, indicating overpressure increased the driving force for oil migration. The oil released from the source rock has a trend to migration from the center of the sag to the uplift belt, which is also indicated by the physical properties of crude oil. The knowledge of the generation and evolution of overpressure has great significance for further exploration in the Linnan Sag and other extensional basins.