To clarify the dynamic evolution characteristics of reservoir flow units during water injection development, the upper member of the Neogene Guantao formation in Block M of Gudao Oilfield is taken as a case study. Based on logging data, water injection profile test data, subwell data, and production performance data, among others, the flow zone index (FZI static) was proposed as the static evaluation parameter of the flow unit. The relationship between cumulative water injection (W_T) and FZI change () was fitted. Hence, the caused by water injection is combined with the static parameter of flow unit evaluation (FZI static) as the dynamic parameter of flow unit evaluation (FZI dynamic). The comprehensively evaluated reality of flow units in different periods is characterized by formula . The study shows that as the division standard of the flow unit, the FZI has a good correlation with the test results of the water absorption profile, such as water absorption intensity and relative water injection volume. Using the FZI as the static parameter of flow unit evaluation, four types of flow units were divided as follows: type I flow unit, ; type II flow unit, ; type III flow unit, ; type IV flow unit, . The reservoir porosity and permeability characteristics of different flow units are highly correlated. Moreover, the relative permeability curve confirms that different flow units have different seepage capacities. Though, comparing characteristic reservoir parameters in different periods, the reservoir’s physical parameters became more conducive to fluid flow with the water injection development. The increase in the same water injection rate for type I and II flow units was greater than that for type III and IV flow units. Furthermore, when type I flow units were continuously distributed in a large area, high water consumption bands were formed, absorbing most of the water injection in the water injection wells. Hence, the waterflood efficiency was low. The change in different flow units was mainly controlled by the injection production well pattern and W_T. Combined with the relative change characteristics of interlayer flow units, the changes can be divided into increasing type change and decreasing type changes. Finally, according to the distribution characteristics of different flow units, oil saturation, and water flooding results, strategies for tapping the potential of the remaining oil from three aspects (plane, interlayer, and inner layer) were formulated.

Machine learning is the main technical means for lithofacies logging identification. As the main target of shale oil spatial distribution prediction, mud shale petrography is subjected to the constraints of stratigraphic inhomogeneity and logging information redundancy. Therefore, choosing the most applicable machine learning method for different geological characteristics and data situations is one of the key aspects of high-precision lithofacies identification. However, only a few studies have been conducted on the applicability of machine learning methods for mud shale petrography. This paper aims to identify lithofacies using commonly used machine learning methods. The study employs five supervised learning algorithms, namely Random Forest Algorithm (RF), BP Neural Network Algorithm (BPANN), Gradient Boosting Decision Tree Method (GBDT), Nearest Neighbor Method (KNN), and Vector Machine Method (SVM), as well as four unsupervised learning algorithms, namely K-means, DBSCAN, SOM, and MRGC. The results are evaluated using the confusion matrix, which provides the accuracy of each algorithm. The GBDT algorithm has better accuracy in supervised learning, while the K-means and DBSCAN algorithms have higher accuracy in unsupervised learning. Based on the comparison of different algorithms, it can be concluded that shale lithofacies identification poses challenges due to limited sample data and high overlapping degree of type distribution areas. Therefore, selecting the appropriate algorithm is crucial. Although supervised machine learning algorithms are generally accurate, they are limited by the data volume of lithofacies samples. Future research should focus on how to make the most of limited samples for supervised learning and combine unsupervised learning algorithms to explore lithofacies types of non-coring wells.

The current seismic prediction methods of the shale brittleness index are all based on the pre-stack seismic inversion of elastic parameters, and the elastic parameters are transformed by Rickman and other simple linear mathematical relationship formulas. In order to address the low accuracy of the seismic prediction results for the brittleness index, this study proposes a method for predicting the brittleness index of shale reservoirs based on an error backpropagation neural network (BP neural network). The continuous static rock elastic parameters were calculated by fitting the triaxial test data with well logging data, and the static elastic parameters with good correlation with the brittleness index of shale minerals were selected as the sample data of the BP neural network model. A dataset of 1970 data points, characterized by Young’s modulus, Poisson’s ratio, shear modulus, and the mineral brittleness index, was constructed. A total of 367 sets of data points from well Z4 were randomly retained as model validation data, and 1603 sets of data points from the other three wells were divided into model training data and test data at a ratio of 7:3. The calculation accuracy of the model with different numbers of nodes was analyzed and the key parameters of the BP neural network structure such as the number of input layers, the number of output layers, the number of hidden layers, and the number of neurons were determined. The gradient descent method was used to determine the weight and bias of the model parameters with the smallest error, the BP neural network model was trained, and the stability of the brittleness index prediction model of the BP neural network was verified by posterior data. After obtaining Young’s modulus, Poisson’s ratio, and shear modulus through pre-stack seismic inversion, the BP neural network model established in this study was used to predict the brittleness index distribution of the target layer in the study area. Compared with the conventional Rickman method, the prediction coincidence rate is 69.16%, and the prediction coincidence rate between the prediction results and the real value is 95.79%, which is 26.63% higher. The BP neural network method proposed in this paper provides a reliable new method for seismic prediction of the shale reservoir brittleness index, which has important practical significance for clarifying the shale gas development scheme and improving shale gas exploitation efficiency.

In order to improve the combined exploitation efficiency of horizontal and vertical wells, and given the fact that the complex and varied spatial structure of sand bodies in the Fuyu oil layer in the Qian’an area of Songliao Basin leads to significant differences in production characteristics of horizontal wells, the sand body types and internal spatial structure are finely dissected according to the theory of configuration analysis, and the internal spatial structure is divided into three configuration styles: spatial clipping type, overlapping type, and separation type. Then, by comparing the productivity characteristics of horizontal wells with different configurations of sand bodies, combined with the analysis of fluid flow law under horizontal well volume fracturing, a main fracture–fracture network–matrix coupled fluid flow model in a tight reservoir based on composite sand body configuration is established. Combined with the actual volume fracturing the horizontal well area, the productivity curves of each cluster in the horizontal section after numerical simulation of volume fracturing of typical horizontal well groups are extracted, which are divided into four types: high-yield stable type, high-yield two-stage type, high-yield rapid-decline type, and low-yield rapid-decline type, and the coupling relationship between the productivity characteristics of each cluster in the horizontal well volume fracturing and sand body configuration style is established, which provides a theoretical basis for the adjustment of different sand body development methods in subsequent oilfields.

The 8# coal seam in the Benxi Formation of the southeastern margin of the Ordos Basin is a deep coal seam with abundant coalbed methane resources. Calcite veins are commonly developed within the 8# coal seam, and their formation processes and mechanisms have significant implications for the enrichment of deep coalbed methane. Genesis of the calcite veins was analyzed to reveal the impact of the calcite veins formation on coalbed methane accumulation, with an integrated application of petrographic study by thin section, cathodoluminescence analysis, carbon-oxygen isotope analysis, and homogeneous temperature measurements of fluid inclusions. The research findings indicate that the calcite veins in the 8# coal seam can be classified into three stages: C1, C2, and C3. The diagenetic fluids of C1 primarily originated from contemporaneous seawater. The fluids responsible for the formation of C2 primarily consist of organic fluids enriched in biogenic gas, whereas the fluids contributing to the formation of C3 are primarily associated with liquid hydrocarbons originated form decarboxylation of organic matter. Furthermore, the development of both C2 and C3 is influenced by deep hydrothermal fluids resulting from tectonic heating events during the Early Cretaceous. By combining analysis of the hydrocarbon accumulation history and burial history in the study area, it has been established that C2 formation occurred during the Late Triassic to Early Jurassic, while C3 formation took place during the Late Jurassic to Early Cretaceous. The exploration and production practices in the study area have firmly established the crucial significance of the formation and evolution of calcite veins within the 8# coal seam for the migration and accumulation of coalbed methane. The research outcomes provide valuable insights for the exploration of deep coalbed methane enrichment areas.

Oil content and the movability of shale oil are important indicators for the evaluation of continental shale oil. In recent years, the sandwiched shale oil in the fourth member of the Shahejie Formation in the Liaohe Western Depression area of the Bohai Bay Basin has shown great exploration potential, while the understanding of shale oil content and the movability of shale oil is weak. In this study, through a combination of core observations and experiments, we clarified the shale lithofacies types in the fourth member of the Shahejie Formation in the Liaohe Western Depression and explored the influencing factors of the characteristics in the oil-bearing and movability of shales in different lithofacies. The results of the study show that the organic matter content of the shale is high (TOC = 2.2–4.3%), but the maturity of thermal evolution is low (R_o = 0.38–0.55%), and the mineral component is dominated by clay minerals (30.3–72.7%), with quartz, feldspar, and other feldspar minerals developing secondarily. According to the content of organic matter, the mineral component, and the sedimentary structure, five types of lithofacies can be classified: organic-rich laminated clay-bearing felsic shale lithofacies (LS1), organic-rich laminated clay felsic mixed shale lithofacies (LS2), organic-rich layered clay felsic mixed shale lithofacies (LS3), organic-containing massive felsic-bearing clay shale lithofacies (LS4), and organic-containing massive clay felsic mixed shale lithofacies (LS5). The oil content of shale is mainly affected by the organic matter. The rate of increase in oil content of shale is fastest when the organic matter content is between 2 and 4%. The movability of shale oil is mainly controlled by the sedimentary structure, mineral component, and microscopic pore structure; the more the shale laminae is developed, the lower the clay content is, and the more the pore space is developed, the better the movability of shale oil is. Combined with the results of the shale oil content and mobility analysis in the study area, LS2 and LS3 are the dominant lithofacies in the fourth member of the Shahejie Formation in the study area, followed by LS1 >LS5 >LS4, so shale oil exploration should focus on the development of LS2 and LS3.