The complexity of hydraulic fracture network generation during the fracturing of shale reservoirs is a key indicator of the effectiveness of fracture stimulation. To obtain as large a reservoir stimulation volume as possible, this paper reviews articles on the study of hydraulic fracture propagation mechanism during hydraulic fracturing, analyses the factors affecting hydraulic fracture propagation, and classifies them into two categories: geological factors and engineering factors. In particular, the geological factors affecting hydraulic fracture propagation are classified into five categories: mineral composition of the shale, connections between mineral grains, defects in the shale, geostress, and temperature. Various influencing factors act together, resulting in the hydraulic fracture propagation path is difficult to predict. Therefore, this paper firstly explores the hydraulic fracture propagation pattern under the action of single geological factors and specifies its action mechanism; secondly, it also analyses the hydraulic fracture propagation pattern under the combined action of multiple geological factors and analyses its action mechanism. It is clear that relatively high brittle mineral content and temperature, low stress anisotropy and cementation strength, and a more developed natural fracture network are conducive to the generation of a complex fracture network. By analyzing the influence mechanism of single factors and multiple factors, the influence mechanism of geological factors on hydraulic fracture propagation is identified, guiding the optimal design of hydraulic fracturing.

As the reservoir and its attribute distribution are obviously controlled by sedimentary facies, the facies modeling is one of the important bases for delineating the area of high-quality reservoir and characterizing the attribute parameter distribution. There are a large number of continental sedimentary reservoirs with strong heterogeneity in China, the geometry and distribution of various sedimentary microfacies are relatively complex. The traditional geostatistics methods which have shortage in characterization of the complex and non-stationary geological patterns, have limitation in facies modeling of continental sedimentary reservoirs. The generative adversarial network (GANs) is a recent state-of-the-art deep learning method, which has capabilities of pattern learning and generation, and is widely used in the domain of image generation. Because of the similarity in content and structure between facies models and specific images (such as fluvial facies and the images of modern rivers), and the various images generated by GANs are often more complex than reservoir facies models, GANs has potential to be used in reservoir facies modeling. Therefore, this paper proposes a reservoir facies modeling method based on GANs: (1) for unconditional modeling, select training images (TIs) based on priori geological knowledge, and use GANs to learn priori geological patterns in TIs, then generate the reservoir facies model by GANs; (2) for conditional modeling, a training method of “unconditional-conditional simulation cooperation” (UCSC) is used to realize the constraint of hard data while learning the priori geological patterns. Testing the method using both synthetic data and actual data from oil field, the results meet perfectly the priori geological patterns and honor the well point hard data, and show that this method can overcome the limitation that traditional geostatistics are difficult to deal with the complex non-stationary patterns and improve the conditional constraint effect of GANs based methods. Given its good performance in facies modeling, the method has a good prospect in practical application.

Three-dimensional (3D) geological property modeling is used to quantitatively characterize various geological attributes in 3D space based on geostatistics with the help of computer visualization technology, and the results are often stored in grid data. The 3D geological property modeling includes two main components, grid model generation and property interpolation. In this review article, the existing grid generation methods are systematically investigated, and both traditional and multiple-point geostatistical algorithms involved in interpolation methods are comprehensively analyzed. It is shown that considering the numerical simulation of oil reservoirs, the orthogonal hexahedral grid remains the most suitable grid model for simulations in petroleum exploration and development. For the interpolation methods aspect, most geological phenomena are nonstationary, to simulate various types of reservoirs; the main development trends are increasing geological constraints and reducing the limitation of stationarity. Both methods have certain constraints, and the multiscale problem of multiple-point geostatistics poses a main challenge to the field. In addition, the deep-learning based method is a new trend in geological property modeling.

As an important type of unconventional hydrocarbon, tight sandstone oil has great present and future resource potential. Reservoir quality evaluation is the basis of tight sandstone oil development. A comprehensive evaluation approach based on the gray correlation algorithm is established to effectively assess tight sandstone reservoir quality. Seven tight sandstone samples from the Chang 6 reservoir in the W area of the AS oilfield in the Ordos Basin are employed. First, the petrological and physical characteristics of the study area reservoir are briefly discussed through thin section observations, electron microscopy analysis, core physical property tests, and whole-rock and clay mineral content experiments. Second, the pore type, throat type and pore and throat combination characteristics are described from casting thin sections and scanning electron microscopy. Third, high-pressure mercury injection and nitrogen adsorption experiments are optimized to evaluate the characteristic parameters of pore throat distribution, micro- and nanopore throat frequency, permeability contribution and volume continuous distribution characteristics to quantitatively characterize the reservoir micro- and nanopores and throats. Then, the effective pore throat frequency specific gravity parameter of movable oil and the irreducible oil pore throat volume specific gravity parameter are introduced and combined with the reservoir physical properties, multipoint Brunauer-Emmett-Teller (BET) specific surface area, displacement pressure, maximum mercury saturation and mercury withdrawal efficiency parameters as the basic parameters for evaluation of tight sandstone reservoir quality. Finally, the weight coefficient of each parameter is calculated by the gray correlation method, and a reservoir comprehensive evaluation indicator (RCEI) is designed. The results show that the study area is dominated by types II and III tight sandstone reservoirs. In addition, the research method in this paper can be further extended to the evaluation of shale gas and other unconventional reservoirs after appropriate modification.