Hydraulic fracturing technology is vital for the efficient extraction of oil and gas from low-permeability tight sandstone reservoirs.Taking the central Bohai oilfield in China as an example, these fields are typically composed of thinly interbedded tight sandstone, characterized by low permeability and significant lithological heterogeneity between layers. Fractures may either be confined, limiting vertical growth and reducing production, or overextend into water-bearing zones, causing contamination and compromising reservoir integrity. Therefore, predicting vertical fracture propagation during field fracturing operations is critical for efficient resource extraction.However, there is still a lack of comprehensive understanding of the mechanisms governing vertical fracture growth offshore.This paper applies numerical simulations based on the finite element method to elucidate the interlayer fracture propagation behavior in low-permeability tight sandstone reservoirs. A fracture propagation model for thin interlayered tight sandstone formations is constructed, and the effects of various factors on hydraulic fracture propagation are systematically analyzed, including geological factors such as interlayer stress contrast, thickness, and differences in elastic modulus, as well as operational parameters including fracturing fluid viscosity and injection rate. This study clarifies the cross-layer propagation patterns of hydraulic fractures under the influence of multiple factors and yields a comprehensive prediction chart for fracture propagation thickness under the combination of complex factors. The results of this research can provide theoretical support for the design of reservoir stimulation operations in low-permeability tight sandstone oilfields.

To enhance oil and gas recovery, a novel hydrophobic terpolymer was synthesized via free radical polymerization. The terpolymer consists of acrylamide, acrylic acid, and hydrophobic monomers, and is used as a hydraulic fracturing fluid thickener for freshwater environments. Hydrophobic groups were introduced into terpolymer to improve its tackiness and temperature resistance. The conformation and key parameters of hydrophobic monomers at different temperatures were investigated through a combination of experiments and molecular dynamics simulations. These methods were employed to elucidate the mechanism behind its high-temperature resistance. The experiment results show that, at concentrations between 0.2% and 0.4%, significant intermolecular aggregation occurs, leading to a substantial increase in solution viscosity. Configuring the base fluid of synthetic polymer fracturing fluid with 1% doping, the apparent viscosities of the base fluid were 129.23 mPa·s and 133.11 mPa·s, respectively. The viscosity increase rate was 97%. The base fluid was crosslinked with 1.5% organozirconium crosslinker to form a gel. The controlled loss coefficient and loss velocity of the filter cake were C₃ = 0.84 × 10⁻³ m/min^1/2 and v_c = 1.40 × 10⁻⁴ m/min at 90 °C, meeting the technical requirements for water-based fracturing fluid. Molecular dynamics simulations revealed that the radius of gyration of the hydrophobically linked polymer chain segments decreases as the temperature increases. This is due to the increased thermal motion of the polymer chain segments, resulting in less stretching and intertwining of the chains. As a result, the polymer chains move more freely, which decreases the viscosity of the solution. In conclusion, the proposed fracturing fluid thickener system demonstrates excellent overall performance and shows significant potential for application in oil and gas recovery.

The oil and gas resources in the deep Paleogene system of the South China Sea are abundant. However, due to its poor reservoir physical properties and strong heterogeneity, the deep Paleogene system needs to be commercially exploited by hydraulic fracturing technology. In view of the challenges of offshore low-permeability reservoirs, large-scale fracturing is not allowed because of the limited operation sites and complex string structure. Taking the H oilfield in the South China Sea as the target, based on the concept of the integration of geologic and engineering techniques, parameters such as the number of fracturing stages and the fracture length were optimized by a numerical simulation, and a study on the slurry rate and fracturing scale was carried out based on the type of fracturing and the pipe string structure. The results show that multistage fracturing technology is available in low-permeability offshore oil fields. It is suggested to adopt networking fracturing technology with a “slick water + high slurry rate” framework. A higher rate is recommended, and the fracturing scale of each stage should be 50 m³ of the sands and 700 m³ of the fluids. This research provides a new model for offshore low-permeability oilfield development.

Deep coalbed methane (CBM) is rich in resources and is an important replacement resource for tight gas in China. Accurate prediction of post-fracture production and dynamic change characteristics of fractured wells of partial CBM is of great significance in predicting the final recovery rate. In terms of predicting time-series production, the problem one encounters is low prediction accuracy and poor generalisation ability under limited sample conditions. In this paper, we propose a hybrid deep neural network (AT-GRU-MTL) production prediction model based on the combination of an attention mechanism gated recurrent neural network (GRU) and multi-task learning (MTL), where the AT-GRU is responsible for capturing the nonlinear pattern of the production change, while introducing an MTL method that includes a cross-stitch network (CSN) and a weighted loss using homoskedasticity uncertainty to automatically determine the degree of sharing between multiple tasks and the weighting ratio of the total loss function. The model is applied to several typical deep CBM fracturing wells in China, and the accuracy of gas production prediction reaches 90%, while the accuracy of water production prediction is 68%. The experimental results show that, for the blocks with a very large difference in the order of magnitude of the gas and water production, it is very easy for a certain small order of magnitude to be suppressed from learning during the two-way multi-task learning process, which leads to deterioration of its prediction effect; at the same time, the adaptability of the model is evaluated, and it is found that the model is more advantageous for the wells that have been produced for approximately one year. Meanwhile, the evaluation of the model adaptability shows that the model is more dominant in the prediction of wells with production of about one and a half years. Based on the two test wells with shorter (380 days) and longer (709 days) spans, the results indicate that the model may have insufficient sensitivity to the sudden change of the ratio of gas to water and the failure of the dynamic generalisation of the matrix shrinkage–desorption coupling, and the introduction of physical constraints (such as bottomhole flow pressure, etc.) or the division of the data into the production stages may be attempted to deal with the case subsequently. The research results in this paper provide a theoretical basis for dynamic production prediction and analysis in oil and gas field sites.

Offshore low-permeability thin interbedded reservoirs contain significant oil reserves and are crucial for future development. However, due to the high cost and operational challenges associated with offshore fracturing, large-scale fracturing common in onshore fields is uneconomical. Furthermore, offshore low-permeability reservoirs often have sparse well placement and wide well spacing, in contrast to onshore low-permeability fields, which leads to low recovery. Additionally, there is a lack of comprehensive theory on optimizing the well patterns and fracture networks to maximize net income, highlighting the need for further research. This study tackles these issues in a low-permeability thin interbedded reservoir in the Bozhong Oilfield by using reservoir numerical simulation. First, fracture parameters, including fracture half-length and conductivity, are optimized for different well patterns. Subsequently, well pattern optimization is conducted under fractured conditions, targeting maximum net income under various conditions. The results indicate that when fractures are confined to a single reservoir layer and the main reservoir layer accounts for less than 36% of the development section, fractured directional well patterns yield a higher net income. Conversely, when fractures penetrate all reservoir layers, fractured horizontal wells with closer fracture spacing a higher number of fractures are the most profitable option, particularly in offshore fields with large well spacing. The findings provide critical insights into optimizing well patterns and fracture network designs for offshore low-permeability thin interbedded reservoirs.

The synergistic development of low-permeability reservoirs such as deep coalbed methane (CBM) and tight gas has emerged as a key technology to reduce development costs, enhance single-well productivity, and improve gas recovery. However, due to fundamental differences between coal seams and tight sandstones in their pore structure, permeability, water saturation, and pressure sensitivity, significant variations exist in their flow capacities and fluid production behaviors. To address the challenges of production allocation and main reservoir identification in the co-development of CBM and tight gas within deep gas-bearing basins, this study employs the transient multiphase flow simulation software OLGA to construct a representative dual-gas co-production well model. The regulatory mechanisms of the gas–liquid distribution, deliquification efficiency, and interlayer interference under two typical vertical stacking relationships—“coal over sand” and “sand over coal”—are systematically analyzed with respect to different tubing setting depths. A high-precision dynamic production allocation method is proposed, which couples the wellbore structure with real-time monitoring parameters. The results demonstrate that positioning the tubing near the bottom of both reservoirs significantly enhances the deliquification efficiency and bottomhole pressure differential, reduces the liquid holdup in the wellbore, and improves the synergistic productivity of the dual-reservoirs, achieving optimal drainage and production performance. Building upon this, a physically constrained model integrating real-time monitoring data—such as the gas and liquid production from tubing and casing, wellhead pressures, and other parameters—is established. Specifically, the model is built upon fundamental physical constraints, including mass conservation and the pressure equilibrium, to logically model the flow paths and phase distribution behaviors of the gas–liquid two-phase flow. This enables the accurate derivation of the respective contributions of each reservoir interval and dynamic production allocation without the need for downhole logging. Validation results show that the proposed method reliably reconstructs reservoir contribution rates under various operational conditions and wellbore configurations. Through a comparison of calculated and simulated results, the maximum relative error occurs during abrupt changes in the production capacity, approximately 6.37%, while for most time periods, the error remains within 1%, with an average error of 0.49% throughout the process. These results substantially improve the timeliness and accuracy of the reservoir identification. This study offers a novel approach for the co-optimization of complex multi-reservoir gas fields, enriching the theoretical framework of dual-gas co-production and providing technically adaptive solutions and engineering guidance for multilayer unconventional gas exploitation.