Reservoir rocks of the Pearl River Mouth Basin’s Lufeng Sag have low porosity (average porosity 12.6%) and low permeability (average permeability 16.5 mD), requiring hydraulic fracturing to obtain economic production of oil and gas. To contribute to the understanding of these reservoirs, and to promote successful production in the region, we analyzed the mechanical properties of tight sandstone. Moreover, we introduced the shear/tensile strength factor, in combination with the fracture toughness and horizontal stress difference coefficient, as an innovative approach to characterize the ease of forming a complex fracture network after reservoir fracturing. Based on this, we established a fracability evaluation model suitable for offshore low-permeability sandstone reservoirs by an analytic hierarchy process from the perspective of whether the reservoir can form an effective transformation volume and complex fracture network after fracturing. The results indicate that the primary minerals of the target reservoir are quartz and clay minerals, and the natural fractures are not developed. The mechanical properties exhibit a high Young’s modulus (ranging from 30.4 to 34.4 GPa) and high compressive strength (with cohesion between 41 and 45 MPa and an angle of internal friction between 31.0 and 33.5°). The relatively low tensile strength and fracture toughness values are conducive to fracture initiation and extension during the fracturing process. Through the fracability evaluation model constructed in this paper, the depth interval at 4155.1–4172.1 m is identified as a high-quality fractured layer. The results of this study not only provide theoretical guidance for target well and formation selection in the Lufeng Sag, but also have important practical implications for increasing oil and gas production from tight sandstone reservoirs.

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.

The Sha3 member of the BZ Oilfield in Bohai Sea represents a typical ultra-low permeability thin interbedding reservoir. Hydraulic fracturing is an effective method for increasing oil and gas production. However, the complex geological conditions make it difficult to screen out the optimal frac spot. Consequently, the design of hydraulic fracturing exhibits certain limitations. A novel method for evaluating sweet spots is proposed, based on a refined geological reservoir model that considers the varying degrees of influence that geological and engineering factors have on fracturing effectiveness. This method utilizes grey correlation and hierarchical analysis to quantify and characterize the weight coefficients of each factor, ultimately leading to a combined weight coefficient approach. The results indicate that permeability and fracturing scale are the primary factors that impact the post-frac production of the BZ oilfield, with their respective combined weights being 0.33 and 0.25. The average weight for geological factors is 0.18, while for engineering factors it is 0.15. This suggests that geological factors have a greater influence on production than engineering factors. Furthermore, the correlation coefficient between the dual sweet spot index and the production is 0.96, indicating a strong positive correlation between the two variables. After comparing the predicted and actual production of each well, it was determined that the anastomosis rate is 80%. This finding holds significant guiding significance for selecting the optimal fracturing spot.

Deep coalbed methane development faces technical challenges, such as high in situ stress and low permeability. The dynamic evolution of fractures after hydraulic fracturing and the flowback mechanism are crucial for optimizing productivity. This paper focuses on the inversion of post-fracturing fracture volume parameters and dynamic analysis of the flowback in deep coalbed methane wells, with 89 vertical wells in the eastern margin of the Ordos Basin as the research objects, conducting systematic studies. Firstly, through the analysis of the double-logarithmic curve of normalized pressure and material balance time, the quantitative inversion of the volume of propped fractures and unpropped secondary fractures was realized. Using Pearson correlation coefficients to screen characteristic parameters, four machine learning models (Ridge Regression, Decision Tree, Random Forest, and AdaBoost) were constructed for fracture volume inversion prediction. The results show that the Random Forest model performed the best, with a test set R² of 0.86 and good generalization performance, so it was selected as the final prediction model. With the help of the SHAP model to analyze the influence of each characteristic parameter, it was found that the total fluid volume into the well, proppant intensity, minimum horizontal in situ stress, and elastic modulus were the main driving factors, all of which had threshold effects and exerted non-linear influences on fracture volume. The interaction of multiple parameters was explored by the Partial Dependence Plot (PDP) method, revealing the synergistic mechanism of geological and engineering parameters. For example, a high elastic modulus can enhance the promoting effect of fluid volume into the well and proppant intensity. There is a critical threshold of 2600 m³ in the interaction between the total fluid volume into the well and the minimum horizontal in situ stress. These findings provide a theoretical basis and technical support for optimizing fracturing operation parameters and efficient development of deep coalbed methane.

Hydraulic fracturing stands as a pivotal technological approach for enhanced tight gas recovery. This paper investigates the influences of geological and engineering parameters on the vertical extension mechanism of hydraulic fractures. In addition, the feasibility and effectiveness of fracture height prediction method and various fracture height control techniques have been examined. The results indicate that the height of hydraulic fractures decreases with an increase in the thickness of the barrier layers, the stress difference between the barrier and reservoir layers, the difference in tensile strength, and the difference in fracture toughness, whereas it increases with the increasing of difference in elastic modulus between the barrier and reservoir layers. Compred with the difference in Poisson’s ratio, the volume of fracturing fluid, discharge rate, and fluid viscosity have little impactd. The influence of these factors on fracture height, in descending order, is stress difference between barrier and reservoir layers, fracturing fluid viscosity, fracturing discharge, fracturing fluid volume, barrier layer thickness, tensile strength difference between barrier and reservoir layers, elastic modulus difference between barrier and reservoir layers, Poisson’s ratio difference between barrier and reservoir layers. Furthermore, based on typical geomechanic and reservoir parameters of the target area, a fracture height prediction workflow has been developed. Engineering practice has proven the reliability of fracture height prediction method. The results of this study provide theoretical support and guidance for predicting fracture morphology, controlling fracture height in the hydraulic fracturing development of the tight gas reservoir, and optimizing fracturing process design.