Gas well productivity prediction is an important task in gas field development. In contrast, shale gas production is influenced by many factors in geology and production with strong nonlinear characteristics. Traditional mechanism-based productivity prediction methods are difficult to comprehensively and accurately characterize multi-dimensional and multi-structural types of productivity influencing factors, and it is difficult to quickly solve the production dynamics after shale gas fracturing. To address this problem, based on LSTM and DNN, a novel fitting function-neural network synergistic model for dynamic production productivity prediction of shale gas wells was proposed in this paper. Firstly, the data set was constructed by reorganizing the data dimensions, mixing the time-series parameters such as production and pressure in the early stage of the target well with the static productivity control parameters such as fluid intensity, sand addition intensity, total gas content, brittle mineral content, etc., in order to achieve the prediction of the production curve in the late stage of the target well. Second, based on the real daily gas production data in the field, the Arps productivity curve fitting model was used to filter the productivity data of neighboring wells in the same block to indirectly add a weak physical constraint containing the law of decreasing productivity; based on the strong correlation between single-day production time and production under actual working conditions, a strong physical constraint was added inside the neural network model to improve the productivity time series prediction accuracy and local stability of this model. This improves the prediction accuracy and local stability of the model. Based on this model, a shale gas block in China was predicted to have a future production curve, and the prediction results were cross-validated by k-fold Method. Among them, the effects of neural network model parameters, productivity control parameters and time step on the model accuracy were discussed separately. The results show that the model in this paper has a high accuracy rate. With a small sample of production data from neighboring wells, the model can still capture more production characteristics by using static capacity control parameters such as fluid intensity and pre-production and pressure profiles of the target wells. This study results in this paper provide some guidance for the evaluation of fracturing effect of old wells and the optimization of production parameters of new wells.

Low-frequency distributed acoustic sensing in adjacent wells, a recently emerged fracturing monitoring technology, enables detailed diagnosis of hydraulic fractures. To promote industry understanding of recent advances in low-frequency distributed acoustic sensing technology for hydraulic fracture monitoring and facilitate its large-scale field application, this paper begins with the principles of distributed acoustic sensing. It briefly explains the sensing mechanism and well deployment methods, systematically summarizes research progress in numerical simulation, physical modeling, and field applications during hydraulic fracturing, and concludes by outlining future development directions for low-frequency distributed acoustic sensing technology. Research findings indicate that: ①Low-frequency fiber-optic acoustic sensing technology for hydraulic fracturing delivers high precision and real-time monitoring capabilities. This technology is increasingly being deployed for field fracture monitoring and has garnered significant attention from researchers worldwide. Disposable fiber optic systems offer distinct advantages including simplified deployment, low cost, compact footprint, and excellent value proposition. They represent a promising primary solution for future offset-well fracturing monitoring. Mitigating fiber slippage artifacts’ impact on strain response is therefore paramount for enhancing strain data fidelity in fiber optic sensing applications. ② Forward modeling primarily involves comparative analysis of simulated fiber optic strain fields with actual monitoring data to qualitatively characterize strain patterns. This establishes correlations between distinct fracture propagation types and their corresponding strain signatures, enabling interpretation of hydraulic fracture geometry and growth modes in offset wells. Current strain interpretation models predominantly consider two monitoring configurations: horizontal and vertical offset wells. However, these models fail to characterize fracture deflection induced by stress shadowing, resulting in discrepancies with field monitoring observations. Future work urgently requires developing sophisticated multi-fracture forward models that incorporate stress interference effects and fluid partitioning mechanisms to provide reliable guidance for field data interpretation. ③ Inversion modeling primarily utilizes the Displacement Discontinuity Method (DDM) to construct fracture propagation models and solve for fracture dimensions. Current solution approaches include Least Squares, Picard iteration, Levenberg-Marquardt (L-M) method, and the Delayed Rejection Adaptive Metropolis (DRAM) algorithm. However, none can simultaneously invert fracture geometric parameters in all three spatial dimensions. Future inversion research must focus on optimizing solution algorithms, where effectively mitigating the impact of solution non-uniqueness will be the primary research focus for subsequent algorithmic enhancements. ④Physical simulation experiments primarily integrate distributed optical fiber interrogators based on Optical Frequency Domain Reflectometry (OFDR) technology with True Triaxial fracturing apparatuses to monitor fracture propagation. However, current experimental parameter configurations still fall short of fully replicating field conditions. Optimizing fiber deployment methodologies across diverse rock specimens and advancing the interpretation of laboratory-derived fiber optic data represent critical research priorities for future physical simulation studies. The study concludes that offset-well fiber optic monitoring demonstrates significant potential for interpreting hydraulic fracture dimensions. This technology holds considerable promise as a key enabling technology for addressing critical bottlenecks in unconventional resource development.

Underground fluid-injection operations, such as hydraulic fracturing and enhanced geothermal stimulation, have triggered multiple earthquakes across the globe. Earthquake nucleation models within the rate-and-state friction framework suggest that an increase in fluid pressure favors stable slip. However, certain observations indicate that fluid injected into faults may reduce effective normal stress, promoting fault failure, which highlights the debate on the role of fluids in controlling earthquake fault stability. This paper proposes a rate-and-state friction-based model of earthquake nucleation that incorporates fluid injection and diffusion processes, and extends the stability criteria of the system. The results show that fluid pressure heterogeneity can indeed influence fault stability. Elevated fluid pressure stabilizes faults, however, fluid pressure heterogeneity counteracts this stabilizing effect. The model suggests that pressure heterogeneity above a certain threshold facilitates seismic slip, whereas heterogeneity below this threshold can stabilize it. The results further indicate that this threshold reflects a universal instability criterion inherent to the system, rather than an incidental product of a specific fault or rock type. Accordingly, this study proposes a pressure-heterogeneity index as an operational precursor: Tracking spatiotemporal pore-pressure heterogeneity can guide the traffic-light-style adaptive control of injection. These insights provide a new, mechanism-based explanation for the role of fluids in triggering earthquakes.

High-frequency pressure monitoring (HFPM) is a real-time, easy to operate and cost-effective method for hydraulic fracturing diagnostics. It enables the rapid assessment of injection point depth, fracture initiation position, temporary plugging and diversion effects and mechanical plug sealing. By analyzing water hammer pressure waves stimulated during the fracturing process, it facilitates the evaluation of fracturing stimulation effectiveness, guiding on-site construction decisions, resource allocation rationalization, and the comprehensive development of shale gas reservoirs. In this study, HFPM is employed to evaluate the temporary plugging effect and mechanical plug sealing efficiency in the fracturing of a horizontal shale gas well in Changning. Additionally, the complexity of the fractures is assessed based on the attenuation characteristics of the water hammer pressure wave. A comparative analysis is conducted between the diagnostic effects of HFPM and the pressure increase method before and after temporary plugging. HFPM demonstrates a higher ability to identify temporary plugging turning effects, with a recognition rate increasing from 45% to 75%, and exhibits superior diagnostic performance. The application of HFPM can contribute to the optimization of fracturing process parameters for shale gas horizontal wells and facilitate the efficient development of shale gas reservoirs in China.

Water hammer diagnostics is an important fracturing diagnosis technique to evaluate fracture locations and other downhole events in fracturing. The evaluation results are obtained by analyzing shut-in water hammer pressure signal. The field-sampled water hammer signal is often disturbed by noise interference. Noise interference exists in various pumping stages during water hammer diagnostics, with significantly different frequency range and energy distribution. Clarifying the differences in frequency range and energy distribution between effective water hammer signals and noise is the basis of setting specific filtering parameters, including filtering frequency range and energy thresholds. Filtering specifically could separate the effective signal and noise, which is the key to ensuring the accuracy of water hammer diagnosis. As an emerging technique, there is a lack of research on the frequency range and energy distribution of effective signals in water hammer diagnostics. In this paper, the frequency range and energy distribution characteristics of field-sampled water hammer signals were clarified quantitatively and qualitatively for the first time by a newly proposed comprehensive water hammer segmentation-energy analysis method. The water hammer signals were preprocessed and divided into three segments, including pre-shut-in, water hammer oscillation, and leak-off segment. Then, the three segments were analyzed by energy analysis and correlation analysis. The results indicated that, one aspect, the frequency range of water hammer oscillation spans from 0 to 0.65 Hz, considered as effective water hammer signal. The pre-shut-in and leak-off segment ranges from 0 to 0.35 Hz and 0–0.2 Hz respectively. Meanwhile, odd harmonics were manifested in water hammer oscillation segment, with the harmonic frequencies ranging approximately from 0.07 to 0.75 Hz. Whereas integer harmonics were observed in pre-shut-in segment, ranging from 6 to 40 Hz. The other aspect, the energy distribution of water hammer signals was analyzed in different frequency ranges. In 0–1 Hz, an exponential decay was observed in all three segments. In 1–100 Hz, a periodical energy distribution was observed in pre-shut-in segment, an exponential decay was observed in water hammer oscillation, and an even energy distribution was observed in leak-off segment. In 100–500 Hz, an even energy distribution was observed in those three segments, yet the highest magnitude was noted in leak-off segment. In this study, the effective frequency range and energy distribution characteristics of the field-sampled water hammer signals in different segments were sufficiently elucidated quantitatively and qualitatively for the first time, laying the groundwork for optimizing the filtering parameters of the field filtering models and advancing the accuracy of identifying downhole event locations.

Fracture propagation is affected by multi-metal-veins formed by geological diagenesis in shale during the hydraulic fracturing. However, the influence of multi-metal-veins on fractures propagation remains unclear. To solve the problem, based on the semi-circle bending (SCB) test and the extended finite element (XFEM) theory, the interaction between multi-metal-veins and fractures is investigated. The experimental results reveal that the fractures usually deflect at the upper or lower interfaces between metal veins and rocks (e.g. the specimen S-2), which is different from the propagation behavior of fractures in calcite veins. Meanwhile, the fracture toughness of the specimen S-1 is 24.40% higher than that of the specimen S-2, indicating that the increasing of total thickness of multiple metal veins increases the resistance to the fracture vertical propagation. The simulation results show that the increasing of the number, total thickness of veins, the modulus difference between veins and rock, the approach angle and the notch angle all increase the resistance of the fracture passing through metal veins. The maximum deviation distance (D_max) of the fracture decreases with the number of veins, while thickness combination types of metal veins do not affect D_max. The reduction of the notch angle leads to the more tortuous fracture propagation path. Finally, we propose a new comprehensive fracture network pattern. Fracture networks are divided into two categories, including orthogonal fracture networks and sub-orthogonal fracture networks, and the n divided into six sub-categories further. The research results will provide reference for hydraulic fracturing of shale reservoirs containing multi-metal-veins.