Shale gas, as a clean, low-carbon, and abundant unconventional natural gas resource, plays a crucial role in achieving clean energy transformation and carbon neutrality. The Fuling shale gas reservoir in Sichuan Basin stands out as China's most promising area for shale gas exploration and recovery. However, the continuous recovery of shale gas in the southern Sichuan Basin has led to well interference events in hundreds of wells, with the furthest well distance reaching over 2000 m. This study introduces a multi-scale approach for transient analysis of a multi-well horizontal pad with well interference in shale gas reservoirs. The approach utilizes Laplace transform technology, boundary element theory, and the finite difference method to address the complexities of the system. Well interference is managed using the pressure superposition principle. To validate the proposed multi-scale method, a commercial numerical simulator is employed. The comprehensive pressure behavior of a multi-well horizontal pad in a shale gas reservoir is analyzed, encompassing wellbore storage effect, skin effect, bilinear flow, linear flow, pseudo-radial flow of primary fractures, well interference period, dual-porosity flow, pseudo-radial flow of the total system, and boundary-dominated flow. A case study is conducted on the typical well, the well with the longest production history in the Fuling shale gas reservoir. The rate transient analysis is conducted to integrate up to 229 days of shale gas production daily data and wellhead pressure data, enabling the generation of pressure behavior under unit flow rate. The results indicate that the linear flow, transitional flow, and boundary-dominated flow are more likely to be observed in the actual data. Secondary fractures are considered to be the primary pathways for fluid migration during well interference events. The evaluated formation permeability is 2.58 × 10⁻² mD, the well spacing is 227.8 m, the diffusion coefficient is 1.49 × 10⁻⁴, and the skin factor is 0.09.

Deep learning has been widely recognized in the field of CO₂ geological utilization and storage applications. With the development of deep learning algorithms, intelligent models are gradually able to improve multi-source, multi-scale and multi-physicochemical mechanism barriers with high-fidelity solutions in practical applications. In this perspective, an overview of the traditional and state-of-the-art deep learning architectures involved in CO₂ geological utilization and storage is outlined in terms of evolutionary trajectories. Meanwhile, the favorable directions and application scenarios of different deep learning algorithms for geo-energy intelligence modeling are summarized. Moreover, further insights into the future direction of deep learning burgeoning architectures in this field are provided. The physics-guided deep learning, explainable artificial intelligence, and generative artificial intelligence are expected to deliver more accurate solutions for information extraction and decision support within the CO₂ geological utilization and storage communities.

This paper presents a new semi-analytical solution and the related methodology to analyze the pressure behavior of multi-branch wells produced from natural gas hydrates. For constant bottom-hole pressure production, the transient flow solution is obtained by Laplace transforms. The interference among various branches is investigated using the superposition principle. A simplified form of the proposed model is validated using published analytical solutions. The complete flow profile can be divided into nine distinct regimes: wellbore storage and skin, vertical radial flow, linear flow, pseudo-radial flow, composite flow, dissociated flow, transitional flow, improvement flow and stress-sensitive flow. A well's multi-branch structure governs the vertical radial and the linear flow regimes. In our model, a dynamic interface divides the natural gas hydrates deposit into dissociated and non-dissociated regions. Natural gas hydrates formation properties govern the composite-effect, dissociated, transitional, and improvement flow regimes. A dissociation coefficient governs the difference in flow resistance between dissociated and non-dissociated natural gas hydrates regions. The dissociated-zone radius affects the timing of these flow regimes. Conversion of natural gas hydrates to natural gas becomes instantaneous as the dissociation coefficient increases. The pressure derivative exhibits the same features as a homogeneous formation. The natural gas hydrates parameter values in the Shenhu area of the South China Sea cause the prominent dissociated flow regime to conceal the later transitional and improvement flow regimes. Due to the maximum practical well-test duration limitation, the first five flow regimes (through composite flow) are more likely to appear in practice than later flow regimes.