Predicting shale gas production under different geological and fracturing conditions in the fractured shale gas reservoirs is the foundation of optimizing the fracturing parameters, which is crucial to effectively exploit shale gas. We present a multi-layer perceptron (MLP) network and a long short-term memory (LSTM) network to predict shale gas production, both of which can quickly and accurately forecast gas production. The prediction performances of the networks are comprehensively evaluated and compared. The results show that the MLP network can predict shale gas production by geological and fracturing reservoir parameters. The average relative error of the MLP neural network is 2.85%, and the maximum relative error is 12.9%, which can meet the demand of engineering shale gas productivity prediction. The LSTM network can predict shale gas production through historical production under the constraints of geological and fracturing reservoir parameters. The average relative error of the LSTM neural network is 0.68%, and the maximum relative error is 3.08%, which can reliably predict shale gas production. There is a slight deviation between the predicted results of the MLP model and the true values in the first 10 days. This is because the daily production decreases rapidly during the early production stage, and the production data change greatly. The largest relative errors of LSTM in this work on the 10th, 100th, and 1000th day are 0.95%, 0.73%, and 1.85%, respectively, which are far lower than the relative errors of the MLP predictions. The research results can provide a fast and effective mean for shale gas productivity prediction.

Shale oil resources, noted for their broad distribution and significant reserves, are increasingly recognized as vital supplements to traditional oil resources. In response to the high fracturing costs and swift decline in productivity associated with shale oil horizontal wells, this research introduces a novel approach utilizing CO₂ for enhanced shale oil recovery in radial boreholes. A compositional numerical simulation method is built accounted for component diffusion, adsorption, and non-Darcy flow, to explore the viability of this technique. The study examines how different factors—such as initial reservoir pressure, permeability, numbers of radial boreholes, and their branching patterns—influence oil production and CO₂ storage. Our principal conclusions indicate that with a constant CO₂ injection rate, lower initial reservoir pressures predominantly lead to immiscible oil displacement, hastening the occurrence of CO₂ gas channeling. Therefore, maintaining higher initial or injection pressures is critical for effective miscible displacement in CO₂-enhanced recovery using radial boreholes. Notably, the adsorption of CO₂ in shale oil results in the displacement of lighter hydrocarbons, an effect amplified by competitive adsorption. While CO₂ diffusion tends to prompt earlier gas channeling, its migration towards areas of lower concentration within the reservoir reduces the extent of channeling CO₂. Nonetheless, when reservoir permeability falls below 0.01 mD, the yield from CO₂-enhanced recovery using radial boreholes is markedly low. Hence, selecting high-permeability “sweet spot” regions within shale oil reservoirs for the deployment of this method is advisable. To boost oil production, utilizing longer and broader radial boreholes, increasing the number of boreholes, or setting the phase angle to 0° are effective strategies. Finally, by comparing the production of shale oil enhanced by CO₂ with that of a dual horizontal well fracturing system enhanced by CO₂, it was found that although the former’s oil production is only 50.6% of the latter, its cost is merely 11.1%, thereby proving its economic viability. These findings present a new perspective for the economically efficient extraction of shale oil, offering potential guidance for industrial practices.

In this paper, high-temperature and high-pressure corrosion test was used to simulate the corrosion process of multi-component thermal fluid (CO2/O2/H2O) on oil pipes (N80 steel), casings (J55, TP90H, P110 steel, and transportation pipelines (20# steel) in the process of CCUS technology application. The results show that during the entire corrosion process, the oil layer casing and surface casing are corroded most seriously in this environment, and the corrosion forms of these five steel materials in the CO2/O2 environment are uniform corrosion and local corrosion, and the corrosion products mainly consist of FeCO3, Fe2O3, Fe3O4 and FeOOH crystals. These oxides form a double-layer film structure on the surface of the steel. The inner layer is denser and consists mostly of FeCO3 crystals, which has better protection against further corrosion of the internal steel. The outer layer is loose and porous, and its components are mostly FeCO3 crystals. Fe2O3, FeOOH, Fe3O4 and FeCO3 crystals, have poor corrosion protection for internal steel. Due to the difference in corrosion products of the inner and outer film layers, relatively obvious local corrosion appeared on the surfaces of the 5 steel materials.

Optimizing fracturing parameters is crucial for enhancing production and reducing costs in oil and gas exploration and development. Effectively integrating geological and engineering parameters for the automated optimization of fracturing design continues to pose challenges. This study utilizes the cluster-based local outlier factor method for anomaly detection and removal from the dataset, significantly enhancing data quality. By integrating diverse models, including tree-based models and neural networks, an ensemble model for production prediction was developed. This approach successfully addresses the limitations of relying on a single model and achieves high-precision production forecasting. Furthermore, a Covariance Matrix Adaptation Evolution Strategy (CMA-ES)-based framework was established to comprehensively optimize the design parameters of fracturing projects. Optimization practices for two selected wells resulted in a 168.54% increase in production and identified the optimal design parameter configuration for all cases studied. The results of this study demonstrate the feasibility and effectiveness of the proposed ensemble prediction model and optimization framework in practical applications. Data-driven optimization strategies are expected to play a larger role in future oil and gas development, driving technological innovation and advancement in the field.

As oil and gas exploration and development gradually expand into complex areas such as deep and ultra-deep, offshore, and unconventional resources, directional drilling and horizontal wells have become the main methods for increasing reserves and production in petroleum engineering. To address the challenging task of quantitatively evaluating downhole weight on bit (DWOB) and friction drag during sliding drilling in these types of wells, this study proposes a novel method for back-calculation by combining the rate of penetration (ROP) equation with sliding drilling footage. Based on the drilling parameters of the composite drilling phase and the rate of penetration, the ROP equation is established, the downhole weight on bit is estimated based on the drilling parameters of the sliding drilling phase and the ROP. In light of the drilling parameters of the composite drilling phase and the rate of penetration, the ROP equation is established through equation fitting by the least squares method. The downhole weight on bit is estimated on the basis of the drilling parameters of the sliding drilling phase and the ROP. Then the axial friction drag of drilling tools is estimated by combining with the surface weight on bit without the need to equip with the measurement of pup joint. This method enables real-time analysis of sliding drilling efficiency and quantitative estimation of friction drag, with the capability to identify friction reversal trends at formation interfaces. Field results demonstrate that it can effectively capture both gradual and abrupt anomalies in friction behavior. Following mitigation measures, the calculated axial friction drag was reduced by 3.86–90.15%, highlighting the method’s sensitivity to complex downhole conditions and its practical value in improving drilling tool performance. It provides dynamic insight into sliding drilling efficiency and quantitative friction assessment, offering theoretical support for optimizing drilling efficiency, enhancing bottom-hole tool loading, and evaluating the effectiveness of friction-reduction technologies.

The transition to low-carbon energy systems is expanding the use of subsurface resources for heat extraction, energy storage, carbon management, and infrastructure reuse. This review examines hydraulic fracturing as a context-dependent engineering intervention across enhanced geothermal systems, geothermal reuse of depleted reservoirs and wells, unconventional gas, underground hydrogen storage, CO₂-based subsurface engineering, and natural hydrogen. We synthesize how stimulation can improve permeability, connectivity, heat exchange, injectivity, and deliverability, while evaluating constraints related to water use, induced seismicity, leakage, well and caprock integrity, life-cycle emissions, and public acceptance. The central trade-off is that higher stimulation efficiency does not necessarily produce greater sustainability. Short-term gains in flow or energy delivery can increase long-term risks to containment, thermal performance, seismic safety, and environmental accountability. Evidence is strongest for enhanced geothermal systems and commercial unconventional gas, whereas field support remains limited for porous-media hydrogen storage, CO₂-based fracturing, and natural hydrogen. Responsible deployment therefore requires site-specific boundaries, real-time monitoring, multi-physics and data-driven modeling, life-cycle assessment, and adaptive governance. Hydraulic fracturing can enable selected sustainable subsurface applications, but its value depends on balancing engineering performance against long-term environmental and integrity constraints.