New Advances in Low-Energy Processes for Geo-Energy Development: 2nd Edition

Geo-energy systems, including oil, gas, and geothermal ones, are accelerating toward a synergistic development stage characterized by high efficiency, low energy consumption, and low carbon emissions [1]. Guided by the concept of low-energy processes, the industry is re-engineering the entire workflow chain, including formation stimulation [2,3], injection–production control [4], wellbore engineering, surface safety, and digital intelligence [5,6,7,8,9]. The goal is to achieve higher recovery factors, more robust operational safety, and improved environmental performance with less energy input and material consumption [10,11,12]. Achieving this goal relies on multi-scale coupled understanding (pore–fracture–wellbore–pipeline–surface) [13,14,15], process intensification and material innovation [16,17], and data-driven adaptive optimization control.

The development of unconventional reservoirs and hard-to-recover resources—such as those with high water cut, deep burial, and strong heterogeneity—remains the primary focus for enhancing production capacity and reducing unit energy consumption [18,19,20,21]. New technologies and materials are addressing key challenges, including proppant transport and placement during fracturing [2,22,23], flowback optimization in acidizing operations, structural reinforcement for well completion and stability, and high-performance materials for deep conformance improvement and water plugging [24,25,26]. These advancements significantly reduce treatment pressures and the frequency of workover operations [27], as well as enable more precise flow-path control to minimize ineffective circulation and repeated energy expenditure [28,29]. Simultaneously, artificial intelligence and big data are transforming static design into a closed-loop system of dynamic perception–prediction–control. This reduces trial-and-error costs and energy waste. For surface operations, safety assessments for CO₂ transport and storage, along with accurate characterization of the thermal conductivity of fluid-bearing rocks [30], provide low-energy design bases and safety boundaries for geothermal utilization, CCUS, and multi-energy complementarity [31].

This Special Issue, “New Advances in Low-Energy Processes for Geo-Energy Development: 2nd Edition”, focuses on fundamental and methodological innovations. It compiles ten papers that systematically present recent progress in low-energy processes across three key areas: unconventional reservoir stimulation and wellbore engineering, data- and intelligence-driven production optimization, and subsurface storage/transport and thermal property characterization. The following paragraphs provide an overview and commentary on the core findings of these papers, organized according to the three themes above.

The first category focuses on low-energy stimulation and wellbore process intensification. Lu et al. [32] addressed the fundamental unit of complex fracture networks—asymmetric branch fractures—and constructed an experiment using a rough-walled dual-branch slot. It is shown that structural asymmetry significantly induces non-uniform proppant transport and irregular proppant bed morphology, and that reducing branch height and width increases local flow velocity, inhibits settling, and delivers more proppant to the far end. However, injection pressure also increases; a height ratio of 0.25 easily causes screen-out and ineffective placement; and the upper branch also struggles to form a proppant bed and is prone to closure. This research provides geometric optimization and operational parameter boundaries for achieving more effective propping with lower pumping energy in unconventional fracturing. Wang et al. [33] targeted the high energy consumption in acidizing deep, high-pressure, and sensitive offshore Dongying Formation reservoirs. An integrated acidizing–flowback tubing string is proposed, which enables reverse acid injection through the casing–tubing annulus and rapid pump recovery of spent acid/byproducts, reducing single-trip time and secondary contamination. Multi-physics analysis shows maximum deformations of 1.4, 1.9, 0.18, 2.7, 1.8, and 2.5 m under buoyancy, the piston effect, viscosity, helical buckling, temperature difference, and expansion, respectively, and total deformation is less than 3 m. Under different well depth conditions, axial force is 400–600 kN, stress is 260–350 MPa, and the safety factor is greater than 3.0. This process ensures wellbore safety while reducing operational energy and cost through process integration.

Zhao et al. [34] proposed a one-trip shape-memory thermal-sensitive screen pipe, which uses a specific temperature to spontaneously expand and reinforce the wellbore wall. Finite element calculations and theoretical evaluation show that the radial compaction force after pipe–wall contact optimizes near-wellbore stress distribution and reduces sand production risk. It also effectively enhances bottomhole flowing pressure and depressurization capacity in shale oil and gas completions. This passive deployment–active reinforcement single-trip completion method is expected to reduce multiple workovers and energy-intensive sand control operations. Lv et al. [35] addressed the issue of uneven stage and cluster placement caused by the chaotic–strongly heterogeneous internal structure of clastic glutenite reservoirs. A gravel distribution characterization model is established based on imaging logs, identification analysis software is developed, and conventional-imaging log correlation prediction and Kriging point–line–surface interpolation methods are constructed. Model accuracy exceeds 80% under validation from drilled cores, borehole cameras, and imaging logs. It helps avoid high-gravel zones, and wellhead pressure is lower than in offset wells, with reduced fluid volume per well and better production. The precise formation perception–cluster design reduces ineffective fracturing energy.

Xu et al. [36] used amphiphilic carbon dots as a multifunctional modifier. Carbon dot-enhanced preformed particle gels (CD-PPGs) were prepared via in situ polymerization with dual crosslinking from hydrogen bonding and hydrophobic association. Temperature resistance (up to 110 °C), salt tolerance (up to 15 × 10⁴ mg/L), and mechanical properties are significantly improved, and in-depth self-aggregation and targeted high-strength plugging characteristics are demonstrated. Under permeability conditions of 539.0–2988.6 mD, a 0.5 PV (pore volume) injection achieves a plugging rate greater than 95%, and in heterogeneous simulations, total oil recovery reaches 52.6%, which is 20.5% higher than that from waterflooding alone. This material aids low-energy conformance improvement with lower dosage and longer-lasting deep regulation. He and Wang [37] used pyrolyzed biochar from agricultural and forestry waste as a functional enhancer, and constructed an inorganic composite gel system for high-temperature, high-salinity fractured-vuggy carbonate reservoirs. The 0.5 wt% biochar significantly optimizes the matrix pore structure. Through micro-aggregate filling and interfacial chemical bonding, the cured body achieves a compressive strength greater than 2 MPa, and no significant strength decay is observed after aging at 130 °C for 30 days. An adsorption slow-release mechanism stabilizes hydration kinetics at high temperature, extending the setting time to 15 h, which solves the flash setting challenge in deep well pumping. The system exhibits excellent shear-thinning and fracture plugging capabilities, and also shows a tough yield-reconstruction plugging characteristic, balancing durability and low-energy injection.

The second category emphasizes data- and intelligence-driven low-energy production optimization. Xin et al. [38] proposed a method combining data augmentation (noise perturbation and sliding window) with Bayesian hyperparameter optimization for downhole pressure prediction. A CNN-BiGRU-multi-head attention deep network is constructed to address challenges of limited samples and strong operational disturbances. The proposed model outperforms mainstream methods on metrics such as MAE and R², achieving an R² of 0.9831, and demonstrates strong generalization and engineering practicality. This provides a predictive core for intelligent production management and energy optimization under conditions of reduced trial-and-error and shutdowns. Xin et al. [39] further proposed a TCN-LSTM-AVOA intelligent prediction workflow, which integrates temporal convolutional networks and long short-term memory networks. The nonlinear swarm intelligence algorithm African Vulture Optimization is introduced for automatic key hyperparameter tuning, which effectively captures the time-lag and nonlinear characteristics of high-dimensional time series. On 2D three-phase heterogeneous reservoir data, performance metrics of RMSE 7.0806, MAE 3.4780, R² 0.9975, and MAPE 1.81% are achieved, and prediction accuracy and robustness are significantly improved. This supports adaptive optimization of injection–production strategies for energy consumption minimization.

The third category focuses on subsurface storage/transport safety and geothermal/thermal property characterization. Li et al. [40] developed crack arrest assessment software for supercritical CO₂ pipelines containing impurities based on the Battelle two-curve model (BTCM). A three-layer architecture using Python (v.3.12.4) and PyQt6 (v.6.10.0) integrates resistance curve and decompression wave models, and a property database covering pure and multicomponent systems (including PR, HEOS, and GERG-2008 equations) is embedded. The software generates pressure drop, decompression, and resistance curves, and quickly identifies pressure plateaus and determines whether crack arrest has occurred. Compared with existing experiments, full-scale burst test data, and the HLP model, the error is within 10%. This tool provides efficient, low-cost safety assessment capability for CCUS transportation, indirectly reducing comprehensive energy consumption from redundant safety margins. Chen et al. [41] used a segmented differential effective medium (SDEM) framework coupled with high-precision experiments. An integrated measurement-modeling study is conducted on the effects of total porosity, grain size, and saturation on the thermal conductivity (λ) of porous rocks, using a self-developed one-dimensional divided-bar apparatus and ultrasonic pulse transmission. Experiments are performed on Fontainebleau sandstone and quartz sand packs of different grain sizes under dry and saturated conditions up to 2000 psi. The SDEM provides significantly better predictions of λ for saturated sand packs: fine-grained materials exhibit the highest λ and compressional wave velocity and are more stress-sensitive. Shear wave velocity is greater in coarse-grained materials. The model fits both dry and saturated data with a single parameter, and also enables a thermal fluid substitution analogous to Gassmann’s equation. This enhances in situ thermal conductivity prediction capability based on acoustic logs and provides a basis for the low-energy design of geothermal and reservoir thermal management systems.

This Special Issue, “New Advances in Low-Energy Processes for Geo-Energy Development: 2nd Edition”, focuses on key advances in the following areas: low-energy stimulation and wellbore process intensification for unconventional and deep complex reservoirs (including proppant transport geometry optimization, integrated acidizing–flowback, one-trip thermal-sensitive screen completion, intelligent characterization and cluster optimization of gravel distribution, deep high-performance conformance control, and inorganic composite water-plugging materials); AI-driven prediction and control for the entire lifecycle of oil and gas production; and safety and thermophysical property modeling for CCUS and geothermal utilization. The related work provides reusable methods and tools for reducing pumping energy and circulating fluid consumption, lowering tripping and shutdown frequency, improving fluid and energy use efficiency, and strengthening safety boundaries. Looking ahead, more innovations are expected in cross-scale mechanism and data integration, closed-loop field trials and digital twins, as well as low-energy processes for CCUS/hydrogen storage and transportation and comprehensive geothermal utilization, to continuously advance geo-energy development toward higher efficiency, safety, and sustainability.