Because of the problems, such as the lack of an electric power diagram atlas under different working conditions and the difficulty in intelligent diagnosis of variable torqued pumping unit wells, this paper proposes a diagnosis model of working conditions based on feature recognition. The mathematical relationship model between the polished rod load and motor output power is derived based on the analysis of geometric structure, motion law, and process of energy transformation and transfer of the variable torque pumping unit. It can calculate the electric power diagram based on a dynamometer card. On this basis, the electric power diagram atlas is created, and the feature analysis and eigenvalue extraction of the electric power diagrams under different working conditions are carried out to realize the direct diagnosis of the working conditions in the variable torque pumping unit wells. The application and analysis of examples show that the electric power diagram atlas created in this paper has good practicability, and the working condition diagnosis model accuracy is high. It can provide a theoretical basis and technical support for the intelligent diagnosis of oil production working conditions and improve the intellectual management level of the oilfield, which is conducive to reducing production management costs and improving the oilfield’s production efficiency and benefits.

Radial drilling technology, which involves drilling multiple micro-horizontal wellbores radially from a main wellbore, can effectively expand the contact area between the wellbore and the reservoir, as well as the swept volume of thermal fluid. It is a promising technology for enhancing the efficiency of heavy oil thermal recovery. However, a systematic numerical simulation study on the application of this technology in the cyclic steam stimulation (CSS) development of heavy oil reservoirs is currently lacking. This paper establishes a numerical thermal recovery model for heavy oil reservoirs based on an unstructured grid modeling method, which can accurately describe the complex geometry of multi-layer, multi-branch radial wells. The model is discretized using the finite volume method and solved with a fully implicit method. Then, based on the geological parameters of a typical heavy oil reservoir, a comparative study was conducted on the production dynamics and physical field evolution of horizontal wells, single-layer radial wells, and dual-layer radial wells during the CSS process. The results indicate that, compared to conventional well types, dual-layer multi-branch radial wells can simultaneously inject steam into the upper and lower parts of the reservoir. This forms a more balanced and extensive three-dimensional heated body, significantly improving the planar sweep efficiency of heat and the uniformity of reserve recovery, thereby substantially increasing crude oil production and recovery factor. Compared to the horizontal well scenario, using dual-layer radial wells for CSS can increase cumulative oil production by 44.8%.

During water flooding processes, the high viscosity of heavy oil and significant reservoir heterogeneity often lead to severe water channeling and low sweep efficiency. Addressing the limitations of traditional hydrophobically associating polymer-based profile-control agents—such as significant adsorption loss, mechanical degradation during reservoir migration, resulting in a limited effective radius and short functional duration—this study developed a polymeric graded profile-control agent suitable for highly heterogeneous conditions. The physicochemical properties of the system were comprehensively evaluated through systematic testing of its apparent viscosity, salt tolerance, and anti-aging performance. The microscopic oil displacement mechanisms in porous media were elucidated by combining CT scanning and microfluidic visual displacement experiments. Experimental results indicate that the agent exhibits significant hydrophobic association behavior, with a critical association concentration of 1370 mg·L⁻¹, and demonstrates a “low viscosity at low temperature, high viscosity at high temperature” rheological characteristic. At a concentration of 3000 mg·L⁻¹, the apparent viscosity of the solution is 348 mPa·s at 30 °C, rising significantly to 1221 mPa·s at 70 °C. It possesses a salinity tolerance of up to 50,000 mg·L⁻¹, and a viscosity retention rate of 95.4% after 90 days of high-temperature aging, indicating good injectivity, reservoir compatibility, and thermal stability. Furthermore, within a concentration range of 500–3000 mg·L⁻¹, the agent can effectively emulsify Gudao heavy oil, forming O/W emulsion droplets with sizes ranging from 40 to 80 μm, enabling effective plugging of pore throats of corresponding sizes. CT scanning and microfluidic displacement experiments further reveal that the agent possesses a graded control function: in the near-wellbore high-concentration zone, it primarily relies on its aqueous phase viscosity-increasing capability to control the mobility ratio; upon entering the deep reservoir low-concentration zone, it utilizes “emulsion plugging” to achieve fluid diversion, thereby expanding the sweep volume and extending the effective treatment period. This research outcome provides a new technical pathway for the efficient development of highly heterogeneous heavy oil reservoirs.

The upper Shasi reservoir in the LN block is characterized by low abundance and greater depth, low porosity, low permeability, and low pressure. Due to high water injection pressure, the LN block has been developed in an elastic way. The natural productivity of oil wells in this block is low, but the productivity can be improved after fracturing. However, the field development effects show that the oil well has high initial production, but rapid decline and rapid pressure drop. At present, the recovery factor of this block is only 0.38%, and it is difficult to realize the economic and effective development of a difficult-to-develop block by conventional fracturing technology. Based on the geological characteristics of the LN block and the fracturing experience of adjacent wells, the fracturing process is optimized and the key fracturing parameters are determined in combination with the sand body distribution and logging curve of well LN-1. Due to the low-pressure coefficient and medium water sensitivity of well LN-1, a new high-efficiency stimulation fracturing fluid system was selected and the formula of the fracturing fluid system was formed. The cluster perforating process is optimized according to reservoir differences, and the perforating “sweet spot” is optimized. Based on the sand body spread point of well LN-1, the high diversion channel technology and the temporary plugging and turning fracturing technology are selected to form a new fracturing and stimulation technology suitable for this kind of oil reservoir. A fracturing test was performed in layers 17# (electrical sequencing number) and 22# of well LN-1. The initial oil production was 12.5 t/d, and the stimulation effect was significantly higher than the 8.3 t/d (general fracturing) of adjacent wells. At present, the well LN-1 has been producing steadily for more than six months, and the results of this work can provide technical guidance for the efficient development of low-abundance and greater-depth oil reservoirs that are difficult to develop.

With the continuous advancement of shale gas field development, well productivity following initial hydraulic fracturing often declines due to mechanisms such as proppant embedment and fracture conductivity degradation. However, such wells may still retain significant development potential, making re-fracturing crucial for restoring production and highlighting the critical importance of accurate candidate selection for re-fracturing. To improve the precision of candidate well selection for re-fracturing in shale gas wells, this study focuses on a shale gas block in the Southern Chuan Basin. Through comparative analysis of existing selection methods, 14 key parameters were finalized. The threshold values for some of these key parameters were recalibrated based on the specific geological, engineering, and production characteristics of the target block in the Southern Chuan Basin. Furthermore, the AHP-GRA (Analytic Hierarchy Process-Gray Relational Analysis) weighting method was integrated to achieve a balance between empirical knowledge and quantitative objectivity. Ultimately, a more targeted, comprehensive, and combined subjective–objective methodology for selecting re-fracturing candidate wells was developed. A computational tool developed in Python 3.9 was utilized to evaluate 13 candidate wells in the block, successfully identifying three high-priority wells for re-fracturing implementation. The reliability of this selection result was validated by analyzing production data before and after re-fracturing, confirming that the production performance of the selected wells showed relatively significant improvement post re-fracturing, with a notable increase in recovery factor. This model provides critical decision-making support for the low-cost and large-scale development of shale gas. It holds significant theoretical and practical value for promoting the secondary development of mature shale gas wells and contributes positively to the efficient utilization of unconventional natural gas resources and energy security.

History matching traditionally assumes a spatially uniform clay adsorption coefficient Kd, overlooking the fractal heterogeneity of clay mineral distributions in real reservoirs. This study generalizes the classical advection–dispersion framework by treating Kd as a non-uniform field whose logarithmic fluctuations follow fractional Brownian motion (fBm) statistics with Hurst exponent H, and by coupling the resulting heterogeneous retardation to a fractional advection–dispersion equation through the continuous-time random walk bridge β = 2/(2H + 1). A Karhunen–Loève (K-L) expansion compresses the field into a small set of spectral modes, and an adjoint-gradient inversion recovers the clay field from tracer data. The main operational limitation of the K-L approach is that sub-grid variability below the retained spectral cutoff is smoothed; within this boundary the inversion is efficient and numerically stable. Two independent validation paths are pursued. First, a cross-fitting experiment shows that when the true physics is sub-diffusive (β = 0.85), the classical advection–dispersion equation yields a negative coefficient of determination (R2 = −0.927), whereas the correctly specified fractional model fits the data, establishing fractional derivatives as physically necessary rather than mathematically optional. Second, the publicly available EGS Collab DNA-tracer dataset independently confirms statistically significant clay adsorption (retardation R = 1.250 ± 0.009, p < 0.0001). The recovered clay field attains pixel-scale accuracy R2η = 0.844 (Spearman ρ = 0.923) and R2 > 0.94 at reservoir-relevant scales of five cells or coarser. These results indicate that fractal-aware clay-field inversion provides a physically verifiable, rather than purely empirical, pathway for history matching, with direct implications for field-scale contaminant transport prediction and environmental subsurface management, and the evaluation of flue gas enhanced thermal recovery in thin-layer heavy oil reservoirs.