In recent years, companies have employed numerous methods to lower expenses and enhance system efficiency in the oilfield. Energy consumption has constituted a significant portion of these expenses. This paper introduces a normalized consumption factor to effectively evaluate energy consumption in the oilfield. Statistical analysis has been conducted on nearly 45,000 wells from six fields in China. Critical factors such as lifting method, daily production, pump depth, gas–oil ratio (GOR), and well deviation angle were evaluated individually. Results revealed that higher production could lead to lower normalized consumption for beam pumps, progressive cavity pumps, and electric submersible pump systems, thus enhancing system efficiency. Additionally, a higher GOR might result in lower normalized consumption for the beam pump system, while the deviation angle of the well showed negligible impact on the normalized consumption factor. This manuscript offers a method to assess the impacts of artificial lift methods on production and discusses suggestions for reducing consumption associated with each lifting method in the oilfield.

The electrical submersible pump (ESP) well system is widely used in the oil industry due to its advantages of high displacement and lift capability. However, it is associated with significant energy consumption. In order to conserve electrical energy and enhance the efficiency of petroleum companies, a deep learning-based energy consumption calculation method is proposed and utilized to optimize the most energy-efficient operating regime. The energy consumption of the ESP well system is precisely determined through the application of the Pearson correlation coefficient analysis method, which is utilized to examine the relationship between production parameters and energy usage. This process aids in identifying the input parameters of the model. Following this, an energy consumption prediction model is developed using the dual-stage attention-based recurrent neural network (DA-RNN) algorithm. To evaluate the accuracy of the DA-RNN model, a comparison of its errors is carried out in comparison to three other deep learning algorithms: Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), and Transform. Lastly, an orthogonal experiment is executed using the chosen model to pinpoint the most energy-efficient operating regime. Analysis of 325 ESP wells in the Bohai PL oil field indicated that ten parameters, including choke diameter, casing pressure, pump inlet pressure, pump outlet pressure, motor temperature, frequency, oil production, gas production, water production, and GOR significantly impact the energy consumption of the ESP well system. Consequently, these parameters were selected as input variables for the deep learning model. Due to the attention mechanisms employed in the encoding and decoding stages, the DA-RNN algorithm achieved the best performance during model evaluation and was chosen for constructing the energy consumption prediction model. Furthermore, the DA-RNN algorithm demonstrates better model generalization capabilities compared to the other three algorithms. Based on the energy consumption prediction model, the operating regime of the ESP system was optimized to save up to 12% of the maximum energy. The energy consumption of the ESP well system is affected by numerous parameters, and it is difficult to comprehensively evaluate and predict quantitatively. Thus, this work proposes a data-driven model based on the DA-RNN algorithm, which has a dual-stage attention mechanism to rapidly and accurately predict the energy consumption of the ESP well system. Optimization of production parameters using this model can effectively reduce energy consumption.

The synergistic development of low-permeability reservoirs such as deep coalbed methane (CBM) and tight gas has emerged as a key technology to reduce development costs, enhance single-well productivity, and improve gas recovery. However, due to fundamental differences between coal seams and tight sandstones in their pore structure, permeability, water saturation, and pressure sensitivity, significant variations exist in their flow capacities and fluid production behaviors. To address the challenges of production allocation and main reservoir identification in the co-development of CBM and tight gas within deep gas-bearing basins, this study employs the transient multiphase flow simulation software OLGA to construct a representative dual-gas co-production well model. The regulatory mechanisms of the gas–liquid distribution, deliquification efficiency, and interlayer interference under two typical vertical stacking relationships—“coal over sand” and “sand over coal”—are systematically analyzed with respect to different tubing setting depths. A high-precision dynamic production allocation method is proposed, which couples the wellbore structure with real-time monitoring parameters. The results demonstrate that positioning the tubing near the bottom of both reservoirs significantly enhances the deliquification efficiency and bottomhole pressure differential, reduces the liquid holdup in the wellbore, and improves the synergistic productivity of the dual-reservoirs, achieving optimal drainage and production performance. Building upon this, a physically constrained model integrating real-time monitoring data—such as the gas and liquid production from tubing and casing, wellhead pressures, and other parameters—is established. Specifically, the model is built upon fundamental physical constraints, including mass conservation and the pressure equilibrium, to logically model the flow paths and phase distribution behaviors of the gas–liquid two-phase flow. This enables the accurate derivation of the respective contributions of each reservoir interval and dynamic production allocation without the need for downhole logging. Validation results show that the proposed method reliably reconstructs reservoir contribution rates under various operational conditions and wellbore configurations. Through a comparison of calculated and simulated results, the maximum relative error occurs during abrupt changes in the production capacity, approximately 6.37%, while for most time periods, the error remains within 1%, with an average error of 0.49% throughout the process. These results substantially improve the timeliness and accuracy of the reservoir identification. This study offers a novel approach for the co-optimization of complex multi-reservoir gas fields, enriching the theoretical framework of dual-gas co-production and providing technically adaptive solutions and engineering guidance for multilayer unconventional gas exploitation.

Summary: Dual-gas coproduction, as a synergistic method for developing deep coalbed methane (CBM) and conventional natural gas reservoirs, has demonstrated significant potential in enhancing both single-well productivity and recovery efficiency. However, the multiphase flow behavior within the wellbore is complex, particularly at the interface between reservoirs, where gas-liquid countercurrent flow frequently occurs. This phenomenon severely impairs wellbore flow performance and gas production effectiveness. Existing research on multiphase flow mechanisms in dual-gas coproduction wells remains insufficient, and there is currently a lack of flow models capable of accurately capturing gas-liquid countercurrent behavior, posing a key challenge to further advancement of coproduction technologies. To address this issue, we investigated the wellbore structure and production characteristics of dual-gas coproduction wells and developed a full-scale laboratory wellbore experimental apparatus to simulate gas-liquid countercurrent flow. Using high-speed imaging and pressure sensors, we captured gas-liquid distribution, flow regimes, and pressure variations during countercurrent conditions. Three representative flow regimes were systematically identified—cocurrent flow, countercurrent flow induced by liquid loading, and countercurrent flow triggered by large-amplitude wave rupture—clarifying the transitional mechanisms and instability features of multiphase flow in dual-gas wells. Based on these observations, a novel transition boundary model was established, integrating liquid loading and wave rupture theory, which reduces prediction errors for critical gas velocity and pressure to within ±5%. Furthermore, a countercurrent-adapted multiphase flow model was developed, achieving a mean relative error of only 0.02 in pressure drop predictions. Compared with mainstream multiphase flow models (Beggs-Brill, Ansari, and Duns-Ros), the proposed model reduces residuals and standard deviations by more than 30%, thereby offering quantitatively validated improvements in predictive performance. These advancements provide explicit novelty in bridging experimental characterization with theoretical modeling, delivering strong theoretical support and practical guidance for optimizing wellbore structures, deliquification strategies, and production stability in dual-gas coproduction wells.

Horizontal wells combined with multi-stage fracturing are key techniques for extracting tight oil formation. However, due to the ultra-low permeability and porosity of reservoirs, energy depletion occurs rapidly, necessitating external supplements to sustain production. During the hydraulic fracturing process, large volumes of fracturing fluid are injected into reservoirs, increasing its pressure to a certain extent. However, due to the oil-wet nature of the formation, the fracturing fluid cannot penetrate the rock, failing to enhance oil recovery during the shut-in period. Surfactant-based nanofluids have been introduced as fracturing fluid additives to reverse rock wettability, thereby boosting imbibition-driven recovery. Although the imbibition has been studied to inspire the tight oil recovery, few studies have demonstrated the imbibition in enhanced fossil hydrogen energy, which further promotes the imbibition recovery. In this paper, complex nanofluid dispersions (CND) have been proved to enhance the tight reservoir pressure. Through contact angle and imbibition experiments, it is shown that CND can transform oil-wet rock to water-wet, reduce the adhesion of oil, and improve the ultimate oil recovery through the imbibition effect. Then, core flow testing experiments were conducted to show CND can decrease the flow resistance and improve the swept area of the injected fluid. In the end, pressure transmission tests were conducted to show CND can enhance the formation energy and production after fracturing. Results demonstrate that CND enables the fracturing fluid to travel further away from the hydraulic fractures, thus decreasing the depletion of tight formation pressure and maintaining a higher oil production rate. Results help optimize the design of the hydraulic fracturing of tight offshore reservoirs.

With the continuous increase in well depth and the gradual depletion of formation energy, the pump-setting depths in rod-pumped wells have increased significantly, leading to higher suspension loads at the pumping unit. The application of glass fiber-reinforced plastic (FRP) sucker rods can effectively reduce suspension loads due to their low density and high tensile strength. However, the mechanical performance of FRP rods is highly sensitive to temperature, which poses challenges for their application in deep and high-temperature wells. In FRP–steel hybrid sucker-rod string design, the influence of temperature—particularly on FRP rods—must therefore be carefully considered to prevent failures such as rod parting or coupling separation. This study systematically investigates the effects of temperature on the mechanical properties of FRP sucker rods, including elastic modulus, flexural shear strength, and tensile strength. Based on the operating characteristics of sucker-rod pumping systems and established design criteria, a temperature-aware design methodology for FRP–steel hybrid rod strings is developed and implemented in dedicated design software. The proposed approach enables rational determination of the FRP–steel partition depth under thermal constraints while satisfying mechanical safety requirements. A field case study is conducted to validate the design results, demonstrating that the software provides reliable and practical guidance for hybrid rod-string design in deep wells.