Petroleum Science最新文献

Porous ionic liquids have demonstrated excellent performance in the field of separation, attributed to their high specific surface area and efficient mass transfer. Herein, task-specific protic porous ionic liquids (PPILs) were prepared by employing a novel one-step coupling neutralization reaction strategy for extractive desulfurization. The single-extraction efficiency of PPILs reached 75.0% for dibenzothiophene. Moreover, adding aromatic hydrocarbon interferents resulted in a slight decrease in the extraction efficiency of PPILs (from 45.2% to 37.3%, 37.9%, and 33.5%), indicating the excellent extraction selectivity of PPILs. The experimental measurements and density functional theory calculations reveal that the surface channels of porous structures can selectively capture dibenzothiophene by the stronger electrophilicity (E_{int (HS surface channel/DBT)} = −39.8 kcal mol⁻¹), and the multiple extraction sites of ion pairs can effectively enrich and transport dibenzothiophene from the oil phase into PPILs through π···π, C–H···π and hydrogen bonds interactions. Furthermore, this straightforward synthetic strategy can be employed in preparing porous liquids, offering new possibilities for synthesizing PPILs with tailored functionalities.

Conventional machine learning (CML) methods have been successfully applied for gas reservoir prediction. Their prediction accuracy largely depends on the quality of the sample data; therefore, feature optimization of the input samples is particularly important. Commonly used feature optimization methods increase the interpretability of gas reservoirs; however, their steps are cumbersome, and the selected features cannot sufficiently guide CML models to mine the intrinsic features of sample data efficiently. In contrast to CML methods, deep learning (DL) methods can directly extract the important features of targets from raw data. Therefore, this study proposes a feature optimization and gas-bearing prediction method based on a hybrid fusion model that combines a convolutional neural network (CNN) and an adaptive particle swarm optimization-least squares support vector machine (APSO-LSSVM). This model adopts an end-to-end algorithm structure to directly extract features from sensitive multicomponent seismic attributes, considerably simplifying the feature optimization. A CNN was used for feature optimization to highlight sensitive gas reservoir information. APSO-LSSVM was used to fully learn the relationship between the features extracted by the CNN to obtain the prediction results. The constructed hybrid fusion model improves gas-bearing prediction accuracy through two processes of feature optimization and intelligent prediction, giving full play to the advantages of DL and CML methods. The prediction results obtained are better than those of a single CNN model or APSO-LSSVM model. In the feature optimization process of multicomponent seismic attribute data, CNN has demonstrated better gas reservoir feature extraction capabilities than commonly used attribute optimization methods. In the prediction process, the APSO-LSSVM model can learn the gas reservoir characteristics better than the LSSVM model and has a higher prediction accuracy. The constructed CNN-APSO-LSSVM model had lower errors and a better fit on the test dataset than the other individual models. This method proves the effectiveness of DL technology for the feature extraction of gas reservoirs and provides a feasible way to combine DL and CML technologies to predict gas reservoirs.

The continental shale reservoirs of Jurassic Lianggaoshan Formation in Sichuan Basin contain thin lamina, which is characterized by strong plasticity and developed longitudinal shell limestone interlayer. To improve the production efficiency of reservoirs by multi-cluster fracturing, it is necessary to consider the unbalanced propagation of hydraulic fractures and the penetration effect of fractures. This paper constructed a numerical model of multi-fracture propagation and penetration based on the finite element coupling cohesive zone method; considering the construction cluster spacing, pump rate, lamina strength and other parameters studied the influencing factors of multi-cluster fracture interaction propagation; combined with AE energy data and fracture mode reconstruction method, quantitatively characterized the comprehensive impact of the strength of thin interlayer rock interfaces on the initiation and propagation of fractures that penetrate layers, and accurately predicted the propagation pattern of hydraulic fractures through laminated shale oil reservoirs. Simulation results revealed that in the process of multi-cluster fracturing, the proportion of shear damage is low, and mainly occurs in bedding fractures activated by outer fractures. Reducing the cluster spacing enhances the fracture system's penetration ability, though it lowers the activation efficiency of lamina. The high plasticity of the limestone interlayer may impact the vertical propagation distance of the main fracture. Improving the interface strength is beneficial to the reconstruction of the fracture height, but the interface communication effect is limited. Reasonable selection of layers with moderate lamina strength for fracturing stimulation, increasing the pump rate during fracturing and setting the cluster spacing reasonably are beneficial to improve the effect of reservoir stimulation.

CO₂ flooding is a vital development method for enhanced oil recovery in low-permeability reservoirs. However, micro-fractures are developed in low-permeability reservoirs, which are essential oil flow channels but can also cause severe CO₂ gas channeling problems. Therefore, anti-gas channeling is a necessary measure to improve the effect of CO₂ flooding. The kind of anti-gas channeling refers to the plugging of fractures in the deep formation to prevent CO₂ gas channeling, which is different from the wellbore leakage. Polymer microspheres have the characteristics of controllable deep plugging, which can achieve the profile control of low-permeability fractured reservoirs. In acidic environments with supercritical CO₂, traditional polymer microspheres have poor expandability and plugging properties. Based on previous work, a systematic evaluation of the expansion performance, dispersion rheological properties, stability, deep migration, anti-CO₂ channeling and enhanced oil recovery ability of a novel acid-resistant polymer microsphere (DCNPM-A) was carried out under CQ oilfield conditions (salinity of 85,000 mg/L, 80 °C, pH = 3). The results show that the DCNPM-A microsphere had a better expansion performance than the traditional microsphere, with a swelling rate of 13.5. The microsphere dispersion with a concentration of 0.1%–0.5% had the advantages of low viscosity, high dispersion and good injectability in the low permeability fractured core. In the acidic environment of supercritical CO₂, DCNPM-A microspheres showed excellent stability and could maintain strength for over 60 d with less loss. In core experiments, DCNPM-A microspheres exhibited delayed swelling characteristics and could effectively plug deep formations. With a plugging rate of 95%, the subsequent enhanced oil recovery of CO₂ flooding could reach 21.03%. The experimental results can provide a theoretical basis for anti-CO₂ channeling and enhanced oil recovery in low-permeability fractured reservoirs.

This work studied the thickening progression mechanism of the silica fume – oil well cement composite system at high temperatures (110–180 °C) in order to provide a theoretical guidance for the rational application of silica fume in the cementing engineering. Results showed that silica fume seldom affected the thickening progression of oil well cement slurry at 110–120 °C, but when temperature reached above 130 °C, it would aggravate the bulging degree of thickening curves and significantly extend the thickening time, meanwhile causing the abnormal “temperature-based thickening time reversal” and “dosage-based thickening time reversal” phenomena in the range of 130–160 °C and 170–180 °C respectively. At 130–160 °C, the thickening time of oil well cement slurry was mainly associated with the generation rate of calcium hydroxide (CH) crystal. The introduced silica fume would be attracted to the cement minerals’ surface that were hydrating to produce CH and agglomerate together to form an “adsorptive barrier” to hinder further hydration of the inner cement minerals. This “adsorptive barrier” effect strengthened with the rising temperature which extended the thickening time and caused the occurrence of the “temperature-based thickening time reversal” phenomenon. At 170–180 °C, the pozzolanic activity of silica fume significantly enhanced and considerable amount of C−S−H was generated, thus the “temperature-based thickening time reversal” vanished and the “dosage-based thickening time reversal” was presented.

As drilling wells continue to move into deep ultra-deep layers, the requirements for temperature resistance of drilling fluid treatments are getting higher and higher. Among them, blocking agent, as one of the key treatment agents, has also become a hot spot of research. In this study, a high temperature resistant strong adsorption rigid blocking agent (QW-1) was prepared using KH570 modified silica, acrylamide (AM) and allyltrimethylammonium chloride (TMAAC). QW-1 has good thermal stability, average particle size of 1.46 μm, water contact angle of 10.5°, has a strong hydrophilicity, can be well dispersed in water. The experimental results showed that when 2 wt% QW-1 was added to recipe A (4 wt% bentonite slurry+0.5 wt% DSP-1 (filtration loss depressant)), the API filtration loss decreased from 7.8 to 6.4 mL. After aging at 240 °C, the API loss of filtration was reduced from 21 to 14 mL, which has certain performance of high temperature loss of filtration. At the same time, it is effective in sealing 80–100 mesh and 100–120 mesh sand beds as well as 3 and 5 μm ceramic sand discs. Under the same conditions, the blocking performance was superior to silica (5 μm) and calcium carbonate (2.6 μm). In addition, the mechanism of action of QW-1 was further investigated. The results show that QW-1 with amide and quaternary ammonium groups on the molecular chain can be adsorbed onto the surface of clay particles through hydrogen bonding and electrostatic interaction to form a dense blocking layer, thus preventing further intrusion of drilling fluid into the formation.

During the operational process of natural gas gathering and transmission pipelines, the formation of hydrates is highly probable, leading to uncontrolled movement and aggregation of hydrates. The continuous migration and accumulation of hydrates further contribute to the obstruction of natural gas pipelines, resulting in production reduction, shutdowns, and pressure build-ups. Consequently, a cascade of risks is prone to occur. To address this issue, this study focuses on the operational process of natural gas gathering and transmission pipelines, where a comprehensive framework is established. This framework includes theoretical models for pipeline temperature distribution, pipeline pressure distribution, multiphase flow within the pipeline, hydrate blockage, and numerical solution methods. By analyzing the influence of inlet temperature, inlet pressure, and terminal pressure on hydrate formation within the pipeline, the sensitivity patterns of hydrate blockage risks are derived. The research indicates that reducing inlet pressure and terminal pressure could lead to a decreased maximum hydrate formation rate, potentially mitigating pipeline blockage during natural gas transportation. Furthermore, an increase in inlet temperature and terminal pressure, and a decrease in inlet pressure, results in a displacement of the most probable location for hydrate blockage towards the terminal station. However, it is crucial to note that operating under low-pressure conditions significantly elevates energy consumption within the gathering system, contradicting the operational goal of energy efficiency and reduction of energy consumption. Consequently, for high-pressure gathering pipelines, measures such as raising the inlet temperature or employing inhibitors, electrical heat tracing, and thermal insulation should be adopted to prevent hydrate formation during natural gas transportation. Moreover, considering abnormal conditions such as gas well production and pipeline network shutdowns, which could potentially trigger hydrate formation, the installation of methanol injection connectors remains necessary to ensure production safety.

Gas flexible pipes are critical multi-layered equipment for offshore oil and gas development. Under high pressure conditions, small molecular components of natural gas dissolve into the polymer inner liner of the flexible pipes and further diffuse into the annular space, incurring annular pressure build-up and/or production of acidic environment, which poses serious challenges to the structure and integrity of the flexible pipes. Gas permeation in pipes is a complex phenomenon governed by various factors such as internal pressure and temperature, annular structure, external temperature. In a long-distance gas flexible pipe, moreover, gas permeation exhibits non-uniform features, and the gas permeated into the annular space flows along the metal gap. To assess the complex gas transport behavior in long-distance gas flexible pipes, a mathematical model is established in this paper considering the multiphase flow phenomena inside the flexible pipes, the diffusion of gas in the inner liner, and the gas seepage in the annular space under varying permeable properties of the annulus. In addition, the effect of a variable temperature is accounted. A numerical calculation method is accordingly constructed to solve the coupling mathematical equations. The annular permeability was shown to significantly influence the distribution of annular pressure. As permeability increases, the annular pressure tends to become more uniform, and the annular pressure at the wellhead rises more rapidly. After annular pressure relief followed by shut-in, the pressure increase follows a convex function. By simulating the pressure recovery pattern after pressure relief and comparing it with test results, we deduce that the annular permeability lies between 123 and 512 mD. The results help shed light upon assessing the annular pressure in long distance gas flexible pipes and thus ensure the security of gas transport in the emerging development of offshore resources.

Time-frequency analysis is a successfully used tool for analyzing the local features of seismic data. However, it suffers from several inevitable limitations, such as the restricted time-frequency resolution, the difficulty in selecting parameters, and the low computational efficiency. Inspired by deep learning, we suggest a deep learning-based workflow for seismic time-frequency analysis. The sparse S transform network (SSTNet) is first built to map the relationship between synthetic traces and sparse S transform spectra, which can be easily pre-trained by using synthetic traces and training labels. Next, we introduce knowledge distillation (KD) based transfer learning to re-train SSTNet by using a field data set without training labels, which is named the sparse S transform network with knowledge distillation (KD-SSTNet). In this way, we can effectively calculate the sparse time-frequency spectra of field data and avoid the use of field training labels. To test the availability of the suggested KD-SSTNet, we apply it to field data to estimate seismic attenuation for reservoir characterization and make detailed comparisons with the traditional time-frequency analysis methods.

The similarities and differences in inherent mechanism and characteristic frequency between the one-dimensional (1D) poroelastic model and the layered White model were investigated. This investigation was conducted under the assumption that the rock was homogenous and isotropic at the mesoscopic scale. For the inherent mechanism, both models resulted from quasi-static flow in a slow P-wave diffusion mode, and the differences between them originated from saturated fluids and boundary conditions. On the other hand, for the characteristic frequencies of the models, the characteristic frequency of the 1D poroelastic model was first modified because the elastic constant and formula for calculating it were misused and then compared to that of the layered White model. Both of them moved towards higher frequencies with increasing permeability and decreasing viscosity and diffusion length. The differences between them were due to the diffusion length. The diffusion length for the 1D poroelastic model was determined by the sample length, whereas that for the layered White model was determined by the length of the representative elementary volume (REV). Subsequently, a numerical example was presented to demonstrate the similarities and differences between the models. Finally, published experimental data were interpreted using the 1D poroelastic model combined with the Cole-Cole model. The prediction of the combined model was in good agreement with the experimental data, thereby validating the effectiveness of the 1D poroelastic model. Furthermore, the modified characteristic frequency in our study was much closer to the experimental data than the previous prediction, validating the effectiveness of our modification of the characteristic frequency of the 1D poroelastic model. The investigation provided insight into the internal relationship between wave-induced fluid flow (WIFF) models at macroscopic and mesoscopic scales and can aid in a better understanding of the elastic modulus dispersion and attenuation caused by the WIFF at different scales.