Gas flow behavior in porous media with micro- and nanoscale pores has always been attracted great attention. Gas transport mechanism in such pores is a complex problem, which includes continuous flow, slip flow and transition flow. In this study, the microtubes of quartz microcapillary and nanopores alumina membrane were used, and the gas flow measurements through the microtubes and nanopores with the diameters ranging from 6.42 μm to 12.5 nm were conducted. The experimental results show that the gas flow characteristics are in rough agreement with the Hagen-Poiseuille (H-P) equation in microscale. However, the flux of gas flow through the nanopores is larger than the H-P equation by more than an order of magnitude, and thus the H-P equation considerably underestimates gas flux. The Knudsen diffusion and slip flow coexist in the nanoscale pores and their contributions to the gas flux increase as the diameter decreases. The slip flow increases with the decrease in diameter, and the slip length decreases with the increase in driving pressure. Furthermore, the experimental gas flow resistance is less than the theoretical value in the nanopores and the flow resistance decreases along with the decrease in diameter, which explains the phenomenon of flux increase and the occurrence of a considerable slip length in nanoscale. These results can provide insights into a better understanding of gas flow in micro- and nanoscale pores and enable us to exactly predict and actively control gas slip.

Tight reservoirs have poor physical properties: low permeability and strong heterogeneity, which makes it difficult to predict productivity. Accurate prediction of oil well production plays a very important role in the exploration and development of oil and gas reservoirs, and improving the accuracy of production prediction has always been a key issue in reservoir characterization. With the development of artificial intelligence, high-performance algorithms make reliable production prediction possible from the perspective of data. Due to the high cost and large error of traditional seepage theory formulas in predicting oil well production, this paper establishes a horizontal well productivity prediction model based on a hybrid neural network method (CNN-LSTM), which solves the limitations of traditional methods and produces accurate predictions of horizontal wells’ daily oil production. In order to prove the effectiveness of the model, compared with the prediction results of BPNN, RBF, RNN and LSTM, it is concluded that the error results of the CNN-LSTM prediction model are 67%, 60%, 51.3% and 28% less than those of the four models, respectively, and the determination coefficient exceeds 0.95. The results show that the prediction model based on a hybrid neural network can accurately reflect the dynamic change law of production, which marks this study as a preliminary attempt of the application of this neural network method in petroleum engineering, and also provides a new method for the application of artificial intelligence in oil and gas field development.

Polymeric nanoparticle suspension is a newly developed oil-displacing agent for enhanced oil recovery (EOR) in low-permeability reservoirs. In this work, SiO₂/P(MBAAm-co-AM) polymeric nanoparticles were successfully synthesized by a simple distillation–precipitation polymerization method. Due to the introduction of polymer, the SiO₂/P(MBAAm-co-AM) nanoparticles show a favorable swelling performance in aqueous solution, and their particle sizes increase from 631 to 1258 nm as the swelling times increase from 24 to 120 h. The apparent viscosity of SiO₂/P(MBAAm-co-AM) suspension increases with an increase of mass concentration and swelling time, whereas it decreases as the salinity and temperature increase. The SiO₂/P(MBAAm-co-AM) suspension behaves like a non-Newtonian fluid at lower shear rates, yet like a Newtonian fluid at shear rates greater than 300 s⁻¹. The EOR tests of the SiO₂/P(MBAAm-co-AM) suspension in heterogeneous, low-permeability cores show that SiO₂/P(MBAAm-co-AM) nanoparticles can effectively improve the sweep efficiency and recover more residual oils. A high permeability ratio can result in a high incremental oil recovery in parallel cores. With an increase of the permeability ratio of parallel cores from 1.40 to 15.49, the ratios of incremental oil recoveries (low permeability/high permeability) change from 7.69/4.61 to 23.61/8.46. This work demonstrates that this SiO₂/P(MBAAm-co-AM) suspension is an excellent conformance control agent for EOR in heterogeneous, low-permeability reservoirs. The findings of this study can help to further the understanding of the mechanisms of EOR using SiO₂/P(MBAAm-co-AM) suspension in heterogeneous, low-permeability reservoirs.

Porous carbon nanofibers doped with nickel (Ni) were successfully fabricated through electrospinning, carbonization, and CO₂ activation techniques using polyacrylonitrile (PAN) and petroleum pitch as carbon sources and nickel acetate as the dopant. During the activation process, Ni was reduced and dispersed in situ on the carbon matrix. The effects of Ni doping content on the morphology and structure of the carbon nanofibers were systematically investigated using SEM, TEM, XPS, XRD, Raman, and BET analyses. The experimental results revealed that the prepared materials had a hierarchically porous structure and that Ni nanoparticles played multiple roles in the preparation process, including catalyzing pore expansion and catalytic graphitization. However, particle agglomeration and fiber fracture occurred when the Ni content was high. In the adsorption/desorption experiments, the sample with 10 wt% Ni doping exhibited the highest specific surface area and micropore volume of 750.7 m²/g and 0.258 cm³/g, respectively, and had the maximum hydrogen storage capacity of 1.39 wt% at 298 K and 10 MPa. The analyses suggested that the hydrogen adsorption mechanism contributed to enhanced H₂ adsorption by the spillover effect in addition to physisorption.

The characteristic scale of flow in micro–nanochannels is generally in the range of 0.01 μm∼1 μm. When crude oil passes through micro-nano channels and tight reservoirs, it shows obvious nonlinear seepage characteristics, which does not conform to the continuity assumption of fluid. Therefore, a non-Newtonian model of crude oil flowing in micro-nano channels and tight reservoirs under the action of shear stress is established, and the relationship between flow rate and apparent viscosity and shear rate is analyzed. The experiment of crude oil flow in micro-nano channels and tight oil reservoir cores shows that the model can be used to describe the nonlinear seepage law of liquid through the nonlinear fitting. The power law index of the oil-phase power-law non-Newtonian fluid is greater than 1 at the micro-nano scale, which conforms to the flow characteristics of the expansive fluid, thus verifying the effectiveness of the non-Newtonian model. In addition, the study of apparent viscosity and shear rate of non-Newtonian fluid shows that the increasing and decreasing trends of flow rate and shear rate and the changing trends of flow rate and pressure gradient are consistent, and shear rate can be used to describe the characteristics of fluid instead of the pressure gradient.

The characteristic scale of pore flow in tight reservoirs is generally in the range of 0.1μm to 1μm, which shows the obvious micro- and nanoscale effect. The traditional oil and gas seepage theory cannot accurately describe the flow law of liquid in the micro- and nanopores. The determination of seepage characteristics is crucial to the development, layout, and prediction of tight oil. Therefore, a non-Newtonian fluid model is established to discuss the flow characteristics of confined liquid in the heterogeneous pores of microtubules and reveal the nonlinear seepage law of water in micro- and nanochannels and tight reservoirs. Based on the characteristics of non-Newtonian fluid of confined fluid in micro- and nanospace, the flow model of non-Newtonian fluid under the action of shear stress was deduced. The flow velocity variation of liquid in micro- and nanochannel and dense core was analyzed, and the flow rate of water was less than that predicted by macro theory. According to the flow experiment of water in micro- and nanochannels, the flow model of power-law non-Newtonian fluid was verified. At the same time, through the flow experiment of water in the dense rock core, the non-Newtonian model was used for nonlinear fitting, and the non-Newtonian power-law parameters and average pore radius were obtained, which verified the effectiveness of the non-Newtonian flow model.