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The characteristic scale of pore flow in tight reservoirs is generally in the range of 0.1 μm to 1 μm, 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.

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.

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.

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