Modeling Permeability Using Advanced White-Box Machine Learning Technique: Application to a Heterogeneous Carbonate Reservoir.

From exploration to production, the permeability of reservoir rocks is essential for various stages of all types of hydrocarbon field development. In the absence of costly reservoir rock samples, having a reliable correlation to predict rock permeability in the zone(s) of interest is crucial. To predict permeability conventionally, petrophysical rock typing is done. This method divides the reservoir into zones of similar petrophysical properties, and the permeability correlation for each zone is independently developed. The challenge of this approach is that the success depends upon the reservoir's complexity and heterogeneity and the methods and parameters used for rock typing. As a result, in the case of heterogeneous reservoirs, conventional rock typing methods and indices fail to predict the permeability accurately. The target area is a heterogeneous carbonate reservoir in southwestern Iran with a permeability range of 0.1-127.0 md. In this work, two approaches were used. First, based on permeability, porosity, the radius of pore throats at mercury saturation of 35% (r35), and connate water saturation (Swc) as inputs of K-nearest neighbors, the reservoir was classified into two petrophysical zones, and then, permeability for each zone was estimated. Due to the heterogeneous nature of the formation, the predicted permeability results needed to be more accurate. In the second part, we applied novel machine learning algorithms, modified group modeling data handling (GMDH), and genetic programming (GP) to develop one universal permeability equation for the whole reservoir of interest as a function of porosity, the radius of pore throats at mercury saturation of 35% (r35), and connate water saturation (Swc). The novelty of the current approach is that despite being universal, the models developed using GP and GMDH performed substantially better than zone-specific permeability, index-based empirical, or data-driven models used in the literature, such as FZI and Winland. The predicted permeability using GMDH and GP resulted in accurate prediction with R2 of 0.99 and 0.95, respectively, in the heterogeneous reservoir of interest. Moreover, as this study aimed to develop an explainable model, different parameter importance analyses were also applied to the developed permeability models, and r35 was found to be the most impactful feature.

Effect of microbubbles on transcranial doppler ultrasound-assisted intracranial recombinant tissue-type plasminogen activator thrombolysis.

OBJECTIVES: The aim of this study was to evaluate the effect of microbubbles on the efficacy of transcranial doppler (TCD) ultrasound-assisted thrombolytic therapy of recombinant tissue-type plasminogen activator (rt-PA). METHODS: Male New Zealand white rabbits (n = 36) were randomly divided into an rt-PA group (n = 18) and an rt-PA plus microbubble group (n = 18). After the cerebral infarction model was constructed with autologous blood clots, rt-PA and rt-PA plus microbubble intervention were performed, respectively. The hemodynamic changes and infarct size of the two groups were recorded. In addition, the ELISA method was used to detect the level of nitric oxide (NO), superoxide dismutase (SOD), and malondialdehyde (MDA) in the brain tissue of the two-group graph model and high-sensitivity C-reactive protein (hs-CRP) in the serum. RESULTS: In the rt-PA group, the recanalization rate was 38.9% and the average infarct size was 11.8%. In the rt-PA plus microbubble group, the recanalization rate was 66.7% and the average infarct size was 8.2%. In addition, the average values for NO, SOD, MDA, and hs-CRP were 16.48 ± 5.39 µmol/L, 730.2 ± 9.86 U/mg, 0.92 ± 0.43 nmol/mg, and 8.56 ± 1.64 mg/L in the rt-PA group, respectively, and the average values were 9.18 ± 3.37 µmol/L, 426.2 ± 6.39 U/mg, 0.73 ± 0.44 nmol/mg, and 5.23 ± 0.94 mg/L in the rt-PA plus microbubble group, respectively. CONCLUSIONS: The addition of microbubbles enhanced the effects of TCD-assisted rrt-PA thrombolysis.

The hypoxia-related signaling pathways of vasculogenic mimicry in tumor treatment.

Tumors require a blood supply for survival, growth, and metastasis. It is widely accepted that the development of the tumor microcirculation compartment need the production of new blood vessels (angiogenesis). Vasculogenic mimicry (VM) is an alternative type of blood supplement independent of endothelial vessels which refers to the formation of tumor cell-lined vessels and is associated with tumor invasion, metastasis and poor cancer patient prognosis. Although a variety of proteins and microenvironmental factors are known to contribute to VM, the mechanisms underlying its formation remain unclear. The induction of VM seems to be related to hypoxia, which may promote the plastic, transendothelial phenotype of tumor cells capable of VM. Here, with regard to the above aspects, we review the advanced research on VM including molecular mechanisms and its clinical significance; and explore the development of VM-related strategies which are being utilized for anticancer treatment.

[Application of data fusion of microscopic spectral imaging in reservoir characterization].

In recent years, spectral imaging technique has been applied widely in mineralogy and petrology. The technique combines the spectral technique with imaging technique. The samples can be analyzed and recognized both in spectra and space by using the technique. However, the problem is how to acquire the needful information from a large number of data of spectral imaging, and how to enhance the needful information. In the present paper, the experimental data were processed by using the technique of data fusion of microscopic spectral imaging. The space distribution map of chemical composition and physical parameters of samples were obtained. The result showed that the distribution of different hydrocarbon in the reservoirs, pore connectivity, etc. were revealed well. The technique of data fusion of microscopic spectral imaging provided a new method for reservoir characterization.