Evaluation of petrophysical classification of strongly heterogeneous reservoirs based on the MRGC algorithm

The target formation in the study area of the Pearl River Mouth Basin is characterized by complex lithology and thin interbedded layers, with a large pore-permeability distribution range and strongly heterogeneous characteristics, which makes the reservoir pore structure and production capacity significantly different and brings research difficulties for reservoir logging evaluation and desert identification. The conventional reservoir classification method is mainly based on physical research, which requires developing extremely accurate formulas for calculating porosity and permeability; the calculation accuracy of pore permeability of low-porosity and low-permeability reservoirs is difficult to guarantee; and the conventional logging data cannot be comprehensively applied in reservoir classification. In this paper, taking Zhujiang and Zhuhai Formation reservoirs in the Huizhou M oilfield as an example, we integrated core analysis data such as core cast thin section, pore permeability data, rock electrical parameters, grain size, and relative permeability curves and combined with petrophysical parameters and pore structure characteristics to classify the reservoirs. The artificial neural network is used to predict the resistivity of saturated pure water (R0) to remove the influence of oil and gas on reservoir resistivity. The natural gamma ray (GR) “fluctuation” is used to calculate the variance root of variation (GS) to reflect the lithological variability and sedimentary heterogeneity of the reservoir, and then the conventional logging preferences, R0 and Gs (based on GR), are classified based on the automatic clustering MRGC algorithm to classify the logging facies. To classify the petrophysical phase reservoirs under the constraint of pore structure classification, we proposed a petrophysical classification logging model based on the natural gamma curve “fluctuation” intensity for strongly heterogeneous reservoirs. The learning model is extended to the whole area for training and prediction of desert identification, and the prediction results of the model are in good agreement with the actual results, which is important for determining favorable reservoirs in the area and the adjustment of oilfield development measures

Plant Expression of Cocaine Hydrolase-Fc Fusion Protein for Treatment of Cocaine Abuse

BACKGROUND: A recently reported cocaine hydrolase (CocH3) fused with fragment crystallizable (Fc) region of human immunoglobulin G1, denoted as CocH3-Fc, is known as a promising therapeutic candidate for the treatment of cocaine overdose and addiction. A challenge for practical therapeutic use of this enzyme exists in the large-scale protein production and, therefore, it is interesting to identify a low-cost and feasible, sustainable source of CocH3-Fc production. RESULTS: CocH3-Fc was transiently expressed in plant Nicotiana benthamiana leaves. The plant-expressed protein, denoted as pCocH3-Fc, was as active as that expressed in mammalian cells both in vitro and in vivo. However, compared to the mammalian-cell expressed CocH3-Fc protein, pCocH3-Fc had a shorter biological half-life, probably due to the lack of protein sialylation in plant. Nevertheless, the in vivo half-life was significantly extended upon the PEGylation of pCocH3-Fc. The Fc fusion did not prolong the biological half-life of the plant-expressed enzyme pCocH3-Fc, but increased the yield of the enzyme expression in the plant under the same experimental conditions. CONCLUSIONS: It is feasible to express pCocH3-Fc in plants. Further studies on the pCocH3-Fc production in plants should focus on the development of vectors with additional genes/promoters for the complete protein sialylation and for a better yield

A Limited Memory BFGS Method for Solving Large-Scale Symmetric Nonlinear Equations

A limited memory BFGS (L-BFGS) algorithm is presented for solving large-scale symmetric nonlinear equations, where a line search technique without derivative information is used. The global convergence of the proposed algorithm is established under some suitable conditions. Numerical results show that the given method is competitive to those of the normal BFGS methods