Micropore Structures and Percolation Characteristics of Carboniferous Carbonate Reservoirs in the Zanarol Area.

Proven oil and gas reserves in carbonate rocks comprise a high proportion of oil and gas fields, but these reservoirs have high heterogeneity. It is of great importance to study the micropore structures and percolation characteristics of carbonate rocks for the development of oilfields. In this paper, reservoirs are studied by means of casting sections, high-pressure mercury injection, and water and gas flooding oil phase permeability experiments. Reservoirs are classified into three categories, I, II, and III, by the k-means cluster analysis method. The results show that class I reservoirs are mainly composed of biolimestone with strong dissolution, displacement pressure of 0.016 MPa, median pressure of 0.135 MPa, mercury removal efficiency of 17.15%, well-developed pore throats, and good connectivity. They have the highest reservoir quality index and strong percolation ability. Class II reservoirs are mainly biogenic limestone and granular limestone with intergranular pores, a displacement pressure of 0.098 MPa, a median pressure of 6.026 MPa, and a mercury removal efficiency of 25.82%. The pore throat class is complex, and the sorting is poor. Class III reservoirs are mainly clastic limestone with residual intergranular pores, poor connectivity, displacement pressure of 0.403 MPa, median pressure of 3.77 MPa, mercury removal efficiency of 14.01%, small median radii, and good sorting performance. Relative permeability experiments show that water drive permeability at the isopermeability point is (0.049 10-3 µm2) higher than that of gas drive (0.041 10-3 µm2). The permeability of oil and water phases in class I reservoirs is obviously higher than those of class II and III reservoirs. When gas flooding is used, the phase permeability characteristics of class I and II reservoirs are no different than when water flooding is used. The permeability of gas flooding is slightly lower than that of water flooding. Because of the high proportion of micropores in class III reservoirs, gas can easily enter the pores, so the relative permeability of the gas phase increases rapidly. With increases in injection volume, the ultimate oil displacement efficiency of class I reservoirs can reach 53.2%, while those of class II and III reservoirs are 50.7 and 46.1%, respectively. This study provides important guidance for formulating oilfield development plans.

Modeling flood susceptibility using data-driven approaches of naïve Bayes tree, alternating decision tree, and random forest methods.

Floods are one of the most devastating types of disasters that cause loss of lives and property worldwide each year. This study aimed to evaluate and compare the prediction capability of the naïve Bayes tree (NBTree), alternating decision tree (ADTree), and random forest (RF) methods for the spatial prediction of flood occurrence in the Quannan area, China. A flood inventory map with 363 flood locations was produced and partitioned into training and validation datasets through random selection with a ratio of 70/30. The spatial flood database was constructed using thirteen flood explanatory factors. The probability certainty factor (PCF) method was used to analyze the correlation between the factors and flood occurrences. Consequently, three flood susceptibility maps were produced using the NBTree, ADTree, and RF methods. Finally, the area under the curve (AUC) and statistical measures were used to validate the flood susceptibility models. The results indicated that the RF method is an efficient and reliable model in flood susceptibility assessment, with the highest AUC values, positive predictive rate, negative predictive rate, sensitivity, specificity, and accuracy for the training (0.951, 0.892, 0.941, 0.945, 0.886, and 0.915, respectively) and validation (0.925, 0.851, 0.938, 0.945, 0.835, and 0.890, respectively) datasets.

Landslide spatial modelling using novel bivariate statistical based Naïve Bayes, RBF Classifier, and RBF Network machine learning algorithms.

Landslides are major hazards for human activities often causing great damage to human lives and infrastructure. Therefore, the main aim of the present study is to evaluate and compare three machine learning algorithms (MLAs) including Naïve Bayes (NB), radial basis function (RBF) Classifier, and RBF Network for landslide susceptibility mapping (LSM) at Longhai area in China. A total of 14 landslide conditioning factors were obtained from various data sources, then the frequency ratio (FR) and support vector machine (SVM) methods were used for the correlation and selection the most important factors for modelling process, respectively. Subsequently, the resulting three models were validated and compared using some statistical metrics including area under the receiver operating characteristics (AUROC) curve, and Friedman and Wilcoxon signed-rank tests The results indicated that the RBF Classifier model had the highest goodness-of-fit and performance based on the training and validation datasets. The results concluded that the RBF Classifier model outperformed and outclassed (AUROC = 0.881), the NB (AUROC = 0.872) and the RBF Network (AUROC = 0.854) models. The obtained results pointed out that the RBF Classifier model is a promising method for spatial prediction of landslide over the world.

Criteria for Applying the Lucas-Washburn Law.

Spontaneous imbibition happens in many natural and chemical engineering processes in which the mean advancing front usually follows Lucas-Washburn's law. However it has been found that the scaling law does not apply in many cases. There have been few criteria to determine under what conditions the Washburn law works. The effect of gravity on spontaneous imbibition in porous media was investigated both theoretically and experimentally. The mathematical model derived analytically was used to calculate the imbibition rates in porous media with different permeabilities. The results demonstrated that the effect of gravity on spontaneous imbibition was governed by the hydraulic conductivity of the porous media (permeability of the imbibition systems). The criteria for applying the Lucas-Washburn law have been proposed. The effect of gravity becomes more apparent with the increase in permeability or with the decrease in CGR number (the ratio of capillary pressure to gravity forces) and may be ignored when the CGR number is less than a specific value N(*)(cg) ≅ 3.0. The effect of gravity on imbibition in porous media can be modeled theoretically. It may not be necessary to conduct spontaneous imbibition experiments horizontally in order to exclude the effect of gravity, as has been done previously.