Search Results (2)

The carboniferous carbonate reservoirs in the North Truva Oilfield have undergone complex sedimentation, diagenesis and tectonic transformation. Various reservoir spaces of pores, caves and fractures, with strong reservoir heterogeneity and diverse pore structures, have been developed. As a result, a quantitative description of the pore structure is difficult, and the accuracy of logging identification and prediction is low. These pose a lot of challenges to reservoir classification and evaluation as well as efficient development of the reservoirs. This study is based on the analysis of core, thin section, scanning electron microscope, high-pressure mercury injection and other data. Six types of petrophysical facies, PG1, PG2, PG3, PG4, PG5, and PG6, were divided according to the displacement pressure, mercury removal efficiency, and median pore-throat radius isobaric mercury parameters, combined with the shape of the capillary pressure curve. The petrophysical facies of the wells with mercury injection data were divided accordingly, and then the machine learning method was applied. The petrophysical facies division results of two mercury injection wells were used as training samples. The artificial neural network (ANN) method was applied to establish a training model of petrophysical facies recognition. Subsequently, the prediction for the petrophysical facies of each well in the oilfield was carried out, and the petrophysical facies division results of other mercury injection wells were applied to verify the prediction. The results show that the overall coincidence rate for identifying petrophysical facies is as high as 89.3%, which can be used for high-precision identification and prediction of petrophysical facies in non-coring wells. Full article

In order to further study the internal relationship between the microscopic pore characteristics and macroscopic mechanical properties of cemented tailings backfill (CTB), in this study, mine tailings and ordinary Portland cement (PC32.5) were selected as aggregate and cementing materials, respectively, and different additives (anionic polyacrylamide (APAM), lime and fly ash) were added to backfill samples with mass concentration of 74% and cement–sand ratios of 1:4, 1:6 and 1:8. After 28 days of curing, based on the uniaxial compressive strength test, nuclear magnetic resonance (NMR) porosity test and the fractal characteristics of pore structure, the relationships of the compressive strength with the proportion and fractal dimension of pores with different radii were analyzed. The uniaxial compressive strength prediction model of the CTB with the proportion of harmless pores and the fractal dimension of harmful pores as independent variables was established. The results show that the internal pores of the material are mainly the harmless and less harmful pores, and the sum of the average proportions of the two reaches 73.45%. Some characterization parameters of pore structure have a high correlation with the compressive strength. Among them, the correlation coefficients of compressive strength with the proportion of harmless pores and fractal dimension of harmful pores are 0.9219 and 0.9049, respectively. The regression results of the strength prediction model are significant, and the correlation coefficient is 0.9524. The predicted strength value is close to the actual strength value, and the predicted results are accurate and reliable. Full article