Filtering pore chanels distribution density in Western Siberia

It is well known that information on filter channels distribution density can be obtained based on the data of core samples capillary studies in laboratory conditions. The curve of the fractional participation of pore channels in filtration, as a rule, is obtained by numerical processing of the capillary studies results. In this study, using a generalized mathematical model of capillary curves, an analytical solution is obtained for filtration channels distribution density by size in the conditions of Western Siberia reservoirs. The work shows that the main share in the filtration is taken by pore channels, the sizes of which are close to the maximum value. The density function of the filtering channels is mainly determined by the maximum radius and heterogeneity of the pore channel size distribution. Keywords: capillary pressure curve; generalized model; distribution density; filtering channels.

Characterization of Fluid Drainage Mechanism at Core and Pore Scales: an NMR Capillary Pressure–Based Saturation Exponent Prediction

Capillary pressure (Pc) and electrical resistivity index (RI) curves are used in many reservoir engineering applications. Drainage capillary pressure curve represents a scenario where a non-wetting phase displaces a wetting phase such as (i) during gas injection (ii) gas storage in reservoirs (e.g. aquifer or depleted hydrocarbon reservoirs). The gas used for injection is typically natural gas, N2, or CO2. Gas storage principally used to meet requirement variations, and water injection into oil-wet reservoirs are drainage processes. Resistivity index (RI) curve which is used to evaluate the potential of oil recovery from a reservoir, is also an important tool used in log calibration and reservoir fluid typing. The pore drainage mechanism in a multimodal pore system is important for effective recovery of hydrocarbon reserves; enhance oil recovery (EOR) planning and underground gas storage. The understanding of pore structure and drainage mechanism within a multimodal pore system during petrophysical analysis is of paramount importance to reservoir engineers. Therefore, it becomes inherent to study and establish a way to relate these special core analyses laboratory (SCAL) methods with quick measurements such as the nuclear magnetic resonance (NMR) to reduce the time requirement for analysis. This research employed the use of nuclear magnetic resonance (NMR) to estimate saturation exponent (n) of rocks using nitrogen as the displacing fluid. Different rock types were used in this study that cover carbonates, sandstones, and dolomites. We developed an analytical workflow to separate the capillary pressure curve into capillary pressure curve for macropores and a capillary pressure curve for the micropores, and then used these pore scale Pc curves to estimate an NMR - capillary pressure - based electrical resistivity index - saturation (NMR-RI-Sw) curve for the rocks. We predicted the saturation exponent (n) for the rock samples from the NMR-RI-Sw curve. The NMR-based saturation exponent estimation method requires the transverse (T2) relaxation distribution of the rock - fluid system at various saturations. To verify the reliability of the new workflow, we performed porous plate capillary pressure and electrical resistivity measurements on the rock samples. The reliability of the results for the resistivity index curve and the saturation exponent was verified using the experimental data obtained from the SCAL method. The pore scale Pc curve was used to ascertain the drainage pattern and fluid contribution of the different pore subsystems. For bimodal rock system, the drainage mechanism can be in series, in parallel, or in series - parallel depending on the rock pore structure.

Application of Artificial Intelligence in Lithology Recognition of Petroleum Logging in Low Permeability Reservoirs

In low permeability reservoirs, the conversion accuracy of the existing petroleum logging lithology identification method to small pore capillary pressure curve is not high, resulting in a low rock mass identification accuracy. Therefore, artificial intelligence technology is considered in this study to enhance the accuracy of lithology identification in low permeability reservoirs. Firstly, the radar mapping program is used to predict the position of reservoir oil logging, and then the small pore capillary pressure curve is converted by using the conversion method of piecewise power function scale to obtain the pore characteristics of low-permeability reservoir rocks. On this basis, the crossplot method is used to gather the pore characteristic data in well logging and form a plan, and the response parameters of well logging rock mass are obtained to realize the identification and analysis of lithology. The experimental results show that, compared with the existing identification methods, the accuracy of lithology identification in low-permeability reservoir logging is significantly increased after the application of artificial intelligence technology, and the identification process takes less time, which fully proves that the application of artificial intelligence technology is conducive to improving the performance of lithology identification.

Microscale Mineral and Pore Structure Characterization of the Low-Permeability Sandstone in the Ordos Basin, China

Given the insufficient understanding of the characteristics and controlling factors of the low-permeability sandstone reservoir in the Heshui area, the Ordos Basin, the present study examined the microscale mineral and pore structure of Chang 2 reservoir. It analyzed its major controlling factors using a series of methods, including imaging and indirect methods. The results show that the rocks of Chang 2 reservoir in the study area are dominated by lithic arkose and feldspathic detritus quartz sandstone. The reservoir space develops intragranular pores, feldspar dissolved pores, lithic dissolved pores, and intercrystallite pores. Microcracks can occasionally be found. The average porosity is 10.5%, and the average permeability is 2.2 mD, featuring a low-porosity-ultralow-permeability reservoir. During the reservoir development, traps formed by small-scale nose-shaped uplifts resulting from the tectonic effects provide opportunities for good reservoir space. Sedimentation and diagenetic processes control the degree of development and direction of the evolution of reservoir porosity to a certain degree. Multisegment capillary pressure curve and long missing zone were corresponding to relatively good pore-throat structures. Illite was the predominant diagenetic clay minerals that determine the reservoir quality. These three effects all contribute to the overall development of the reservoir.

A New Method for Predicting the Permeability of Sandstone in Deep Reservoirs

Capillary pressure curve data measured through the mercury injection method can accurately reflect the pore throat characteristics of reservoir rock; in this study, a new methodology is proposed to solve the aforementioned problem by virtue of the support vector regression tool and two improved models according to Swanson and capillary parachor parameters. Based on previous research data on the mercury injection capillary pressure (MICP) for two groups of core plugs excised, several permeability prediction models, including Swanson, improved Swanson, capillary parachor, improved capillary parachor, and support vector regression (SVR) models, are established to estimate the permeability. The results show that the SVR models are applicable in both high and relatively low porosity-permeability sandstone reservoirs; it can provide a higher degree of precision, and it is recognized as a helpful tool aimed at estimating the permeability in sandstone formations, particularly in situations where it is crucial to obtain a precise estimation value.