Pore Structure Characterization of Eocene Low-Permeability Sandstones via Fractal Analysis and Machine Learning: An Example from the Dongying Depression, Bohai Bay Basin, China

Estimation of pore structure and permeability in tight carbonate reservoir based on machine learning (ML) algorithm using SEM images of Jaisalmer sub-basin, India

Analyzing the pore structure in carbonate reservoirs plays a crucial role in predicting fluid flow characteristics within these formations. The goal of the study was to use machine learning techniques for pore structure analysis and estimation of permeability in carbonate reservoirs. We implemented these algorithms by examining 2D scanning electron microscope (SEM) images of carbonate samples from the Jaisalmer sub-basin captured at various magnifications. In the initial stage of the analysis, various binarization algorithms were applied to determine carbonate sample porosity. Among these algorithms, the MaxEntropy algorithm gave a porosity value closely aligned with those obtained through petrography analysis. We employed the watershed algorithm to find the pore network parameters of carbonate samples at various magnifications. We observed that changes in magnification affected pore network parameters, resulting in a reduction in pore size distribution, throat radius, and grain size. Subsequently, we employed the numerical lattice Boltzmann method (LBM) to estimate the permeability of carbonate samples and compared to values derived from well logs. We employed machine learning (ML) algorithms, specifically Artificial Neural Network (ANN) and Support Vector Machine (SVM), to predict the permeability of carbonate samples. The input features for these models were the pore network parameters, while the LBM permeability values served as the output. We examine the prediction performance of these methods against the measured LBM permeability by conducting the error analysis and the coefficient of determination ([Formula: see text]) calculation. Our findings revealed that the ANN models outperformed the SVM models. Specifically, the ANN model displayed an impressive R2 value of 0.892, along with root mean square error (RMSE), mean squared error (MSE) and, mean absolute error (MAE) values of 1.927, 3.716 and 1.580, respectively. In contrast, the SVM model yielded an R2 value of 0.849, with RMSE, MSE and, MAE values of 2.324, 5.401 and, 2.166 respectively, when assessed on testing data of measured permeability. This study found that ANN is more dependable, robust, and precise than SVM in forecasting carbonate sample permeability.

Experimental and digital investigations of heterogeneity in lower cretaceous carbonate reservoir using fractal and multifractal concepts

Characterization and prediction of reservoir heterogeneity are crucial for hydrocarbon production. This study applies the multifractal theory using both numerical and experimental data to characterize quantitatively the heterogeneity of pore structures in Lower Cretaceous limestone reservoir from the United Arab Emirates. Fractal dimensions calculated from three dimensional digital images showed good correlation (R2 =  + 0.69) with experimental high-pressure mercury injection (HPMI) measurements. Moreover, both experimental and numerical fractal dimensions correlate well with experimental HPMI porosity measurements. Multifractal parameters such as the non-uniformity degree of the pore structures Δα, the asymmetry degree in the vertical axis Δf(α), the concentration of pore size distribution α0 and the asymmetry degree in the horizontal axis Rd estimated from digital and experimental data correlated well and revealed ability to quantitatively describe samples heterogeneity. The ranges of digital and experimental multifractal parameters provided the means to differentiate between homogeneous and heterogeneous samples.

Pore Structure and Movable Fluid Characteristics of Typical Sedimentary Lithofacies in a Tight Conglomerate Reservoir, Mahu Depression, Northwest China

The pore structure and movable fluid characteristics of tight conglomerate reservoirs are complex, which are greatly different from conventional reservoirs. The depositional mechanism is the fundamental factor controlling the physical properties of conglomerate reservoirs. However, there is a lack of systematic research on the pore structure and movable fluid characteristics of conglomerate reservoirs with typical sedimentary facies. This paper investigates the pore structure and movable fluid characteristics of conglomerate of different sedimentary facies based on various experiments. Casting thin sections, X-ray diffraction, scanning electron microscopy, high-pressure mercury injection, and nuclear magnetic resonance experiments were conducted on 32 conglomerates samples from the Mahu Sag, Junggar Basin, China. The quality classification method of tight conglomerate reservoirs is established. The results show that the conglomerate can be divided into three sedimentary facies; traction flow conglomerate (TFC) and pebbled sandstone (PSS) mainly develop intergranular pores and dissolved pores; and the pore diameter curves are mainly a double peak, single peak, and flat peak. Gravity flow conglomerate (GFC) mainly develops dissolved pores and interstitial micropores, and the pore diameter curve is mainly a single peak. PSS includes pebbled gritty sandstone (P(G)SS) and pebbled fine sandstone (P(F)SS). TFC and P(G)SS are favorable class I reservoirs, while GFC and P(F)SS are nonfavorable class II reservoirs. A new parameter, the ratio of the major axis to the minor axis of the pore outer ellipse (axial ratio), is proposed to quantitatively describe the compaction effect. The average axial ratios of the three lithofacies are 3.04, 3.98, and 8.78, respectively, indicating that the compaction is intensified and the pore structure becomes worse. By analyzing the correlation between pore structure parameters and permeability, it is found that the main controlling factors of permeability of GFC and TFC are sorting and connectivity, respectively, and the main flow radius is the most suitable parameter to describe permeability. A linear spectral decomposition method was used to establish a new quantitative calculation method of movable fluid saturation for different types of pores, and the results show that the movable fluid saturation of intergranular pores is the highest (average: 65.43%), and the movable fluid saturation of TFC and P(G)SS with more intergranular pores is the highest. Movable fluid saturation is inversely proportional to the content of I/S and the compaction rate and positively proportional to the content of quartz and feldspar and the cementation rate. The fluid mobility of water-wet samples is weaker. The research results provide theoretical support for the identification of favorable reservoirs and the cognition of a development mechanism.