Permeability Prediction Model Research Articles

Integrated petrographical and petrophysical studies of sandstones from the Araba Formation for reservoir characterization

The present study aims at an integration of both petrographic and petrophysical data of 45 sandstone samples collected from Araba Formation (Lower Paleozoic) for reservoir characterization. The studied Araba Formation is classified into three types of facies: (i) quartz arenite, (ii) sublithic quartz arenite, and (iii) calcareous quartz arenite. The mineralogical composition of the studied sandstone samples includes mainly quartz and detrital clays, some pyrite, and iron oxides as cement. The petrographic investigation of the studied Araba Formation reveals various diagenetic processes including compaction and pressure solution, cementation with iron oxides, carbonates, or clays. The porosity values for investigated samples range from 14.5 to 27.8% and permeability values from 2.4 mD to 8.4 D, the surface area per unit pore volume (Spor) from 0.011 to 17 µm−1, the formation factor from 6 to 13, and the dimensionless volumetric magnetic susceptibility from 13 10−6 to 126 10−6. The samples of facies (i) and (ii) are characterized by higher values of porosity and permeability. The samples of facies (iii) indicate increasing permeability with rising porosity. The relationship between permeability and formation factor reflects the effect of porosity changes. The specific surface Spor shows an inverse relationship to permeability. The petrophysical investigations provide relationships between permeability and other reservoir parameters such as porosity, formation factor, specific surface, and longitudinal and transverse relaxation time resulting from nuclear magnetic resonance (NMR) relaxometry. The permeability prediction model based on NMR data proves to be applicable for samples from facies (i) and (ii), whereas the predictive quality is considerably lower for facies (iii).

Erosion Effect on Non-Darcy Hydraulic Characteristics of Limestone and Mudstone Mixture

In order to study the water inrush mechanism of faulted rock mass, a series of erosion seepage experiments were carried out to test the non-Darcy hydraulic properties of a limestone and mudstone mixture. The test results are employed to verify and modify a classic permeability prediction model. Based on the evolution of mass loss rate, the erosion process can be divided into four stages, i.e., particle rearrangement, severe erosion, mild erosion, and stable seepage. During the erosion, the sample height decreases gradually, the water pressure at outlet grows first and then keeps stable, and the porosity and permeability decrease slightly first, then grow gradually and finally keep stable. The hydraulic properties show a more significant variation in the sample within high mudstone particle contents. Through the comparison of test results and the predicted results by the classic Carman-Kozeny model, it is found that the accuracy of the model is greatly affected by lithology. Based on this investigation, a revised model is proposed which introduces a proportional coefficient related to the rock composition, so as to increase the predicted precision in variable rock composition. The forecast accuracy of the revised model is much higher than the classical one in permeability although it decreases with the increase of mudstone content.

A new predrilling reservoir permeability prediction model and its application

The accurate prediction of sandstone permeability from variables such as porosity, composition, and texture is one of the major problems in petroleum geology. However, the application of geological variables related to diagenesis in permeability prediction models is relatively rare, especially the anomalous variations of clay content and type. This study investigates the differential control of sedimentation and diagenesis on permeability using a comprehensive analysis of cores, thin sections, routine core analysis, X-ray diffraction, and scanning electron microscopy from sandstone samples in the third member of the Weizhou Formation (Ew3), Weizhou 12-X oilfield, Weixinan Sag, Beibu Gulf Basin, China. An innovative sandstone reservoir permeability prediction model is proposed by introducing the clay factor β confining the effect of clay minerals on permeability, predicting the spatial distribution of diagenetic index (ID) using the Diagenetic Modeling System (DMS), and improving the Walderhaug permeability prediction model. The permeability of the well evaluated in the study was predicted with the relative and absolute errors changing from 0.01 mD to 192.65 mD and from 0.00 to 1.00, with an average values of 38.6 mD and 0.33, respectively, based on the sandstone reservoir permeability prediction model in Ew3. The permeability predicted by the permeability prediction model matches well with the measured permeability in Ew3. The results indicate that permeability is successfully predicted by the modified Walderhaug permeability prediction model for most sandstone reservoirs with strong diagenesis (such as anomalous changes in clay mineral types and content) under the background of a single distributary channel sedimentary microfacies. In general, anomalously high permeability zones are preferentially distributed in dominant sedimentary-diagenetic facies.

Generalizable Permeability Prediction of Digital Porous Media via a Novel Multi‐Scale 3D Convolutional Neural Network

AbstractSubsurface characterization is critical in understanding and controlling many natural and industrial processes including groundwater movement, oil extraction, and geological carbon dioxide sequestration. While recent advances in three‐dimensional (3D) imaging of core samples have enabled digital subsurface characterization, the exorbitant computational cost associated with direct numerical simulation in 3D remains a persistent challenge. In contrast, machine learning models are much more efficient, though their use in subsurface characterization is still in its infancy. Here, we introduce a novel 3D convolutional neural network (CNN) for end‐to‐end prediction of permeability, which is a fundamental characteristic of subsurface porous media. We show that increasing the dataset size and diversity, utilizing multi‐scale feature aggregation, and optimizing the network architecture elevate the model accuracy beyond that of existing state‐of‐the‐art 3D CNN models for permeability prediction. We demonstrate that the model is generalizable, and it is capable of predicting the permeability of previously unseen samples with an excellent accuracy.

New insights into gas production behavior and seepage-wettability evolution during methane hydrate dissociation in sand matrix by NMR investigation

The commercial and sustainable exploitation of NGH reservoir requires a comprehensive understanding of gas production behaviors and seepage-wettability evolutions during hydrate dissociation process in hydrate bearing sediments. In this paper, the in-situ real-time CH4 hydrate dissociation process in sand pack was acquired with the aid of NMR technique. The results showed that the step-wise depressurization method could effectively prevent the ice generation and/or secondary hydrate formation phenomenon during NGH exploitation. When the pore pressure reached the hydrate dissociation surging point, the CH4 hydrate dissociation rate would enter a surge stage. Then, the hydrate dissociation rate of simple step-wise depressurization method kept steady for a relatively long time, while the hydrate dissociation rate of combined method showed a pressure-sensitive feature. And the optimum final CH4 recovery ratio of 92.85% was obtained in Case 7 with the finer step-wise depressurization combined method. The comparison between measured gas effective permeability and prediction value indicated that the accuracy of current widely used permeability prediction models was limited due to lacking consideration of wettability evolutions of hydrate bearing sediments. In this study, a novel NMR-based approach for quantitatively characterizing the overall wettability evolution of hydrate bearing sediments was proposed. It was found out that the hydrophilicity of hydrate bearing mixing system increased with the decrease of hydrate saturation during the dissociation process. And a modified Tokyo model coupled with wettability term was proposed to increase the normalized permeability prediction accuracy by 9.75%, which indicated the flow seepage behavior was governed by the coupling effect of hydrate pore habits and wettability of hydrate bearing sediments.

Permeability Prediction of Sandstones Based on Mercury Intrusion Method

The use of the mercury intrusion method has been one of the most relevant trends in determining the permeability of porous media in the past decades. In this paper, general knowledge of sandstone reservoir evaluation is delineated including the pore distribution of sandstones and air permeability measurement. Based upon the paradigmatic study conducted by Purcell, a schematic diagram illustrating apparatus used in mercury intrusion is shown and introduced, and the relevant procedure is also outlined. Four significant permeability prediction models are described respectively and compared based on researches focusing on tight rocks. By doing so, this article reveals that the performance of the models is different despite the painstaking analysis and the significance of these studies. The contribution of this present study is providing a general reference of permeability prediction by mercury intrusion method as well as its previous momentous studies, giving a comparison among the given models.

A Site-specific Comparison of Permeability Prediction Models in Alluvial Sediments from Physical and Geoelectrical Measurements

An evaluation of five petrophysical models for hydraulic permeability predication was completed for a site-specific set of alluvial sediments. The models considered were the Kozeny-Carmen (1927–1937), Börner (1996), Revil-Cathles (1999), and two Revil-Florsch (2010) models. The river deposited sediments represent a relatively narrow grain-size distribution, and were acquired adjacent to the Kansas River, in Olathe, Kansas. Using measured physical, electrical, and hydraulic data from Slater and Glaser (2003) , a comparison of the performance of these five petrophysical models for hydraulic permeability estimation of soils was completed. For models where the key parameter is effective grain-size, three model variations were considered using d10, d50, and d90, resulting in an evaluation of a total of eleven individual models. Parameters included in the models can be classified as three different types: a) physical parameters only, b) electrical parameters only, and c) physical and electrical parameters together. The performance of each model was rated in terms of linear regression R squared, slope, and y-intercept values when plotted against the measured hydraulic conductivities. The top three models were the Kozeny-Carmen, a modified Revil-Cathles, and the Börner model. The Kozeny-Carmen model performed with the highest rating, followed by the modified Revil-Cathles model, and the Börner model rounding out the top three. There was a significant disparity between the rating associated with the top three and the fourth best performing model suggested by Revil and Florsch. However, it should be noted that the Börner model and Revil-Florsch model are based entirely on electrical measurements. The Revil-Cathles model was greatly improved when d10 was substituted for d50 for this limited sediment grain-size range.

Development of HFU-based permeability prediction models using core data for characterisation of a heterogeneous Oligocene sand in the Nam Con Son basin

Core data by both routine and special core analysis are required to understand and predict reservoir petrophysical characteristics. In this research, a total number of 50 core plugs taken from an Oligocene sand (T30) in the Nam Con Son basin, offshore southern Vietnam, were tested in the core laboratory of the Vietnam Petroleum Institute (VPI).
 The results of routine core analysis (RCA) including porosity and permeability measurements were employed to divide the study reservoir into hydraulic flow units (HFUs) using the global hydraulic elements (GHEs) approach. Based on five classified HFUs, 16 samples were selected for special core analysis, i.e., mercury injection capillary pressure (MICP) and grain size analyses for establishing non-linear porosity-permeability model of each HFU based on Kozeny-Carman equation, which provides an improved prediction of permeability (R2 = 0.846) comparing to that by the empirical poro-perm relationship (R2 = 0.633).
 In addition, another permeability model, namely the Winland R35 method, was applied and gave very satisfactory results (R2 = 0.919). Finally, by integrating the results from MICP and grain size analyses, a good trendline of pore size distribution index (λ) and grain sorting was successfully obtained to help characterise the study reservoir. High λ came with poor sorting, and vice versa, the low λ corresponded to good sorting of grain size.

A comparative study of permeability prediction for Eocene sandstones — Part 1: Application of modified Swanson models to mercury injection capillary pressure and nuclear magnetic resonance data

The mercury injection capillary pressure (MICP) method and nuclear magnetic resonance (NMR) relaxometry provide insight into the pore radius distribution (PRD) either of pore throats (MICP) or pore bodies (NMR) of rocks. One variety of permeability ( k) prediction models is based on the knowledge of the PRD. We have evaluated the quality of k-prediction models using a sample set of Eocene sandstones with known values of measured permeability. The Swanson method relates the apex point of the capillary pressure curve to k. Although this widely acknowledged method uses only a single point of the PRD, it provides a predictive quality with an average ratio between the measured and predicted permeability lower than a factor of three. The pore throat radius of the apex point proves to be a good proxy of the effective hydraulic radius. We determine that an improved k prediction can be achieved if a larger section of the PRD is considered in our generalized model. Using reliable values of surface relaxivity, the NMR relaxation time distribution is transformed into a PRD. We find that a characteristic apex point can be determined from NMR data, too. This characteristic point enables a good k prediction for the set of Eocene sandstone samples. In contrast to MICP, the predictive quality cannot be improved by applying an integration over a larger section of the PRD. Further tests with samples of different pore structure and lithology should demonstrate the potential of our models.

Simulation and experiment analysis of relationship between voids and permeability of composites

Due to the significant weight reduction advantage, aerospace composite tanks have become the focus of international competition of spacecraft. However, the permeation of small molecules at low temperature caused by the internal voids of composites is one of the key technical problems which restrict the engineering application of composite tanks. In this study, a combination of theoretical research and experimental research was adopted. By changing the curing pressures of the composites’ autoclave process, laminates with different porosities were prepared and tested for low-temperature permeability. Based on the grayscale processing and median filtering methods, a permeability prediction model with the true voids morphology of composite materials was established. Based on the random medium theory, the random voids model of composites was built to study the effect of void size and shape on the permeability of laminates. The results showed that the finite element analysis of composites, permeability based on the real voids morphology model was in good agreement with the experimental results, which proved the feasibility of this method. The permeation rate of laminates increased with the raised of porosity, and the internal porosity of the laminates could be diminished by improving the curing pressure so as to reduce the permeation of the composite components. Under the condition of a certain porosity, the laminates with small, stripe voids had a higher permeation rate than the laminates with large, circular voids.

Erosion Effect on Non-Darcy Hydraulic Characteristics of Limestone and Mudstone Mixture

In order to study the water inrush mechanism of faulted rock mass, a series of erosion seepage experiments were carried out to test the non-Darcy hydraulic properties of a limestone and mudstone mixture. The test results are employed to verify and modify a classic permeability prediction model. Based on the evolution of mass loss rate, the erosion process can be divided into four stages, i.e., particle rearrangement, severe erosion, mild erosion, and stable seepage. During the erosion, the sample height decreases gradually, the water pressure at outlet grows first and then keeps stable, and the porosity and permeability decrease slightly first, then grow gradually and finally keep stable. The hydraulic properties show a more significant variation in the sample within high mudstone particle contents. Through the comparison of test results and the predicted results by the classic Carman-Kozeny model, it is found that the accuracy of the model is greatly affected by lithology. Based on this investigation, a revised model is proposed which introduces a proportional coefficient related to the rock composition, so as to increase the predicted precision in variable rock composition. The forecast accuracy of the revised model is much higher than the classical one in permeability although it decreases with the increase of mudstone content.

Development of Machine Learning Based Blood-brain Barrier Permeability Prediction Models Using Physicochemical Properties, MACCS and Substructure Fingerprints

Background: Blood-Brain Barrier (BBB) protects the central nervous system from systemic circulation and maintains the homeostasis of the brain. BBB permeability is one of the essential characteristics of drugs acting on the central nervous system to indicate if the drug could reach the brain or not. The available laboratory methods for the prediction of BBB permeability are accurate but expensive and time-consuming. Therefore, many attempts have been made over the years to predict the BBB permeability of compounds using computational approaches. The accuracy of the prediction models with external dataset has always been an issue with the prediction models. Objective: To develop Machine learning-based BBB permeability prediction model using physicochemical properties and molecular fingerprints Methods: Support vector machine (SVM), k-nearest neighbor (kNN), Random forest (RF), and Naïve Bayes (NB) algorithms were applied on a large dataset of 1978 compounds using 1917 feature vectors containing physicochemical properties, MACCS fingerprints, and substructure fingerprints to predict the BBB permeability. Results and Discussion: The comparative analysis of performance metrics of developed models suggested that SVM with the radial basis function kernel performed better than the kNN, RF, and NB algorithms. The BBB permeability prediction model's accuracy with the SVM was 96.77%. The prediction performance of the model developed in this study was found better than the existing machine learning-based BBB permeability prediction models. Conclusion: The prediction model developed in this study could be useful for screening compounds based on their BBB permeability at the preliminary stages of drug design and development.

Permeability prediction of multi-stage tight gas sandstones based on Bayesian regularization neural network

In order to construct a unified permeability prediction model for multi-stage tight gas sandstones with permeability across 6 orders of magnitude and changeable porosity-permeability relationship, Bayesian regularization neural network is properly configured with core porosity, conventional logs and a few derivates of them as input items. With high accuracy and excellent generalization, it is promising to be stably and reliably popularized in the study area. The way of model construction, optimization and evaluation may provide underlying insights needed for permeability prediction of similar reservoirs and application of machine learning in reservoir evaluation.

Petrophysical heterogeneity of the early Cretaceous Alamein dolomite reservoir from North Razzak oil field, Egypt integrating well logs, core measurements, and machine learning approach

Capturing the petrophysical heterogeneities within a reservoir has a critical influence on reservoir deliverability as well as field development programs. In this study, we report a comprehensive petrophysical evaluation of the oil-producing Aptian Alamein dolomite reservoir from the North Razzak field, Western Desert of Egypt. Integration of wireline logs and routine core analysis indicates that the Alamein reservoir has an extremely wide range of porosity (1–23%) and permeability (0.01–7000 mD), contributed by the early diagenetic dolomitization history and complex distribution of vugs. Petrophysical assessment by reservoir quality index (RQI) and flow zone indicator (FZI) infers that the megaporous rock types offer very good to excellent reservoir qualities and macroporosity dominated intervals are of fair to good quality. Further, we developed a permeability prediction model in this challenging carbonate rock based on Random Forest (RF) regression, and tested its efficacy and generalizability by well-defined performance metrics. The RF-based algorithm provided a more confident permeability prediction (R2 = 0.937) compared to conventional methods. Based on the petrophysical attributes; six distinct petrofacies (PF) associations are identified. PF-1, PF-3, and PF-5 provide excellent reservoir qualities with superlative storage capacity and hydraulic flow potential contributed by connected vugs, while the microporosity-dominated impervious PF-2 and PF-4 intervals act as intra-reservoir permeability barriers. We suggest that the higher initial oil production rate was mainly contributed by the larger connected pores and vuggy spaces. As reservoir pressure drops, hydrocarbon flows restrict to the smaller pores causing accelerated production weakening. Based on this comprehensive analysis, a suitable drilling and completion strategy is recommended for the future reservoir development program.

Prediction of the Blood-Brain Barrier (BBB) Permeability of Chemicals Based on Machine-Learning and Ensemble Methods.

The ability of chemicals to enter the blood-brain barrier (BBB) is a key factor for central nervous system (CNS) drug development. Although many models for BBB permeability prediction have been developed, they have insufficient accuracy (ACC) and sensitivity (SEN). To improve performance, ensemble models were built to predict the BBB permeability of compounds. In this study, in silico ensemble-learning models were developed using 3 machine-learning algorithms and 9 molecular fingerprints from 1757 chemicals (integrated from 2 published data sets) to predict BBB permeability. The best prediction performance of the base classifier models was achieved by a prediction model based on an random forest (RF) and a MACCS molecular fingerprint with an ACC of 0.910, an area under the receiver-operating characteristic (ROC) curve (AUC) of 0.957, a SEN of 0.927, and a specificity of 0.867 in 5-fold cross-validation. The prediction performance of the ensemble models is better than that of most of the base classifiers. The final ensemble model has also demonstrated good accuracy for an external validation and can be used for the early screening of CNS drugs.

Fractal analysis of CT images of tight sandstone with anisotropy and permeability prediction

For developing unconventional oil and gas resources, it is necessary to quantify the heterogeneity embodied in both pore-size distribution (PSD) and pore spatial distribution, on the basis of which the relationship between the seepage characteristics and the microstructure of heterogeneous porous media could then be clarified. In this work, micro computed tomography (μCT) and image processing technology were applied to characterise matrix pore structure in tight sandstone. Fractal theory was used to investigate the image-based heterogeneity of tight porous media. On the basis of the verification that the PSD of the tight rock is statistically self-similar at a certain scale, we investigated the relationships between porosity and fractal dimension, and between 2D and 3D fractal dimensions, respectively. The results showed that the pore structure characteristic parameters with anisotropy, i.e., the ratio of the minimum pore size to the maximum pore size λ max /λ min and a proportionality constant C KT , could be obtained by liner fitting fractal dimension and porosity. Moreover, the 2D PSD characteristics of the ‘standard cross section’ were consistent with the 3D PSD characteristics of pore network. A fractal permeability model was established by treating the connected pores in the matrix as a bundle of conical micropipes. The permeability values predicted by the proposed model agreed well with those predicted by finite volume method simulation under the condition where irreducible pores were removed. A sensitivity analysis indicated that increasing the heterogeneity of the PSD and pore spatial distribution reduced the flow capacity of the matrix, particularly for low-porosity porous media. • A method to verify the fractal self-similarity of pore size distribution in CT images. • D f-2 vs D f-3 of microstructure in heterogeneous porous media was explored. • Fractal anisotropic model for permeability prediction in heterogeneous porous media.

Construction and experimental analysis of permeability prediction model of unsaturated clay

The calculation process of the traditional test method of unsaturated clay permeability coefficient is complicated, so a prediction model of unsaturated clay permeability is established. The e-lnp curve is drawn through K0 consolidation test of unsaturated remolded clay. The linear relationship between state variables and soil saturation was found, and the influence of unsaturated clay on soil properties was obtained. According to the adsorption storage mechanism and the position of soil compactness in the coordinate system, the characteristic curve of soil moisture and the prediction model of unsaturated clay permeability were established. The permeability test of unsaturated remolded clay is carried out by using transient profile method, and the results show that the model can well describe the change of permeability of unsaturated clay.

Pore structure characterization of tight sandstones via a novel integrated method: A case study of the Sulige gas field, Ordos Basin (Northern China)

• Pore structure can be characterized by combining multiple experimental techniques. • Full size PSDs of pore body and throat can be determined by combining NMR and CMI. • Complexity and irregularity pore structure can be depicted by fractal dimension. • Pore size can be divided into three categories to evaluate each category’s contribution. • A novel permeability model considering classified pore contributions is proposed. Pore structure characterization is a very crucial work to understand rock properties. In this work, a novel integrated method was proposed to characterize the pore structure of a real tight sandstone reservoir in the Sulige gas field. This integrated method requires a comprehensive analysis of experimental results based on cast thin section (CTS), nuclear magnetic resonance (NMR), constant-rate mercury intrusion (CMI) and X-ray computed tomography (XCT) techniques. Five main pore types were firstly identified from CTS images. Bimodal distribution curve of surface relaxivity rate, mercury injection curves of pore body and throat, 3D visual image of pore reconstruction were then generated from NMR, CMI and XCT measurements, respectively. Pore size distribution (PSD) was also obtained by and compared among NMR, CMI and XCT techniques. Full size PSDs of pore body and throat were ultimately determined using a combination analysis method of NMR and CMI. In addition, fractal dimension was calculated by NMR- and CMI-deduced formulas. Moreover, pores were classified into three categories and the contribution of different category pore to rock properties was qualitatively ascertained. At the end, a novel permeability prediction model in consideration of the contributions of three category pore was established. It was found that radii of pore body and throat ranged in 0.21–42.01 μm and 0.006–3.7 μm, respectively. Sandstone pore has multifractal characteristics and its fractal dimension ranged in 1.335–2.982. Overall, sandstone pore structure of the example reservoir was characterized by multiple pore types, nano-micrometer pore scale , irregular geometric shape, non-uniform distribution, poor connection status and bad development degree of pore space . The findings of this study can help for better understanding of pore structure characteristics of tight sandstones.

Identification of Porosity and Permeability While Drilling Based on Machine Learning

The predictions of porosity and permeability from well logging data are important in oil and gas field development. Currently, many scholars use machine learning algorithms to predict reservoir properties. However, few scholars have researched the prediction of reservoir porosity and permeability while drilling. This approach requires not only a high prediction accuracy but also short model processing and calculation times as new logging data are incorporated while drilling. In this paper, four machine learning algorithms were evaluated: the one-versus-rest support vector machine (OVR SVM), one-versus-one support vector machine (OVO SVM), random forest (RF) and gradient boosting decision tree (GBDT) algorithms. First, samples of wireline logging data from the Yan969 wellblock of the Yan’an gas field were chosen for model training. To improve the accuracy and reduce the input parameter dimensions and model training time as much as possible, data correlation analysis was performed. Second, we used the grid search method to approximate ranges of reasonable parameter values and then used k-fold cross-validation to optimize the final parameters and avoid overfitting. Third, we used the four classification models to predict porosity and permeability while drilling with data from logging while drilling (LWD) logs. Finally, we indicate the best porosity and permeability prediction models to use while drilling. To ensure that the prediction accuracy is as high as possible and that the model training time is as short as possible, the OVO SVM algorithm was suggested for porosity and permeability prediction. Therefore, appropriate machine learning algorithms can be used to predict porosity and permeability while drilling.

Pore Structure Characteristics and Permeability Prediction Model in a Cretaceous Carbonate Reservoir, North Persian Gulf Basin

Due to the diversity of pore types, it is challenging to characterize the Middle East’s Cretaceous carbonate reservoir or accurately predict its petrophysical properties. In this paper, pore structure in the reservoir is first classified using a comprehensive method. Then, based on the identified pore structure types, a new permeability model with high prediction precision is established. The reservoir is dominated by 6 pore types, such as intergrain pores and moldic pores, and 6 rock types. Grainstone, algal packstone, algal wackestone, and foraminifera wackestone are porous rock types, and echinoderm wackestone and mudstone are nonporous rock types. The types of pore structure in the study area can be divided into four types. Type I has midhigh porosity and medium-high permeability due to its large throat, while type II has a fine throat type with midhigh porosity and midpermeability. Due to their isolated pores, the permeability is low in types III and IV, and out of these two, type III has better storage capacity. Movable fluid saturation calculated by the spectral coefficient method andrapexcan characterize the boundary between the connected pores and unconnected pores very well in the research area. It is not accurate enough to simply classify the pore structure by permeability and porosity. The combination of porosity, permeability,rapex, flow zone indicator, and the reservoir quality index can effectively distinguish and classify pore structure types in noncoring wells. The characteristics of each pore structure type are consistent with those of the fractal dimension, which thereby proves the effectiveness of the pore structure classification. New permeability prediction models are proposed for different pore structure types, and good prediction results have been obtained. This study is of great significance for enhancing oil recovery.