Permeability Prediction Model Research Articles

Optimization and Application of XGBoost Logging Prediction Model for Porosity and Permeability Based on K-means Method

The prediction and distribution of reservoir porosity and permeability are of paramount importance for the exploration and development of regional oil and gas resources. In order to optimize the prediction methods of porosity and permeability and better guide gas field development, it is necessary to identify the most effective approaches. Therefore, based on the extreme gradient boosting (XGBoost) algorithm, laboratory test data of the porosity and permeability of cores from the southern margin of the Ordos Basin were selected as the target labels, conventional logging curves were used as the input feature variables, and the mean absolute error (MAE) and the coefficient of determination (R2) were used as the evaluation indicators. Following the selection of the optimal feature variables and optimization of the hyper-parameters, an XGBoost porosity and permeability prediction model was established. Subsequently, the innovative application of homogeneous clustering (K-means) data preprocessing was applied to enhance the XGBoost model’s performance. The results show that logarithmically preprocessed (LOG(PERM)) target labels enhanced the performance of the XGBoost permeability prediction model, with an increase of 0.26 in its test set R2. Furthermore, the application of K-means improved the performance of the XGBoost prediction model, with an increase of 0.15 in the R2 of the model and a decrease of 0.017 in the MAE. Finally, the POR_0/POR_1 grouped porosity model was selected as the final predictive model for porosity in the study area, and the Arctan(PERM)_0/Arctan(PER0M)_1 grouped model was selected as the final predictive model for permeability, which has better prediction accuracy than logging curves. The combination of K-means and the XGBoost modeling method provides a new approach and reference for the efficient and relatively accurate evaluation of porosity and permeability in the study area.

Prediction Model of Coal Gas Permeability Based on Improved DBO Optimized BP Neural Network.

Accurate measurement of coal gas permeability helps prevent coal gas safety accidents effectively. To predict permeability more accurately, we propose the IDBO-BPNN coal body gas permeability prediction model. This model combines the Improved Dung Beetle algorithm (IDBO) with the BP neural network (BPNN). First, the Sine chaotic mapping, Osprey optimization algorithm, and adaptive T-distribution dynamic selection strategy are integrated to enhance the DBO algorithm and improve its global search capability. Then, IDBO is utilized to optimize the weights and thresholds in BPNN to enhance its prediction accuracy and mitigate the risk of overfitting to some extent. Secondly, based on the influencing factors of gas permeability, effective stress, gas pressure, temperature, and compressive strength, they are chosen as the coupling indicators. The SPSS 27 software is used to analyze the correlation among the indicators using the Pearson correlation coefficient matrix. Additionally, the Kernel Principal Component Analysis (KPCA) is employed to extract the original data. Then, the original data is divided into principal component data for the model input. The prediction results of the IDBO-BPNN model are compared with those of the PSO-BPNN, PSO-LSSVM, PSO-SVM, MPA-BPNN, WOA-SVM, BES-SVM, and DPO-BPNN models. This comparison assesses the capability of KPCA to enhance the accuracy of model predictions and the performance of the IDBO-BPNN model. Finally, the IDBO-BPNN model is tested using data from a coal mine in Shanxi. The results indicate that the predicted outcome closely aligns with the actual value, confirming the reliability and stability of the model. Therefore, the IDBO-BPNN model is better suited for predicting coal gas permeability in academic research writing.

Prediction of transverse permeability in representative volume elements with closely arranged fibers through the application of delaunay-triangulation and electrical-circuit analogy

The distribution of fibers in composite manufacturing significantly affects fiber bundle permeability, a crucial factor for resin impregnation and void formation, which determines final product quality. However, as the industry increasingly seeks composite materials characterized by high fiber volume fractions, the existing numerical analysis methodologies employed for predicting permeability require a considerable computational cost. In this study, the permeability prediction model was developed using a fluid domain simplification technique based on Delaunay triangulation. The inter-fiber flow was approximated using a lubrication model, and continuity was ensured using a circuit analogy. This model can explain stochastic permeability characteristics according to fiber distribution and accurately predicts the permeability regardless of the distance between fibers. Additionally, compared to existing computational fluid dynamics simulations, it achieved an incredible accuracy of 97.74% and reduced computational costs by an average of 1,700 times.

Prediction of permeability in a tight sandstone reservoir using a gated network stacking model driven by data and physical models

Introduction: Permeability is one of the most important parameters for reservoir evaluation. It is commonly measured in laboratories using underground core samples. However, it cannot describe the entire reservoir because of the limited number of cores. Therefore, petrophysicists use well logs to establish empirical equations to estimate permeability. This method has been widely used in conventional sandstone reservoirs, but it is not applicable to tight sandstone reservoirs with low porosity, extremely low permeability, and complex pore structures.Methods: Machine learning models can identify potential relationships between input features and sample labels, making them a good choice for establishing permeability prediction models. A stacking model is an ensemble learning method that aims to train a meta-learner to learn an optimal combination of expert models. However, the meta-learner does not evaluate or control the experts, making it difficult to interpret the contribution of each model. In this study, we design a gate network stacking (GNS) model, which is an algorithm that combines data and model-driven methods. First, an input log combination is selected for each expert model to ensure the best performance of the expert model and selfoptimization of the hyperparameters. Petrophysical constraints are then added to the inputs of the expert model and meta-learner, and weights are dynamically assigned to the output of the expert model. Finally, the overall performance of the model is evaluated iteratively to enhance its interpretability and robustness.Results and discussion: The GNS model is then used to predict the permeability of a tight sandstone reservoir in the Jurassic Ahe Formation in the Tarim Basin. The case study shows that the permeability predicted by the GNS model is more accurate than that of other ensemble models. This study provides a new approach for predicting the parameters of tight sandstone reservoirs.

A New Model for Predicting Permeability of Chang 7 Tight Sandstone Based on Fractal Characteristics from High-Pressure Mercury Injection

Accurately predicting permeability is important to elucidate the fluid mobility and development potential of tight reservoirs. However, for tight sandstones with the same porosity, permeability can change by nearly three orders of magnitude, which greatly increases the difficulty of permeability prediction. In this paper, we performed casting thin section, scanning electron microscopy and high-pressure mercury injection experiments to analyze the influence of pore structure parameters and fractal dimensions on the permeability of Chang 7 tight sandstones. Furthermore, the key parameters affecting the permeability were optimized, and a new permeability prediction model was established. The results show that the pore throat structure of Chang 7 tight sandstone exhibits three-stage fractal characteristics. Thus, the pore throat structure was divided into large pore throat, medium pore throat and small pore throat. The large pore throat reflects the microfracture system, whose fractal dimension was distributed above 2.99, indicating that the heterogeneity of the large pore throat was the strongest. The medium pore throat is dominated by the conventional pore throat system, and its fractal dimension ranged from 2.378 to 2.997. Small pore throats are mainly composed of the tree-shaped pore throat system, and its fractal dimension varied from 2.652 to 2.870. The medium pore throat volume and its fractal dimension were key factors affecting the permeability of Chang 7 tight sandstones. A new permeability prediction model was established based on the medium pore throat volume and its fractal dimension. Compared to other models, the prediction results of the new model are the best according to the analysis of root mean square value, average absolute percentage error and correlation coefficient. These results indicate that the permeability of tight sandstones can be accurately predicted using mesopore throat volume and fractal dimension.

A new nuclear magnetic resonance-based permeability model based on two pore structure characterization methods for complex pore structure rocks: Permeability assessment in Nanpu Sag, China

The nuclear magnetic resonance (NMR) estimate of permeability is a fundamental method that has numerous applications in reservoir engineering and petrophysics. To improve the accuracy of the NMR-based permeability model, many variables are introduced into NMR-based permeability prediction models due to geometric complexity and pore structure heterogeneity. In this paper, two pore structure characterization methods are investigated based on the Kozeny-Carman model and equivalent component model. Furthermore, an NMR-based permeability model accounting for the effect of pore structure is developed based on the analysis of the relationship between two pore structure parameters, and it is applied to practically predict permeability. Results indicate that the new model-calculated permeability has good agreement with experimental data; moreover, the adaptability of the new NMR-based permeability prediction model is highly improved through reducing undetermined variables, and key parameters can be measured directly using NMR. The new model provides a valuable scientific resource and assists in the evaluation of hydrocarbon-bearing reservoirs with complex pore structure, such as tight sandstone, shale, and carbonate rock.

Improved predictions of permeability properties in cement-based materials: A comparative study of pore size distribution-based models

Permeability is a fundamental physical property of a cement-based material and is closely related to its pore structure. This study developed an improved model for permeability prediction of cementitious materials with a function of the pore size distribution (PSD), in which PSD is simulated from single and compound lognormal distribution functions, respectively. The efficiency of the improved model is demonstrated by calculating permeability for mortars and pastes available in the literature. It is found that, first, for cementitious materials with unimodal PSD, the single lognormal distribution function is adequate to describe the pore structures. In contrast, for cementing mixtures presenting multimodal PSD, the compounded lognormal distribution function is highly recommended to cover a wide range of pore components. Second, the permeability model based on the compound PSD can predict well permeability to gas intrusion, whereas the single PSD-based model is applicable for calculating permeability via water intrusion. Furthermore, slippage effect and water saturation degree are introduced in the PSD-based model. Results prove that the corrected gas permeability model from the compound PSD performs well in estimating the gas permeability in unsaturated states. Finally, the compound PSD-based model is extended by considering varying porosities and relative humidities, which can be used to quantitively describe permeability when cementitious materials undergoing pore structure degradation induced by durability issues.

A pore network-based multiscale coupled model for rapid permeability prediction of tight sandstone gas

Tight sandstone has multiscale pore structures. Gas transport in tight sandstones involves several length scales and the complicated physics. Gas transport characteristics in tight sandstone can be represented by apparent gas permeability. Microstructure-based accurate and efficient calculation of gas permeability is challenging. By combining CT and SEM images along with statistical analysis, we present a novel pore network-based multiscale coupled model (MCPNM) to rapidly predict the apparent gas permeability. CT images are adopted to extract the large-scale pore network (LPNM) and the clay component, and SEM image is used to get the properties of small-scale pores. Upscaled model (UM) of small-scale pores is built via statistical analysis and then assigned to the clay domains. The LPNM and UM are coupled as MCPNM by the cross-scale connection structure with variable diameter. The pore spaces at several length scales and the flow characteristics in them are included in the MCPNM. We validate the MCPNM by comparing the calculated apparent gas permeability to the results of available multiscale pore network models and the experimental data. Compared with the available multiscale pore network models, MCPNM solves the accuracy/efficiency trade-off of tight gas permeability prediction. The effects of pressure, temperature, and gas type on gas permeability are studied. The MCPNM simplifies the permeability calculation process and can accurately and rapidly predict tight sandstone gas permeability.

Estimation of 3D Permeability from Pore Network Models Constructed Using 2D Thin-Section Images in Sandstone Reservoirs

Using thin-section images to estimate core permeability is an economical and less time-consuming method for reservoir evaluation, which is a goal that many petroleum developers aspire to achieve. Although three-dimensional (3D) pore volumes have been successfully applied to train permeability models, it is very expensive to carry out. In this regard, deriving permeability from two-dimensional (2D) images presents a novel approach in which data are fitted directly on the basis of pore-throat characteristics extracted from more cost-effective thin sections. This work proposes a Fluid–MLP workflow for estimating 3D permeability models. We employed DIA technology combined with artificial lithology and pore classification to calculate up to 110 characteristic parameters of the pore-throat structure on the basis of 2D rock cast thin sections. The MLP network was adopted to train the permeability prediction model, utilizing these 110 parameters as input. However, the accuracy of the conventional MLP network only reached 90%. We propose data preprocessing using fluid flow simulations to improve the training accuracy of the MLP network. The fluid flow simulations involve generating a pore network model based on the 2D pore size distribution, followed by employing the lattice Boltzmann method to estimate permeability. Subsequently, six key structural parameters, including permeability calculated by LBM, pore type, lithology, two-dimensional porosity, average pore–throat ratio, and average throat diameter, were fed into the MLP network for training to form a new Fluid–MLP workflow. Comparing the results predicted using this new Fluid–MLP workflow with those of the original MLP network, we found that the Fluid–MLP network exhibited superior predictive performance.

Ensemble Learning Based Sustainable Approach to Carbonate Reservoirs Permeability Prediction

Permeability is a crucial property that can be used to indicate whether a material can hold fluids or not. Predicting the permeability of carbonate reservoirs is always a challenging and expensive task while using traditional techniques. Traditional methods often demand a significant amount of time, resources, and manpower, which are sometimes beyond the limitations of under developing countries. However, predicting permeability with precision is crucial to characterize hydrocarbon deposits and explore oil and gas successfully. To contribute to this regard, the current study offers some permeability prediction models centered around ensemble machine learning techniques, e.g., the gradient boost (GB), random forest (RF), and a few others. In this regard, the prediction accuracy of these schemes has significantly been enhanced using feature selection and ensemble techniques. Importantly, the authors utilized actual industrial datasets in this study while evaluating the proposed models. These datasets were gathered from five different oil wells (OWL) in the Middle Eastern region when a petroleum exploration campaign was conducted. After carrying out exhaustive simulations on these datasets using ensemble learning schemes, with proper tuning of the hyperparameters, the resultant models achieved very promising results. Among the numerous tested models, the GB- and RF-based algorithms offered relatively better performance in terms of root means square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2) while predicting permeability of the carbonate reservoirs. The study can potentially be helpful for the oil and gas industry in terms of permeability prediction in carbonate reservoirs.

Resampling combined with stacking learning for prediction of blood-brain barrier permeability of compounds

It is a significant challenge to improve the blood-brain barrier (BBB) permeability of central nervous system (CNS) drugs in their development. Compared with traditional pharmacokinetic property tests, machine learning techniques have been proven to effectively and cost-effectively predict the BBB permeability of CNS drugs. In this study, we introduce a high-performance BBB permeability prediction model named balanced-stacking-learning based BBB permeability predictor(BSL-B3PP). Firstly, we screen out the feature set that has a strong influence on BBB permeability from the perspective of medicinal chemistry background and machine learning respectively, and summarize the BBB positive(BBB+) quantification intervals. Then, a combination of resampling algorithms and stacking learning(SL) algorithm is used for predicting the BBB permeability of CNS drugs. The BSL-B3PP model is constructed based on a large-scale BBB database (B3DB). Experimental validation shows an area under curve (AUC) of 97.8% and a Matthews correlation coefficient (MCC) of 85.5%. This model demonstrates promising BBB permeability prediction capability, particularly for drugs that cannot penetrate the BBB, which helps reduce CNS drug development costs and accelerate the CNS drug development process.

Fluid seepage mechanism and permeability prediction model of multi-seam interbed coal measures

The successful development of coal measure gas (CMG) in the Surat Basin in Australia shows that the geological conditions of thin-interbed CMG development may be more favorable, but the above understanding is still in the geological law summary level. This study combines numerical simulation and physical simulation experiments to study the core geological problems of CMG in the geological control of the permeability of a multi-thin coal seam composite reservoir (MTCCR). The results show that in the initial stage of stress loading, the permeability advantage of the coal-mudstone/sandstone composite reservoir is reflected in the bedding permeability. Based on this phenomenon, the concept of “cross-layer seepage effect of fluid in MTCCR” is proposed for the first time, which is analyzed as the result of the permeation effect of adjacent rock fluids in the composite reservoir under the action of pressure difference. The numerical simulation shows that the permeability increase of the MTCCR depends on the increase in the fracture, and the coupling effect of the fracture and pore changes the fluid pressure field and seepage mode around the fracture. From the experimental perspective, the above understanding confirms the results of previous conclusions on the geological law of high permeability of MTCCR, and explains the geological phenomenon of high permeability of MTCCR from the seepage mechanism. Based on numerical and physical simulation experiment, a sedimentation-tectonics controlled permeability prediction model was constructed. The seepage mechanism and the permeability prediction model established of MTCCR in this study can provide theoretical support and quantitative basis for the selection of high permeability zones and evaluation of high permeability sections in the exploration and development process of thin interbed CMG.

Influence mechanism and optimization of the plugging capacity of temporary plugging zones with different particle sizes based on pore structure

Reducing the temporary plugging zone permeability is the key to the success of temporary plugging and diverting fracturing. The pore structure of the plugging zone determines the permeability of the temporary plugging zone. This paper introduces a new method based on micro-computed tomography (micro-CT) to quantitatively characterize the pore structure of temporary plugging zones with different particle size. The permeability of temporary plugging zone was measured, and seepage simulation was carried out based on pore network model (PNM). The particle compound method and formula were proposed to reduce the permeability of the temporary plugging zone. Finally, a permeability prediction model of temporary plugging zone based on pore structure was established. The results show that the porosity and tortuosity of the temporary plugging zone are independent of particle size under natural accumulation, and the pore radius decreases with the decrease in particle size. After compaction, the pore structure of the temporary plugging zone changes more obviously with smaller particle size. The seepage simulation shows that the number of flow channels in the temporary plugging zone formed by 2–3 mm particles is only 23.5% of the 0.6–1 mm flow channels. However, several obviously dominant channels are found in the 2–3 mm particles, resulting in a higher permeability. The 0.6–1 mm particles are used to fill the pores formed by 2–3 mm particles so that the dominant channel is divided into several small flow channels to significantly decrease the permeability of the 2–3 mm particles. When 2–3 mm and 0.6–1 mm particles are mixed according to the established particle size recombination formula, the pore radius and permeability of the temporary plugging zone decreased by 42.3% and 53.2% compared with 2–3 mm particles. The permeability prediction model can predict the permeability of temporary plugging zone well, and the average error is 9.145%. The sample preparation methods, pore structure testing and permeability prediction model were established in this work, providing a new method for the selection and design of temporary plugging agents.

Permeability prediction of pervious concrete based on mix proportions and pore characteristics

Permeability is the principal function of pervious concrete, and establishing a permeability prediction model is a prerequisite for designing and optimizing permeability. However, most of the current permeability prediction models depend on porosity or pore characteristics, which are difficult to guide the design of pervious concrete. Therefore, this study investigated the permeability of pervious concrete with different cement-aggregate ratios and gradations of aggregates, and developed prediction models for permeability coefficients based on the mix proportion parameters and pore characteristics. The results show that the two-dimensional porosity obtained from image analysis is slightly larger than the connected porosity obtained from the volumetric method. The three pore characteristics obtained from image analysis include the two-dimensional porosity, the pore number, and the equivalent pore size, all of which correlate significantly with the cement-aggregate ratio. The permeability coefficients measured by the constant and falling head methods show significant correlations with the cement-aggregate ratio and the pore characteristics and insignificant correlations with the equivalent aggregate size. Binary and ternary permeability coefficients prediction models based on the cement-aggregate ratio and the equivalent aggregate size, as well as on the two-dimensional porosity, the pore number, and the equivalent pore size, are developed, respectively. The predicted permeability coefficients are in good agreement with the experimental results, indicating that the permeability of pervious concrete can be designed and predicted according to the prediction models.

New Model for Absolute Permeability Prediction in Coal Samples: Application of Modified Purcell Model to Mercury Injection Pressure and Nuclear Magnetic Resonance Data.

The permeability of rocks is a critical parameter in many subsurface geological applications, and pore properties measured on rock samples (including rock fragments) can be used to estimate rock permeability. A major use of MIP and NMR data is to assess the pore properties of a rock in order to estimate the permeability based on empirical equations. Although sandstones have been extensively studied, permeability in coals has received less attention. Consequently, in order to obtain reliable predictions for coal permeability, a comprehensive study of different permeability models was performed on coal samples having a range of permeabilities from 0.003 to 1.26 mD. The model results showed that the seepage pores in coals account for the bulk of the permeability, while the contribution of adsorption pores to permeability is negligible. The models that only consider a single pore size point on the mercury curve, such as the Pittman and Swanson model, or those that use the entire pore size distribution, like the Purcell and SDR model, are inadequate for predicting permeability in coals. This study modifies the Purcell model to determine permeability from the seepage pores of coal, resulting in the enhancement of the predictive capability, with an increased R2 and reduction in the average absolute error by approximately 50% compared to the Purcell model. To apply the modified Purcell model to NMR data, a new model was developed that provides a high degree of predictive capability (∼0.1 mD). This new model can be used for cuttings, which could lead to a new method for field permeability estimation.

Use of hydraulic radius to estimate the permeability of coarse-grained materials using a new geodatabase

This paper reviews commonly used parameters and prediction models for assessing the permeability of granular soils. Following a review of published models for prediction of soil permeability, a dimensional homogenous transformation model for a-priori estimation of soil permeability was calibrated using a large database (CG/KSAT/7/1278) comprising permeability data for a wide range of granular soils sourced from over 50 publications. The new transformation model requires knowledge of the void ratio and gradation of the material to make estimates of the soil permeability. The prediction accuracy of the calibrated model was then assessed alongside that of other empirical and semi-empirical models also calibrated using CG/KSAT/7/1278. The potential influences of void ratio, key gradation parameters and permeability test type on the prediction accuracy of the proposed model are also examined. The paper shows that while the fitted constants in the proposed transformation model are affected to varying extents by the aforementioned parameters, it does offer reasonable predictions of permeability with only knowledge of the void ratio and material gradation required.

Permeability modeling of Upper Cretaceous Bahariya Formation rocks, Abu Sennan field, Western Desert, Egypt

The present work aims to describe in detail the petrophysical properties of the Bahariya Formation (Abu Sennan field) through processing the core sample data and finding out the possible relations between permeability and other reservoir parameters such as porosity, specific internal surface, formation factor, and nuclear magnetic resonance to obtain reliable permeability prediction model of the Upper Cretaceous formation. The core samples have a length range of 2–4 cm with a diameter of 2.54 cm. Many petrophysical reservoir parameters were measured, such as permeability, porosity, density, formation factor, and special measurements such as spectral induced polarization, nuclear magnetic resonance, and specific internal surface. The samples cover a wide range of porosity (11.82–23.68 %) and permeability (0.06–133.65 mD). The porosity-permeability relationship indicates the increase of the permeability with porosity increase for the studied samples. The reservoir quality index ranges from 0.02 to 0.78 μm. The flow zone indicator ranges from 0.09 to 3.39 μm where pore throat radius (R35) ranges from 0.10 to 7.40 μm. The reservoir quality index (RQI) and flow zone indicator (FZI) cross-plot show a better understanding of the formation evaluation. The hydraulic flow units are (0.09 μm < FZI ≤ 3.39 μm) and RQI < (1 μm). NMR longitudinal relaxation time T1 range is 1.21–175 ms, NMR transversal relaxation time T2(100) range is 0.70–91.77 ms, and T2(600) range is 0.74–75.86 ms. Models that incorporate porosity and NMR relaxation times will be more accurate and suitable for permeability prediction. Using multiple regression to determine the exponents of the power-law equation and replacing the porosity with the formation factor can improve the permeability prediction for the samples under investigation. The utilizing of NMR in predicting the permeability through a robust model considers an additional value for the Upper Cretaceous rocks in Egypt.

Prediction of gas–water relative permeability in tight rock from movable fluid distribution with nuclear magnetic resonance

The measurement of the relative permeability in tight rock is challenging due to its ultralow permeability and the time-consuming nature of the experiments. Studying the movable and unmovable fluid distribution and establishing a reliable relative permeability prediction model is an urgent problem to be solved. This paper used nuclear magnetic resonance (NMR) to investigate movable and unmovable water distribution in tight sandstone under different centrifugal forces. A new method for predicting gas–water relative permeability in tight rock is established based on movable fluid distribution using the capillary bundle model. The results show that the distribution of movable and unmovable fluids is strongly influenced by the tight rock's pore size distribution and structure. The unmovable fluid saturation increases as the tight rock's permeability and median radius decrease. The nonlinear correlation between the NMR relaxation time and the pore throat size obtained from high-pressure mercury intrusion can be used to derive the pore size of the fluid distribution in tight rocks. The ratio of the movable fluid thickness to pore throat size increases near linearly with the logarithm of the pore throat size. The proposed mathematical model for the prediction of gas-water relative permeability based on movable fluid distribution is verified by comparing with the normalized relative permeability curve measured from experiments. This new model offers an alternative method of estimating the gas–water relative permeability when measurement is unavailable due to the ultralow permeability of the core samples.

A Prediction Method of Compacted Rock Hydraulic Permeability Based on the MGEMTIP Model

The permeability of the fluid-bearing rock is an important parameter for reservoir prediction. The Kozeny-Carman (K-C) formulation based on electrical measurements effectively characterizes the permeability-resistivity relationship of rocks with a single mineral composition or high porosity. The complex pore structure and mineral composition of compacted reservoirs affect induced polarization (IP) characteristics, indirectly limiting the applicability of conventional electrical K-C models. The permeability of fluid-bearing rocks is an important parameter for reservoir prediction. The theoretical chargeability of the modified generalized effective medium theory of induced polarization (MGEMTIP) model includes the effects of various conductive minerals. Due to the disconnection assumption of the disturbed medium in the MGEMTIP, there is a significant difference between the theoretical chargeability and the measured chargeability, and the difference is a sensitive parameter of rock permeability. A semi-empirical reservoir permeability prediction model is proposed based on the MGEMTIP. Theoretically and experimentally, the prediction model based on MGEMTIP is compared with the two electrical K-C models. Under the condition that the rock does not contain low-resistivity minerals, the prediction model based on MGEMTIP is theoretically equivalent to the K-C model. The experimental results show that this prediction model is more suitable for low-porosity and low-permeability rocks containing low-resistivity minerals, and the prediction results can be effectively restricted to the same order of magnitude. From the perspective of differences between model assumptions and natural rocks, the prediction model provides a semi-empirical relationship between complex mineral IP characteristics and permeability. Combined with the geological information of the survey area, the permeability prediction model can provide a theoretical basis for reservoir permeability prediction based on electromagnetic exploration.

Research progress on the application of skin permeability prediction model in health risk assessment of emerging pollutants

越来越多的证据表明皮肤暴露是新污染物进入人体的重要途径。污染物的皮肤渗透性主要由皮肤渗透系数和渗透通量两个参数来反映。借助人体和动物模型开展的体内/体外暴露实验是当前测定新污染物透皮特征的主要手段，然而此类方法存在成本高、耗时长、差异大以及道德伦理等诸多问题，难以及时准确地反映新污染物的皮肤暴露风险。因此需发展皮肤渗透性预测数学模型，用于评估污染物的暴露风险和人体健康风险。本文针对风险评估中的皮肤渗透性预测数学模型，介绍皮肤基本结构及新污染主要透皮吸收途径，概述了现有皮肤渗透预测模型的基本原理及其应用，阐明渗透性预测模型在暴露风险和人体健康风险评估中的应用作用，并对数学模型开展新污染物皮肤暴露风险评估方面的研究进行展望。