Porosity Estimation Research Articles

Robocasting of Hierarchical Porous Al2O3 Structures: A Computational and Experimental Methodology for Porosity Estimation and its Effect

Robocasting of Hierarchical Porous Al2O3 Structures: A Computational and Experimental Methodology for Porosity Estimation and its Effect

Simultaneous quantification of triple pore types in carbonate reservoirs using well logs and seismic data

Accurate characterization of carbonate pore types is of great significance for seismic reservoir characterization. However, the existing pore-type inversion methods cannot characterize the coexistence of three main pore types (vuggy pores, matrix pores, and cracks) for each modeling point because they apply a simple iterative procedure without using S-wave velocity. To overcome this problem, we develop a simultaneous triple pore-type inversion (TPTI) strategy to estimate the porosities of different pore types from the P- and S-wave velocities using a global optimization technique known as very fast simulated annealing. Considering the rock composition variation and pore geometry complexity, we first establish an easy-to-implement multipore effective medium model for carbonate reservoirs based on the Keys-Xu model and Gassmann’s equation. Then, the applicability of this model is verified by two sets of experimental acoustic measurements for synthetic and actual carbonate samples. A good comparison of model predictions with laboratory data indicates that our model can effectively capture the elastic responses of carbonate rocks with different pore shapes, which provides a theoretical basis for quantifying multiple pore types. Based on the developed model, we further develop a TPTI method. We apply it to describe pore-type distribution from well logs and seismic data in a heterogeneous carbonate reservoir from the Sichuan Basin in southwest China. Real applications suggest that our method significantly improves the accuracy of porosity estimates for different pore types, outperforming the traditional dual pore-type inversion method. This advancement holds considerable promise for the seismic characterization of pore types in ultradeep carbonate reservoirs.

Simultaneous inversion of four physical parameters of hydrate reservoir for high accuracy porosity estimation

AbstractEstimation of the porosity of a hydrate reservoir is essential for its exploration and development. However, the estimation accuracy was usually less certain in most previous studies that simply assumed that there is a linear relationship between the porosity and single‐elastic wave velocities or other rock physical parameters, thus affecting the evaluation of the reserves. In the three‐phase Biot‐type equations that are fundamental to model a hydrate‐bearing reservoir, porosity, alongside hydrate saturation, mineral constituent proportions and hydrate–grain contact factor, is non‐linearly responded by density, compressional and shear wave velocities. To improve porosity estimation, we propose to invert simultaneously four‐parameter (porosity, hydrate saturation, mineral constituent proportions and hydrate–grain contact factor) using an iteratively nonlinear interior‐point optimization algorithm to solve a nonlinear objective function that is a summation of the squared misfits between the well log and three‐phase Biot‐type equation–modelled density, compressional and shear wave velocities. A test in Mount Elbert gas hydrate research well was conducted for the case of a gas hydrate stratigraphic test well where elastic wave velocities, density, porosity and mineral composition analysis data are available. The four‐parameter inversion yielded a lower root mean square error for porosity (0.0245) across the entire well‐logging section compared to previous estimations from the linear relationship, post‐stacked and pre‐stacked seismic traces as well as the pore‐filling effective medium theory model applied to other well cases. Additionally, the other three parameters demonstrated good agreement with well logs. Inversion tests conducted at three additional hydrate sites also produced accurate results. Consequently, the new method surpasses previous approaches in porosity estimation accuracy.

Fault zone architecture and hydraulic properties characterization using earth tide analysis

Fault zone architecture and hydraulic properties, which determine subsurface fluid flow and storage, have received long-term research attention from hydrogeologists. Current field experimental techniques and numerical simulation methods are limited by the scale and complexity of the investigations and are inadequate for deep subsurface fault zones. Estimating the internal rock properties and structures of fracture zones requires a combination of methods from many different fields. In recent years, groundwater tidal response methods have been widely used to characterize subsurface hydrogeomechanical properties because of their advantages of being in situ and low cost. In this study, tidal analysis was employed for a well within the Blue Ridge Fault Zone, and we integrated the fracture zone properties revealed by a single well. Well W-03 revealed an aquifer with an overall permeability of ∼ 10–14 m2 and hydraulic diffusivity of 0.24 m2⋅s−1. The fault damage zone had a permeability of 10–14 ∼ 10–13 m2 while the host rock had a permeability of 10–16 ∼ 10–14 m2. In addition, we found that the heterogeneity of specific storage affects the assessment of poroelasticity parameters, we offer a method to estimate these parameters without relying on specific storage, yielding porosity estimates ranging from 0.05 to 0.06, aligning with field test results. Furthermore, the estimated dip and strike of the fault zone were also consistent with the outcrop and borehole observations.

Development of a true density-based automated quality grading device for unboiled arecanut kernels

The automated sorting of arecanut kernels is a significant challenge that has not been effectively addressed thus far. Scientific grading techniques are necessary given the paradigm change toward investigating alternative uses for arecanuts in industry and the medical field. This research work emphasizes the relatively unexplored aspect of the post-harvest process; quality grading of kernels based on physical properties. It aimed to develop a novel approach for classifying unboiled (Chali) arecanut kernels cultivated in Goa, India based on their true density, using a combination of mechanical and visual techniques. The study explored the potential of true density as a quality indicator for real-time grading of the kernels. To achieve this, automated grading equipment was devised, utilizing a load cell to measure the kernel's mass and the ellipsoid approximation method to estimate its volume. A machine vision system captured the top and side images of the kernels to measure their volume. Python programs were created to enable image acquisition, processing, object detection, measurement and kernel segregation. Real-time kernel classification was accomplished by establishing serial data communication between the Python code and the Arduino board. The kernel segregation process was facilitated by servomechanism and a stepper motor. The kernels were classified into acceptable and non-acceptable categories based on a threshold value of true density. The research successfully established a method that utilizes the physical attributes of arecanut kernels as parameters for quality grading. However, the study encountered challenges with the density measurements, as the paired t-test results revealed significant differences between the kernel true density measured by the device and the true density estimated using the weighing scale-water displacement method, indicating a percentage error of 13.2%. Addressing these challenges would lead to more accurate density calculations, thereby enhancing the overall effectiveness of the kernel classification process. Furthermore, the technique allowed for the offline estimation of the kernels' porosity, which was found to be 45.3%. In future research, the integration of density and porosity measurement systems could be explored for real-time quality evaluation based on porosity, offering potential opportunities for further enhancement and optimization of the grading process. The technology could be further applied to other types of nuts and agricultural products, thereby overcoming the limitations of color-based sorting using image processing. Key words: Quality grading, True density, Machine vision, Arecanut kernel, Python

Porosity estimation using RHG approach and well log data from southern Niger Delta, Nigeria

Porosity estimation using RHG approach and well log data from southern Niger Delta, Nigeria

Moisture cycles in Jezero crater, Mars

Diurnal and annual water cycles are studied during the first year of Perseverance rover in Jezero, using observations from the Mars Environmental Dynamics Analyzer (MEDA) and column modeling. Areal values for the ground thermal inertia (TI) and albedo are first found at one site by fitting model temperatures to the observed air temperatures. Areal soil porosity and the initial water vapor volume mixing ratios (vmr) are next found via model-vmr fits to the observation-based vmr. The meteorology and physics of the modeled air and subsurface diurnal moisture cycle at the site is then discussed in detail. The process of fit to observations is finally extended to fourteen sites along the Perseverance track, resulting in estimates for areal TI, albedo and porosity at these sites, and in MEDA-based initial estimates for the annual and diurnal moisture cycles at Jezero during MY36.

Increase in porosity and permeability resolution for thin-bedded Miocene formation in Carpathian Foredeep using different clustering methods

AbstractThe accurate interpretation of well-logging data is a crucial stage in the exploration of gas- and oil-bearing reservoirs. Geological formations, such as the Miocene deposits, present many challenges related to thin layers, whose thickness is often less than the measurement resolution. This research emphasizes the potential of utilizing electrofacies in such challenging environments. The application of electrofacies not only allows for the grouping of intervals with similar physical characteristics but can also be useful for estimating porosity and permeability parameters. For this purpose, various clustering methods were tested, including the 2D indexed and probabilized self-organizing map (IPSOM) method with and without supervision. Subsequently, the usefulness of the obtained results to improve the estimation of porosity and permeability parameters with the help of artificial neural networks was verified. As a result of the conducted analyses, significantly better results were obtained compared to classical petrophysical interpretation. The calculated porosity and permeability parameters were characterized by much greater variability and alignment with laboratory measurements on porosity and permeability. The best results were obtained for the IPSOM method, but the other methods did not differ significantly. In conclusion, the studies have shown a positive result of applying clustering methods, including the IPSOM method, to improve the estimation of permeability and porosity parameters in complicated, thinly-layered formations.

Porosity estimation based on the shear modulus inversion of seismic shear wave

Reliable estimation of subsurface porosity is necessary for hydrocarbon reservoir characterization and fluid identification. Porosity estimations from seismic data can provide the lateral distribution of subsurface porosity, but the results may be highly nonunique because subsurface elastic properties (such as velocity and density) can be affected by porosity and pore fluids. Because shear modulus is insensitive to pore fluid content, it can be effectively used to estimate porosity. We develop a novel porosity estimation method using the reflected SH wave (SH-SH wave), whose propagation characteristics depend mainly on shear modulus and S-wave velocity. We derive a new analytical expression for the SH-SH-wave reflection coefficient, which acts as a function of shear modulus and S-wave velocity in its natural logarithm form. This new approximation has high accuracy, and both coefficients corresponding to shear modulus and S-wave velocity are “model-parameter independent”; thus, there is no need for prior estimation of any model parameter during inversion. Numerical analysis indicates that shear modulus inverted from the SH-SH wave has lower uncertainty, the problem is better conditioned, and the method requires data with fewer incidence angles than algorithms that invert for the shear modulus from the PP wave. Furthermore, the highly correlated rock-physics relationships between porosity and shear modulus facilitate accurate porosity estimation. A field data application indicates that high-resolution porosity of fine structures can be reliably recovered using our method.

Estimation of Porosity for Sedimentary Rocks and Volcanic Rocks from Sonic Log Data in the Futagawa Fault Drilling

Estimation of Porosity for Sedimentary Rocks and Volcanic Rocks from Sonic Log Data in the Futagawa Fault Drilling

Improved porosity estimation in complex carbonate reservoirs using hybrid CRNN deep learning model

Improved porosity estimation in complex carbonate reservoirs using hybrid CRNN deep learning model

Robust deep learning estimation of cortical bone porosity from MR T1-weighted images for individualized transcranial focused ultrasound planning.

Transcranial focused ultrasound (tFUS) is an emerging neuromodulation approach that has been demonstrated in animals but is difficult to translate to humans because of acoustic attenuation and scattering in the skull. Optimal dose delivery requires subject-specific skull porosity estimates which has traditionally been done using CT. We propose a deep learning (DL) estimation of skull porosity from T1-weighted MRI images which removes the need for radiation-inducing CT scans. We evaluate the impact of different DL approaches, including network architecture, input size and dimensionality, multichannel inputs, data augmentation, and loss functions. We also propose back-propagation in the mask (BIM), a method whereby only voxels inside the skull mask contribute to training. We evaluate the robustness of the best model to input image noise and MRI acquisition parameters and propagate porosity estimation errors in thousands of beam propagation scenarios. Our best performing model is a cGAN with a ResNet-9 generator with 3D 64×64×64 inputs trained with L1 and L2 losses. The model achieved a mean absolute error of 6.9% in the test set, compared to 9.5% with the pseudo-CT of Izquierdo et al. (38% improvement) and 9.4% with the generic pixel-to-pixel image translation cGAN pix2pix (36% improvement). Acoustic dose distributions in the thalamus were more accurate with our approach than with the pseudo-CT approach of both Burgos et al. and Izquierdo et al, resulting in near-optimal treatment planning and dose estimation at all frequencies compared to CT (reference). Our DL approach porosity estimates with ~7% error, is robust to input image noise and MRI acquisition parameters (sequence, coils, field strength) and yields near-optimal treatment planning and dose estimates for both central (thalamus) and lateral brain targets (amygdala) in the 200-1000 kHz frequency range.

Real-time Bayesian inversion in resin transfer moulding using neural surrogates

In Resin Transfer Moulding (RTM), local variations in reinforcement properties (porosity and permeability) and the formation of gaps along the reinforcement edges result in non-uniform resin flow patterns, which may cause defects in the produced composite component. The ensemble Kalman inversion (EKI) algorithm has previously been used to invert in-process data to estimate local reinforcement properties. However, implementation of this algorithm in some applications is limited by the requirement to run thousands of computationally expensive resin flow simulations. In this study, a machine learning approach is used to train a surrogate model which can emulate resin flow simulations near-instantaneously. A partition of the flow domain into a low-dimensional representation enables an artificial neural network (ANN) surrogate to make accurate predictions, with a simple architecture. When the ANN is integrated within the EKI algorithm, estimates for local reinforcement permeability and porosity can be achieved in real time, as was verified by virtual and lab experiments. Since EKI utilises the Bayesian framework, estimates are given within confidence intervals and statements can be made on-line regarding the probability of defects within sections of the reinforcement. The proposed framework has shown good predictive capabilities for the set of laboratory experiments and estimates for reinforcement properties were always computed within 1 s.

Consideration of the effect of nanoscale porosity on mass transport phenomena in PECVD coatings

Here we show a novel approach to characterize the gas transfer behavior of silicon-oxide (SiO x ) coatings and explain the underlying dynamics. For this, we investigate the coating on a nm-scale both by measurement and simulation. Positron annihilation spectroscopy (PAS) and quantum mechanical electronic structure-based molecular dynamics simulations are combined to characterize the porous landscape of SiO x coatings. This approach analyses the influence of micropores smaller than 2 nm in diameter on gas permeation which are difficult to study with conventional methods. We lay out the main pore diameter ranges and their associated porosity estimates. An influence of layer growth on pore size and porosity was found, with an increased energy input during layer deposition leading to smaller pore sizes and a reduced porosity. The molecular dynamics simulations quantify the self-diffusion of oxygen and water vapor through those PAS deducted micropore ranges for hydrophilic and hydrophobic systems. The theoretical pore size ranges are fitting to our PAS results and complete them by giving diffusion coefficients. This approach enables detailed analysis of pore morphology on mass transport through thin film coatings and characterization of their barrier or membrane performance. This is a crucial prerequisite for the development of an exhaustive model of pore dominated mass transports in PECVD coatings.

Tridimensional porosity modeling through fuzzy logic and geostatistics

The three-dimensional porosity simulation plays a pivotal role in the exploration and prospecting of oil fields. In this study, we introduce a hybrid methodology for subsismic resolution porosity estimation, combining algorithms based on fuzzy logic and geostatistics. We demonstrate how it is possible to use simulated facies association surfaces and structural surfaces as input parameters for the fuzzy logic algorithm to derive porosity trends, subsequently refined using simple kriging with magnetic resonance log obtained from wells. The selected simulation area is the Sapinhoá field, located in the Santos Basin, Brazil, covering the age interval from 123 Ma to 113 Ma. To statistically validate the results, we employed the Pearson correlation coefficient, along with graphs comparing the measured and simulated porosity in the wells. The obtained results showed satisfactory levels of accuracy in the evaluated wells. Moreover, in areas lacking well data, the simulated porosity volumes aligned with the trends modeled using fuzzy logic.

Enhancing seismic porosity estimation through 3D sequence-to-sequence deep learning with data augmentation, spatial constraints, and geologic constraints

Estimating porosity from seismic data is critical for studying underground rock properties, assessing energy reserves, and subsequent reservoir exploration and development. For reservoirs with strong heterogeneity, the endeavor to accurately and stably characterize spatial variations in porosity often encounters considerable challenges due to the rapid lateral changes in formations. In view of this, establishing a robust mapping relationship from seismic data to reservoir properties in 3D space is important in addressing this challenge. We transform the conventional 1D sequence-to-point (STP) prediction paradigm into a 3D sequence-to-sequence (STS) prediction paradigm to enable machine learning to extract 3D seismic data features. The 3D STS prediction presents valuable potential for enhancing the geologic continuity and vertical characterization ability of porosity compared to STP. Building upon the 3D STS prediction model, three strategies from different perspectives are introduced to further enhance the performance of seismic porosity estimation. First, we apply a translation-based data augmentation (DA) strategy to mitigate the problem of sparsely labeled data. Second, we develop spatial constraints (SCs) considering absolute coordinates and relative time to boost the spatial delineation of porosity. Third, to incorporate geologic insights into machine learning, we impose geologic constraints (GCs) by measuring the data distribution similarity between around-the-well predictions and well labels. Compared with DA strategies, incorporating SCs and GCs to STS yields more substantial improvements, which illustrates the importance of prior knowledge for physical parameter inversion. Finally, the combined application of these three strategies and the 3D STS method gives better generalization performance and geologic plausibility in the porosity prediction for investigated carbonate reservoirs, outperforming other methods and decreasing error by an average of 8% across 48 wells compared to STP.

Supplementary cementitious materials-based concrete porosity estimation using modeling approaches: A comparative study of GEP and MEP

Abstract Using supplementary cementitious materials in concrete production makes it eco-friendly by decreasing cement usage and the corresponding CO2 emissions. One key measure of concrete’s durability performance is its porosity. An empirical prediction of the porosity of high-performance concrete with added cementitious elements is the goal of this work, which employs machine learning approaches. Binder, water/cement ratio, slag, aggregate content, superplasticizer (SP), fly ash, and curing conditions were considered as inputs in the database. The aim of this study is to create ML models that could evaluate concrete porosity. Gene expression programming (GEP) and multi-expression programming (MEP) were used to develop these models. Statistical tests, Taylor’s diagram, R 2 values, and the difference between experimental and predicted readings were the metrics used to evaluate the models. With R 2 = 0.971, mean absolute error (MAE) = 0.348%, root mean square error (RMSE) = 0.460%, and Nash–Sutcliffe efficiency (NSE) = 0.971, the MEP provided a slightly better-fitted model and improved prediction performance when contrasted with the GEP, which had R 2 = 0.925, MAE = 0.591%, RMSE = 0.745%, and NSE = 0.923. Binder, water/binder ratio, curing conditions, and aggregate content had a direct (positive) relationship with the porosity of concrete, while SP, fly ash, and slag had an indirect (negative) association, according to the SHapley Additive exPlanations study.

A statistical model for estimating porosity based on various parameters of flow through porous media

ABSTRACT Flow through porous media is a process with a momentous impact on the environment. Hydraulic conductivity, which depends on the particle size, shape, and porosity of the media, has applications in different fields of science, e.g., groundwater, seepage, and soil stability. Precise estimation of porosity leads to accurate computation of the hydraulic conductivity of porous media. The present study investigates the effect of particle size on the porosity of porous media, i.e., crushed quartzite, river gravel, marble chips, and sand. The influence of the wall effect on packing and porosity of porous media based on the diameter ratio, i.e., the ratio of permeameter-to-particle size (Dp/dg) has also been investigated. The hydraulic conductivity and the porosity of porous media are observed to increase with an increase in the particle size. The study postulates a dependable relation between hydraulic conductivity and porosity. The experimentally obtained and the model-generated porosity values for crushed quartzite, river gravel, marble chips, and sand range between 0.38–0.42 and 0.37–0.43, respectively. Experimental data have been used to derive a statistical model based on the diameter ratio (5.08 ≤ (Dp/dg) ≤ 254) for estimating the porosity of media. The validation of the statistical model using experimental data establishes the efficacy of the developed model.

Clay soil porosity estimation using seismic P- and S-wave velocities along Isfahan Metro Line 2

Clay soil porosity estimation using seismic P- and S-wave velocities along Isfahan Metro Line 2

Respon Tekanan Transient Pada Reservoir Gas Multilayer Dengan Hydraulic Fracturing

Transient pressure analysis is designed to provide a quantitative analysis of reservoir properties. The data from the test results are collected to support information on a reservoir which is then used to become a predictive model and update the geological model. Based on the type, Pressure Transient is divided into pressure Build up and Pressure Drawdown. In testing, Pressure Transient analysis can describe the characteristics of the reservoir properties or the formation's ability to produce fluid. This test has advantages compared to other techniques in determining reservoir characteristics, because the transient pressure test covers a larger area so that it allows estimation of porosity, reservoir permeability, average pressure, skin, fracture length, reservoir heterogeneity, drainage area, shape, and even distance. can reach up to the boundary or flow discontinuities.