Porosity Estimation Research Articles

Reservoir permeability prediction based artificial intelligence techniques

Predicting permeability is a cornerstone of petroleum reservoir engineering, playing a vital role in optimizing hydrocarbon recovery strategies. This paper explores the application of neural networks to predict permeability in oil reservoirs, underscoring their growing importance in addressing traditional prediction challenges. Conventional techniques often struggle with the complexities of subsurface conditions, making innovative approaches essential. Neural networks, with their ability to uncover complicated patterns within large datasets, emerge as a powerful alternative. The Quanti-Elan model was used in this study to combine several well logs for mineral volumes, porosity and water saturation estimation. This model goes beyond simply predicting lithology to provide a detailed quantification of primary minerals (e.g., calcite and dolomite) as well as secondary ones (e.g., shale and anhydrite). The results show important lithological contrast with the high-porosity layers correlating to possible reservoir areas. The richness of Quanti-Elan's interpretations goes beyond what log analysis alone can reveal. The methodology is described in-depth, discussing the approaches used to train neural networks (e.g., data processing, network architecture). A case study where output of neural network predictions of permeability in a particular oil well are compared with core measurements. The results indicate an exceptional closeness between predicted and actual values, further emphasizing the power of this approach. An extrapolated neural network model using lithology (dolomite and limestone) and porosity as input emphasizes the close match between predicted vs. observed carbonate reservoir permeability. This case study demonstrated the ability of neural networks to accurately characterize and predict permeability in complex carbonate systems. Therefore, the results confirmed that neural networks are a reliable and transformative technology tool for oil reservoirs management, which can help to make future predictive methodologies more efficient hydrocarbon recovery operations.

Seismic attribute analysis of the Lokbatan-Puta-Gushkhana field

This study presents a detailed seismic attribute – mainly Root Mean Square (RMS) amplitude and Sweetness attribute analysis, aiming to improve the understanding of subsurface structures. We apply both these seismic attributes on a specific petroleum field called Lokbatan-Puta-Gushkhana. Additionally, the study investigates the use of RMS amplitude analysis in the characterization of reservoirs, focusing on the estimation of fluid volume and porosity. The results offer useful guidance for reservoir management techniques and aid in the optimization of reservoir modeling. Seismic attributes listed above (RMS Amplitude, Sweetness) make it possible to distinguish zones by variable properties of the wave field. If we observe that the calculated seismic attributes differ significantly, then it can be assumed that the elastic properties of this area (velocity, density, etc.) have also changed. Differentiation of elastic properties and, accordingly, facies compositions allows for predicting the distribution of reservoirs. The outcomes of this study have a wide range of implications for the petroleum industry and provide a solid methodology for using RMS amplitude analysis in reservoir characterization and basin-scale exploration. The insights derived from this study can help with decision-making in the oil and gas exploration industry, enhancing resource evaluation and promote the development of sustainable energy sources. In order to study the distribution of reservoirs in the Lokbatan-Puta-Gushkhana area of the Absheron oil and gas region in the Productive Series (PS) and the underlying sediments (Diatom and Maykop), the results of the complex studies conducted according to the geophysical survey of the wells and 2D seismic data were analyzed. Additionally, we combine RMS attribute analysis with geological interpretations to improve reservoir models and drilling strategies. Our findings not only contribute to the understanding of the geological complexities of the Lokbatan-Puta-Gushkhana field but also offer practical insights for hydrocarbon exploration and production in this prolific region.

Deep learning in the advanced core sample porosity determination with XCT image

X-ray computed tomography (XCT) for core sample porosity analysis has advanced significantly, integrating machine learning with traditional image processing and introducing methods such as Weighted Assignment by CT number calculation. However, several areas still require enhancement, particularly in image preprocessing and model optimization for specific deep learning approaches. Moreover, the application of the Weighted Assignment method to water-sensitive mudstone core samples in this study revealed the inconsistency with the porosity estimation, primarily due to the overemphasis on fracture effects. To address these limitations, this study proposed the ResNet algorithm, a deep learning model developed for XCT-based porosity estimation. ResNet's architecture, incorporating residual learning with shortcut connections, enables effective training of deep networks and enhances feature extraction. The ResNet-based model was trained on XCT images with known porosity values measured by Multi-Sensor Core Logging (MSCL), effectively predicting volume fraction porosity for materials with similar lithological characteristics. Analysis of testing losses, comparison of predicted versus actual porosity values and RRMSE, demonstrates the model's superior performance than traditional methods. This novel application of ResNet to porosity estimation highlights the potential of deep learning to significantly enhance accuracy by 97.82% compared with the weighted assignment method with RRMSE.

Experimental study of porosity and permeability of stabilized earth blocks by date palm plant fibers

The physical behavior characteristics of stabilized earth blocks (SEBs) reinforced with date palm plant fibers (PFDP) are examined in this experimental investigation. Various compounds were prepared, including 0%, 5%, and 10% lime, 0%, 5%, and 10% cement, and a combination of 5% lime and 5% cement. Additionally, fibers were incorporated at rates of 0%, 0.25%, and 0.5% by weight for each of the compounds. The study of porosity and permeability was conducted using several methods, including total porosity estimation, total volume porosity in water, open porosity, water vapor permeability, chloride ion permeability, and hydraulic conductivity, over a period of 90 days. The findings indicated that the incorporation of fiber enhanced the porosity and connectivity of pores, resulting in increased permeability of the SEBs. The cement-based bricks demonstrated a greater effectiveness than lime in decreasing the porosity and permeability of the blocks. The formulation consisting of 10% cement and the blend of 5% lime with 5% cement demonstrated satisfactory outcomes regarding porosity and permeability. This indicates that these materials are suitable for use in construction applications that demand enhanced durability and resistance to both natural and chemical influences.

Direct estimation of petrophysical properties from elastic parameter: A Case Study on the Batu Raja Formation

Abstract Rock physics analysis is a method to characterize petrophysical properties of rocks based on their elastic behaviour. However, this approach requires complete information on mineral composition of the matrix, pore-filling fluids, and in-situ conditions of the reservoir. Recently, a new approach was introduced to model the rock physics of clastic sediments using only acoustic impedance (AI) and the ratio between P- and S-wave velocity (Vp/Vs ratio) for predicting fluid saturation and porosity without requiring detailed Voigt-Reuss-Hill and/or Hashin-Shtrikman bounds and Gassmann fluid substitution. In this study, we combine AI and Vp/Vs ratio to form pseudo elastic impedance attributes that based on attribute rotation scheme. We apply the approach to carbonate sediments in the Batu Raja formation. We combined AI and Vp/Vs ratio to produce SCPEI (Simple Curved Pseudo Elastic Impedance) and SPEIL (Simple Pseudo Elastic Impedance for Lithology) attribute to obtain better estimation of water saturation and porosity, respectively. This new approach provides a way of estimating the two main petrophysical properties, namely porosity and water saturation, from elastic properties commonly derived from seismic inversion.

Transformasi Gelombang Reguler Akibat Pemecah Gelombang Tiang Pancang Dua Baris Selang-Seling

Hydraulic experiments in a two-dimensional physical laboratory were conducted to evaluate the performance of pile breakwaters in reducing wave energy. The piles on the breakwater were arranged in a staggered pattern in two rows. A total of seventy-two simulation scenarios were run based on variations in the incoming wave height and period, and the spacing between the piles, using a 1:10 model scale. The data from the hydraulic tests were then processed using a spectral method that could separate the energy spectra of reflected and transmitted waves. These changes result in reflected and transmitted waves. The laboratory test data were used to estimate the values of the transmission coefficient and reflection coefficient. Both coefficients were then used to validate semi-empirical formulas for the two coefficients. The semi-empirical formulas for the two coefficients were developed based on a model for estimating the spatial porosity of pile breakwaters. The porosity estimation model takes into account the dimensions of the piles and the dimensional components of the breakwater, including the arrangement, number of rows, and spacing between the piles. The validation results of the semi-empirical formulas with the laboratory test data showed a coefficient of determination of 0.917 and a root mean square of 0.077. The staggered arrangement improves the effectiveness of the breakwater in reducing the transmission wave height. The developed semi-empirical model can be used to design the pile dimensions to achieve optimal reflected and transmitted wave heights for coastal protection. Keywords: pile breakwater, coastal protection, hydraulic experiment, physical wave simulation, wave transmission new formula

Fast mixture spatial regression: A mixture in the geographical and feature space applied to predict porosity in the post-salt

Extracting geological resources like hydrocarbon fluids requires significant investments and precise decision-making processes. To optimize the efficiency of the extraction process, researchers and industry experts have explored innovative methodologies, including the prediction of optimal drilling locations. Porosity, a key attribute of reservoir rocks, plays a crucial role in determining fluid storage capacity. Geostatistical techniques, such as kriging, have been widely used for estimating porosity by capturing spatial dependence in sampled point-referenced data. However, the reliance on geographical coordinates for determining spatial distances may present challenges in scenarios with small and widely separated samples. In this paper, we develop a mixture model that combines the covariance generated by geographical space and the covariance generated in an appropriate feature space to enhance estimation accuracy. Developed within the Bayesian framework, our approach utilizes flexible Markov Chain Monte Carlo (MCMC) methods and leverages the Nearest-Neighbor Gaussian Process (NNGP) strategy for scalability. We present a controlled empirical comparison, considering various data generation configurations, to assess the performance of the mixture model in comparison to the marginal models. Applying our models to a three-dimensional reservoir demonstrates its practical applicability and scalability. This research presents a novel approach for improved porosity estimation by integrating spatial and covariate information, offering the potential for optimizing reservoir exploration and extraction activities.

Analysis of the changes in filtration and capacity properties of the oil and gas condensate field reservoirs using X-ray computed tomography methods

Objective. To study the nature of changes in structural, capacity and filtration properties of the Chayanda oil and gas condensate field reservoir resulting from mechanical and hydrodynamic testing and to improve the quality of laboratory assessment of the reservoir properties of rocks by applying the digital approach. Materials and methods. A high-resolution X-ray micro-CT scanner ProCon X-Ray CT-MINI was used for computed tomography. Digital analysis of images was carried out in GeoDict software. Results. The results of digital analysis of changes in the pore space of hydrocarbon reservoir by computer tomography methods after the tests with fluid filtration under baric conditions are presented, including comparison of macroscopic changes in the pore space and determination of the nature of changes in reservoir porosity and pore geometry at the macro level. Porosity values were determined using digital methods and compared with laboratory data. Numerical modeling of filtration processes on the created 3D models of the rock was performed. The fact of nonuniform distribution of filtration flows was established: filtration in the rock occurs mainly through isolated alternating channels. It was determined that the change in the rock pore space occurred mainly due to deformation and expansion of the walls of the main filtration channels. Conclusions. Nonuniformity of changes in filtration-capacitance properties of rock specimens caused by mechanical and hydrodynamic tests can lead to incorrect estimation of porosity and permeability when applying traditional laboratory methods. The results of nondestructive digital studies can be advised as a supplement to laboratory studies of core material properties. Joint application of digital and traditional laboratory methods allows obtaining the most complete range of data on reservoir properties to solve problems arising during development.

Modified approach to estimate effective porosity using density and neutron logging data in conventional and unconventional reservoirs

Porosity is a critical petrophysical parameter that governs storage capacity in reservoirs. Despite the introduction of various techniques to assess pore structure, the complexity of rock components and the wide range of pore types have led to limitations in accurately evaluating porosity, particularly in clay-dominant reservoirs. Discrepancies and inconsistencies remain among different analytical calculation methods. Determining porosity using neutron and density logs is especially challenging in the presence of clay minerals and hydrocarbon saturation, particularly gas. Gas saturation reduces rock density, while in clay-dominant formations, neutron logs often indicate excessively high porosity due to the water content in clays. The impact of clay-bound water on rock porosity is still not fully accounted for. This study proposes a modified method for estimating porosity in both conventional and unconventional reservoirs, addressing the effect of clay-bound water on porosity calculations. The proposed method incorporates the rock's composition through its response observed in the neutron and density logs. Analytical equations are formulated to account for the influence of clay-bound water on these logs, and porosity is estimated. To validate the methodology, it was applied to two wells in organic shale reservoirs and one well in a conventional reservoir. The proposed porosity estimation method produced results that closely aligned with previously established methods, demonstrating consistency across all three wells with minimal deviations. This method offers broad applicability for exploration and exploitation in both conventional and unconventional reservoirs.

Conceptual advances in petrophysical inversion techniques: The synergy of machine learning and traditional inversion models

Petrophysical inversion techniques are essential for estimating reservoir properties such as porosity, permeability, and fluid saturation, which are critical for optimizing hydrocarbon recovery and reservoir management. Traditionally, deterministic and stochastic inversion methods have relied on well log data, core samples, and seismic information to generate models of subsurface properties. However, these techniques face limitations in handling large datasets, nonlinear relationships, and uncertainties in complex reservoir environments. Recent advances in machine learning (ML) offer an opportunity to enhance these traditional methods by providing a data-driven approach capable of learning patterns and refining inversion models. This review explores the potential synergy between traditional petrophysical inversion techniques and machine learning algorithms to create a new paradigm for reservoir parameter estimation and analysis. Machine learning’s ability to process large volumes of diverse data and capture nonlinear relationships complements the physics-based constraints of traditional inversion methods. By combining these two approaches, hybrid models can provide improved accuracy in predicting reservoir properties and handling data gaps or uncertainties. The review discusses specific case studies where machine learning-assisted inversion models have been successfully applied, such as the estimation of porosity and permeability, seismic inversion for lithology identification, and fluid saturation modeling. These hybrid models demonstrate enhanced predictive accuracy, faster computational efficiency, and real-time adaptability, transforming reservoir characterization and management. The integration of machine learning with traditional inversion techniques represents a major advancement in petrophysical analysis. This synergy holds the potential to revolutionize reservoir parameter estimation, making it more accurate, robust, and responsive to real-time data, ultimately improving decision-making in reservoir engineering and maximizing hydrocarbon recovery. Keywords: Petrophysical Inversion, Machine Learning, Traditional Inversion, Models.

Mercury's Crustal Porosity as Constrained by the Planet's Bombardment History

AbstractKnowing the structure of the crust is critical to understanding a planet's geologic evolution. Crustal thickness inversions rely on bulk density estimates, which are primarily affected by porosity. Due to the absence of high‐resolution gravity data, Mercury's crustal porosity has remained unknown. Here, we use a model that was calibrated to the Moon to relate Mercury's impact crater population and long‐wavelength crustal porosity in the cratered terrains. Therein, porosity is created by large impacts and then decreased as the surface ages due to pore compaction by smaller impacts and overburden pressure. Our models fit independent gravity‐derived porosity estimates in the northern regions, where data is well resolved. Porosity in the cratered terrains is found to be 9%–18% with an average and standard deviation of 13% 2%, indicating lunar‐like crustal bulk densities of 2,565 70 kg from which updated crustal thickness maps are constructed.

Integrated metaheuristic approaches for estimation of fracture porosity derived from fullbore formation micro-imager logs: Reaping the benefits of stand-alone and ensemble machine learning models

Fracture porosity is one of the most effective parameters for reservoir productivity and recovery efficiency. This study aims to predict and improve the accuracy of fracture porosity estimation through the application of advanced machine learning (ML) algorithms. A novel approach was introduced for the first time to estimate fracture porosity by reaping the benefits of petrophysical and fullbore formation micro-imager (FMI) data based on employing various stand-alone, ensemble, optimisation and multi-variable linear regression (MVLR) algorithms. This study proposes a ground-breaking two-step committee machine (CM) model. Petrophysical data containing compressional sonic-log travel time, deep resistivity, neutron porosity and bulk density (inputs), along with FMI-derived fracture porosity values (outputs), were employed. Nine stand-alone ML algorithms, including back-propagation neural network, Takagi and Sugeno fuzzy system, adaptive neuro-fuzzy inference system, decision tree, radial basis function, extreme gradient boosting, least-squares boosting, least squares support vector regression and k-nearest neighbours, were trained for initial estimation. To improve the efficacy of stand-alone algorithms, their outputs were combined in CM structures using optimisation algorithms. This integration was applied through five optimisation algorithms, including genetic algorithm, ant colony, particle swarm, covariance matrix adaptation evolution strategy (CMA-ES) and Coyote optimisation algorithm. Considering the lowest error, the CM with CMA-ES showed superior performance. Subsequently, MVLR was employed to improve the CMs further. Employing MVLR to combine the CMs yielded a 57.85% decline in mean squared error and a 4.502% improvement in the correlation coefficient compared to the stand-alone algorithms. The results of the benchmark analysis validated the efficacy of this approach.

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.

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.

Feasibility of using Cortical Bone as an Ultrasound Reflector to Investigate the Relationship between Fast and Slow Waves and Porosity: A 2D Simulation Study

In the context of the ultrasound pulse-echo (PE) technique, extracting distinct fast and slow waves presents challenges due to their frequent overlap and interference with unwanted scattering waves within cancellous bone. This study aims to explore the viability of utilizing cortical bone as an ultrasound reflector for investigating correlations between fast and slow waves and porosity. Employing a 2-Dimensional (2D) simulation approach, diverse porosity levels within 2D cancellous bone models alongside cortical bone are examined. The bandlimited deconvolution method is applied to isolate fast and slow waves from the original waves. The correlation coefficient is used to compare the result between the original, fast, and slow waves. Results indicate enhanced correlations of fast (R2fast = 0.78) and slow (R2slow = 0.76) waves with porosity compared to the original waveform (R2original = 0.45). Incorporating fast and slow wave analyses could potentially enhance porosity estimation accuracy through the PE ultrasound measurements technique.

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.

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.