Permeability Prediction Method Research Articles

A comparison of permeability prediction methods using core analysis data for sandstone and carbonate reservoirs

Permeability is one of the most important parameters of reservoir rocks, it illustrates the capacity of rock to transmit fluids (oil, gas, water) in pore spaces. Permeability data can be obtained from routine core analysis in laboratory on 1.5 in core plugs and sidewall core. However, most of the wells are not coring due to problems during coring and higher costs, so permeability prediction in uncored sections play a significantly important role. The variety of methods developed to estimate permeability at pore scale, such as Kozeny–Carman, Swanson and Pittman. In fact, those equations are applied individually to estimate permeability. In this research, permeability estimation methods will be used on the same rock (sandstone or carbonate rock) to detect the suitable method for each rock type. Porosity, permeability data from routine core analysis and pore throat size from mercury injection capillary pressure on sandstone and carbonate rocks from Cuu Long and Song Hong basins in Vietnam will be gathered and permeability estimation conducted by using Hydraulic Flow Unit, Mercury Injection Capillary Pressure and Pittman methods based on that data. Estimated permeability obtained from each method will be compared with core permeability, the method with the highest R-squared be selected. The research shows that Hydraulic Flow Unit is optimized method for permeability prediction on sandstone with R-squared > 0.9. Mercury injection capillary pressure is the most accurate method to estimate permeability on carbonate rocks related to heterogeneity and complicated pore system with R-squared > 0.96. That results will help engineers have a fast and accurate decision for permeability prediction methods selection on sandstone and carbonate rocks. In addition, the empirical equations were derived to predict permeability on sandstone and carbonate rocks with the highest coefficient of correlation in multiple regression analysis and based on the relationship between porosity, permeability and pore throat size.

Permeability evaluation on oil-window shale based on hydraulic flow unit: A new approach

Permeability is one of the most important petrophysical properties of shale reservoirs, controlling the fluid flow from the shale matrix to artificial fracture networks, the production and ultimate recovery of shale oil/gas. Various methods have been used to measure this parameter in shales, but no method effectively estimates the permeability of all well intervals due to the complex and heterogeneous pore throat structure of shale. A hydraulic flow unit (HFU) is a correlatable and mappable zone within a reservoir, which is used to subdivide a reservoir into distinct layers based on hydraulic flow properties. From these units, correlations between permeability and porosity can be established. In this study, HFUs were identified and combined with a back propagation neural network to predict the permeability of shale reservoirs in the Dongying Depression, Bohai Bay Basin, China. Well data from three locations were used and subdivided into modeling and validation datasets. The modeling dataset was applied to identify HFUs in the study reservoirs and to train the back propagation neural network models to predict values of porosity and flow zone indicator. Next, a permeability prediction method was established, and its generalization capability was evaluated using the validation dataset. The results identified five HFUs in the shale reservoirs within the Dongying Depression. The correlation between porosity and permeability in each HFU is generally greater than the correlation between the two same variables in the overall core data. The permeability estimation method established in this study effectively and accurately predicts the permeability of shale reservoirs in both cored and un-cored wells. Predicted permeability curves effectively reveal favorable shale oil/gas seepage layers and thus are useful for the exploration and the development of hydrocarbon resources in the Dongying Depression. Cited as : Zhang, P., Lu, S., Li, J., Zhang, J., Xue, H., Chen, C. Permeability evaluation on oil-window shale based on hydraulic flow unit: A new approach. Advances in Geo-Energy Research, 2018, 2(1): 1-13, doi: 10.26804/ager.2018.01.01

The porosity and permeability prediction methods for carbonate reservoirs with extremely limited logging data: Stepwise regression vs. N-way analysis of variance

The available theories and methods for logging interpretation have achieved lots of excellent outcomes in the silicate and carbonate formations. But due to the complicated pore-throat systems and developed fractures, many practical physical models suitable for the silicate formations often need to aid more parameters to character the carbonate, resulting in the difficulties increasing in the reservoir evaluation. As the multivariate linear regression has the capability to calculate targets only using fitting model as a simple linear equation, both useful algorithms of stepwise regression and N-way analysis of variance are proposed in the study of porosity and permeability prediction. The stepwise regression selects out the significant curves from the abundant logging data based on the variance contribution calculation and colinearity verification, thus making the established fitting model optimal. The N-way analysis of variance also performs well on the selection of the significant logs but its calculation only needs to conduct once. Additionally, according to the results of analysis of variance and multiple comparisons, those fitted values can be accurately corrected by the creative algorithm of fitting correction in further step. The logging data derived from three wells of LULA oilfield within Santos Basin are chosen to verify the proposed methods. In the porosity prediction, the mean absolute error of fitted results for the stepwise regression in all verified cases is 3.41, 3.87, 2.89, 3.62, 1.57, and for the N-way analysis of variance is 2.84, 2.68, 2.76, 2.92 and 1.19. In the permeability prediction, the error is 66.28, 1132 400, 146.8, 3617, 0.37 for the stepwise regression, and for the N-way is 48.34, 1098.4, 191.9, 3187.7 and 0.62. Through the verified effects of the cases, all the statistical results indicate that both proposed methods are capable to predict the porosity and permeability in a cost-efficient way. Meanwhile, through the multiple comparisons with the core data, the fitted values obtained by the N-way analysis of variance are more accurate than those from the stepwise regression in general.

Identification of sedimentary-diagenetic facies and reservoir porosity and permeability prediction: An example from the Eocene beach-bar sandstone in the Dongying Depression, China

Accurate prediction of reservoir porosity and permeability is essential for prospecting hydrocarbon reserves and petroleum production capacity. We propose an innovative reservoir porosity and permeability prediction method through identifying sedimentary-diagenetic facies, determining the porosity-permeability trends using core measurement data, extrapolating the spatial distribution of the sedimentary-diagenetic facies using log data through the Bayes discriminant analysis and predicting the reservoir porosity and permeability. The essence of the method was illustrated and its effectiveness was demonstrated using the Eocene beach-bar sandstones in the Dongying Depression, Bohai Bay Basin, eastern China. The Eocene beach bar sandstones are classified into fine sandstone, siltstone, and argillaceous siltstone based on grain sizes, sorting, and matrix contents. The major diagenetic processes that influence the porosity and permeability of the beach-bar sandstones are compaction, carbonate cementation and feldspar dissolution. Seven sedimentary-diagenetic facies were identified in the beach-bar sandstones based on the lithological types, and their corresponding diagenesis and influence extent on reservoir properties. The variation ranges of porosity and permeability (log K) of these sedimentary-diagenetic facies are typically less than 6% and 1.2, respectively. Both the porosity and permeability have well defined functional relationships with depth. The sedimentary-diagenetic facies can be identified effectively from logging data through the use of the Bayes discriminant analysis and corresponding cross-plots. The porosity and permeability (log K) of the well evaluated in the study were predicted with errors in the range of ±3% and ±0.6, respectively, based on the distribution and the fitting equation of the trend lines for the seven sedimentary-diagenetic facies. The predicted porosities and permeability of sedimentary-diagenetic facies match the measured porosities and permeability well.

Improved sensitivity based linear learning method for permeability prediction of carbonate reservoir using interval type-2 fuzzy logic system

This paper proposed an improved sensitivity based linear learning method (SBLLM) model through the hybridization of type-2 fuzzy logic systems (type-2 FLS) and SBLLM. The generalization abilities of the SBLLM often rely on whether the available dataset is free of uncertainties to ensure successful result, which means that its generalization capability is sometimes limited depending on the nature of the dataset. Type-2 FLS has been choosing in order to better handle uncertainties existing in datasets and in the membership functions (MFs) in the traditional type-1 fuzzy logic system (FLS). In the proposed method, the type-2 FLS is used to handle uncertainties in reservoir data so that the cleaned data from type-2 FLS is then passed to the SBLLM for training and then final prediction using testing dataset follows. Comparative studies have been carried out to compare the performance of the proposed hybrid system with that of the standard SBLLM. Empirical results from simulation show that the proposed improved hybrid model has greatly improved upon the performance of the standard SBLLM.

A New Coal Permeability Prediction Method Based on Experiment and Dimension Analysis

Summary This paper aims to investigate the effect of temperature and effective stress on coal permeability. Through the experiment, we find a reversal phenomenon in which the coal permeability presents different change trends as temperature increases at two sides of the reverse point. The term “critical effective stress” refers to the effective stress at the reverse point. When effective stress is lower than the critical effective stress, the outward expansion effect of the coal block caused by grain and gas swell is greater than compaction effect as temperature increases under low effective stress condition. Therefore, the coal expands primarily outward, which results in fissure opening and permeability increase. When effective stress is higher than the critical effective stress, high effective stress limits the coal's outward expansion. The coal expands inward with increasing temperature, thus causing fissure closure and permeability decrease. On the basis of dimension analysis and regression analysis, combined with experimental data, this paper develops a high-precision semitheoretical coal permeability model of Qinshui basin in China. Simultaneously, what this paper presents is a permeability prediction method: measuring coal core permeability and performing dimension analysis and regression analysis. With this work done, we can establish a similar permeability model suitable for other target zones. Thus, the analytical method presented in this paper provides a basis for coal permeability prediction.

The Application of the Lattice Boltzmann Method for Permeability Prediction of Porous Media: Investigating the Effects of Viscosity and Grid Resolution

The lattice Boltzmann method is a relatively new simulation technique of computational fluid dynamic class. Its several advantages such as dealing with complex boundary and incorporating of microscopic interaction make it an alternative and promising numerical scheme for simulating fluid flow in porous media. Three lattice Boltzmann equation models are introduced and used for calculating permeability of a 2D porous media. Analytical solutions of Poiseuille flow between infinite parallel plates is used for validating lattice Boltzmann equation models. In the numerical simulations the effects of grid resolution and viscosity on the predicted permeability are checked.

A systematic method for permeability prediction, a Petro-Facies approach

In this study, using a relatively large and complete data set of a complex carbonate reservoir, it is proven that among the numerous methods proposed for the prediction of permeability, the porosity-facies based models are the best choice from a theoretical and practical point of view. Based on petrographic examinations and petrophysical interpretations, a systematic approach is proposed for permeability prediction.Porosity and pore type have been identified as the main influential attributes and Petro-Facies is the preferred way of permeability estimation in the un-cored wells. The Fuzzy C-Means (FCM) clustering method has been applied for the subdivision of the data space into 12 representative Petro-Facies and the corresponding relationships between porosity and permeability for each facies has been determined. After identification of the main responsive well log suite, based on the rank correlation, a classification tree approach was used for the population of Petro-Facies in the un-cored wells. Then, the relevant porosity–permeability relation was applied for permeability calculation.This study shows that by using a systematic approach for the identification of the controlling parameters of permeability and determining the proper permeability model, it is possible to achieve a reliable permeability prediction.

Comparison of seismic-based methods for fracture permeability prediction

There are several methods based on seismic reflection data for locating natural fracture systems and predicting their permeability. The only practical way to compare the predictive accuracy of each method is to evaluate the results from different oilfields using data from several wells in each field. We have studied methods based on reflection amplitude, reflection curvature and its derivatives, coherency cube, spectral decomposition, ant tracking technology, azimuthal anisotropy of P-wave velocity, and duplex wave migration (DWM) amplitude cube analysis. Here we report and analyse the results of each method for carbonate fracture plays. The study involved the analysis of several 3D data volumes from two different areas using results from hundreds of wells which have been drilled over the last three decades. We conclude that the DWM technique is the most reliable method for fracture permeability prediction. Lukoil planned a 2010 drilling programme for horizontal wells based on this DWM technology, and the first exploration results show that the locations of fracture systems were predicted with an accuracy of 25 m.

A Prediction Method for In-Plane Permeability and Manufacturing Applications in the VARTM Process

VARTM (Vacuum Assisted Resin Transfer Molding) is a popular method for manufacturing large-scaled, single-sided mold composite structures, such as wind turbine blades and yachts. Simulation to find the proper infusion scenario before manufacturing is essential to avoid dry spots as well as incomplete saturation and various fiber weaves with different permeability affect numerical simulation tremendously. This study focused on deriving the in-plane permeability prediction method for FRP (Fiber Reinforced Plastics) laminates in the VARTM process by experimental measurements and numerical analysis. The method provided an efficient way to determine the permeability of laminates without conducting lots of experiments in the future. In-plane permeability imported into the software, RTM-Worx, to simulate resin flowing pattern before the infusion experiments of a 3D ship hull with two different infusion scenarios. The close agreement between experiments and simulations proved the correctness and applicability of the prediction method for the in-plane permeability.

Investigation of vesicle electrokinetic chromatography as an in vitro assay for the estimation of intestinal permeability of pharmaceutical drug candidates

As the pharmaceutical industry continues the daunting search for novel drug candidates, there remains a need for rapid screening methods not only for biological activity, but for physiochemical properties as well. It is invaluable that adequate model systems for absorption and/or bioavailability be developed early in the drug evaluation process to avoid the loss of promising compounds late in development. The focus of this paper is the use of vesicle EKC (VEKC) as a high-throughput, easy, cost-effective, and predictive model for the passive transcellular diffusion of drug candidates in the intestinal epithelium. Vesicles are large aggregates of molecules containing a spherical bilayer structure encapsulating an internal cavity of solvent. It is this bilayer structure that makes vesicles attractive as model membranes. In this study, vesicles were synthesized from both phospholipids and surfactant aggregates, and then employed as pseudostationary phases in EKC (VEKC). The interaction of drug molecules with vesicles in EKC was then used as the basis for an in vitro assay to evaluate passive diffusion. The VEKC technique showed a statistical correlation between the retention of drug candidates using surfactant and phospholipid vesicles and passive diffusion data (log Pow and colon adenocarcinoma). VEKC analysis offers high-throughput capabilities due to the short run times, low sample, and solvent volumes necessary, as well as instrument automation. However, due to the complexity of drug absorption in the intestine, difficulty arises when a single in vitro model is used to predict in vivo absorption characteristics. Therefore, the retention of drug candidates using VEKC in conjunction with other permeability prediction methods can provide a primary screen for a large number of drug candidates early in the drug discovery process with minimal resources.

Using three-dimensional reconstructed microstructures for predicting intrinsic permeability of reservoir rocks based on a Boolean lattice gas method

This paper presents a method for predicting the intrinsic permeability of porous media based on the integration of the local velocity field. Three-dimensional representations of the porous structure are reconstructed from two-dimensional binary images, after segmentation of digital images acquired from thin plates, commonly used in microscopy. Velocity field is calculated on these three-dimensional representations using a Boolean lattice gas method (LGA). Reconstruction is based on a Gaussian stochastic simulation. Mercury-intrusion results furnish auxiliary data that are used for the estimation of a critical percolation diameter and to establish a necessary condition for the binary source images to give accurate predictions of permeability, considering the intrinsic limitations of the reconstruction process. Reconstruction method and connection loss, resolution factor, adherence conditions and the effects of Boolean noise in the calculation of permeability are fully discussed. The method is used to simulate flows through several petroleum reservoir rocks, leading to intrinsic permeability prediction. Simulation is compared with experimental results. Considered as an intrinsic permeability prediction method based on the geometrical information that is possible to recovery from microscopy thin plates, three-dimensional reconstruction appears to be the most critical step in present simulation scheme.

炭酸塩岩油層における浸透率の推測

Permeability is one of important factors for a reservoir simulation model because flow in a model is affected a lot by this parameter. Whether “reservoir characterization” method or conventional reservoir evaluating method would be taken to construct a simulation model, permeability distribution along the wells is important, because the distribution is used as a base to predict field-wide permeability distribution.In this study, two permeability prediction methods were evaluated for estimating uncored well permeability from wire line well logs. One is method using artificial neural network (ANN), which is suitable to analyze complicated non-linear problem. The other is multiple linear regression analysis (MLRA) method. Also classical empirical porosity-permeability relationship method was evaluated to compare the accuracy or validity of ANN and MLRA methods.ANN showed little improvement and MLRA showed no improvement, compared to conventional porosity-permeability relationship result. Especially, better results were seen when all data were fed in to the ANN system, assuming that no geological information was provided. However, even ANN, which showed the best result, is not recommendable method for this reservoir because of lower quality than required. In conclusion, these trials showed difficulty of estimating uncored well permeability in this reservoir because of poor log quality and the nature of this complicated carbonate reservoir.