Estimation Of Reservoir Permeability Research Articles

Sonic Scanner Helps in Identifying Reservoir Potential and Isotropic Characteristics of Khasib Formation

Natural fractures provide an important reservoir space and migration channels for oil and gas reservoirs and control the reservoir potential. Therefore, it is essential to understand the methods for identifying accurate reservoir permeability and characterizing reservoir fractures. In particular, using conventional measurements to identify permeability and characterize fractures is very expensive. While using conventional logging data is very challenging, and an efficient characterization correlation method is urgently needed. In this paper, we have evaluated reservoir potential based on the sensitivity of sonic scanner tools to fluid mobility, maximum stress direction, and fractures presence. This tool provides a continuous estimation of these important parameters along the studied formation using a real field data. Dispersion behavior based on the difference between the maximum and minimum energy of the studied formation is used to detect reservoir heterogeneity and anisotropy. Dispersion analysis shows the presence of inhomogeneous anisotropy in several intervals along the studied formation. The methodology used in this paper provides an accurate estimation of reservoir permeability varies from 1 to 100 md. Also, the application of this technique shows an accepted percent error of reservoir permeability estimation reaches to 7% when compared to laboratory core measurements. The average direction of the maximum horizontal stress in the studied formation is detected to be NW10 to N16E. The study results prove that the proposed technique is effective for the identification of important reservoir properties in the oil industry.

Analysis of Multiple Flow/Buildup Tests Including a 5-Year Buildup: Case Study of an Australian Shale Gas Well

Summary Long-term (multiyear) buildup tests conducted for multifractured horizontal wells (MFHWs) completed in shale reservoirs offer the unique opportunity to study and analyze flow-regimes sequences that are not commonly observed with typical buildup test periods. In this study, two buildup periods (including a rarely observed, nearly 5-year buildup), and the preceding extended flow tests, were analyzed for an MFHW completed in an Australian shale gas reservoir within the Beetaloo Basin. The objectives of the analyses were to (a) identify the sequence of flow regimes observed for each test (flow/buildup, F/BU) period; (b) extract estimates of reservoir permeability and hydraulic fracture properties; and (c) study the evolution of these properties with each subsequent test. An MFHW, the Amungee NW-1H, completed in the Velkerri B shale in Australia, was analyzed. Due to a casing deformation and inability to mill out plugs beyond this, most of the flow contribution was from the heel stages of the well. The first F/BU period was conducted from 2016 to 2021 (a nearly 5-year buildup), while the second F/BU was initiated in 2021 (buildup is currently continuing). The extended (>1 month) production tests (EPTs) preceding the buildups were analyzed using rate-transient analysis (RTA) methods [flow-regime identification/straightline /type curve analysis (TCA)] modified for shale gas properties (e.g., desorption), while the buildups were analyzed using classic pressure-transient analysis (PTA) methods. The first (~5-year) buildup period (BU 1) revealed a sequence of bilinear-linear-elliptical-pseudoradial flow followed by a second linear flow period. The first two flow regimes are interpreted to be associated with interfracture flow, while the latter is assumed to correspond to linear flow to the well. Elliptical/radial flow around fractures is rationalized to occur due to interpreted relatively short fracture half-lengths (corresponding to the high-conductivity portion of the fractures). Permeability estimates are in good agreement with diagnostic fracture injection test (DFIT) analysis. Flow-regime interpretations for the other test periods (EPTs 1 and 2, BU 2) are largely consistent, although EPT 1 flow-regime interpretation was challenged by noisy data. Permeability values derived from EPTs 1 and 2 are smaller than from buildup tests, suggesting stress sensitivity caused by drawdown. Properties derived from the analysis of BU 1 and 2 are in good agreement, suggesting that any effects caused by stress sensitivity of reservoir parameters are largely reversible. Permeability derived from all tests are much larger than those obtained from laboratory data, leading to the interpretation that natural fractures are elevating system permeability. Fracture half-lengths are also much shorter than those typically reported for MFHWs. The mostly “textbook” quality well test data obtained for this field example, combined with the length of the test periods, resulted in one of the most complete flow-regime sequences observed for an MFHW completed in a shale gas reservoir. The existence of a radial flow period observed for all test periods (interpreted to be interfracture radial flow) allows for confident estimates of reservoir permeability/skin and their evolution with each subsequent test, which is rarely reported. The radial-flow-derived permeability, combined with early linear flow analysis, also allowed fracture half-length to be estimated for all tests. This case study adds significantly to our understanding of shale gas reservoir characteristics and flow-regime sequences associated with MFHWs.

Dependence of effective permeability on pore pressure gradient at low flow rates in low-permeability limestone

Many researchers note a nonlinearity of flow in tight rock samples at low rates. The effects include changes in apparent permeability with pore pressure gradient and so-called threshold gradients, below which low flow or no flow occurs. It is believed that these effects may be related to the influence of adhesive layers with altered properties at the interfaces between liquid and solid phases. For low-permeable rocks, the estimation of reservoir permeability without taking into account the nonlinearity of the flow can lead to significant errors. An account of the flow nonlinearity also can downgrade the reservoir productivity estimation and may result in underestimation of stationary oil zone appearances. Conventional methods for studying fluid flows in porous rocks are poorly suited for studying their permeability when approaching zero flow rates because of the lack of accuracy. Such studies also require a special approach for the interpretation of the data obtained. At the same time, there is a lack of discussion of the appropriate experimental technique in the publications known. Our goal was to develop a technique suitable for studying the flow nonlinearity at extremely low flow rates. Using the developed experimental technique, we carried out three series of consecutive tests on three similar limestone core samples. The samples were taken from the aquifer; however, their porosity and permeability are similar to the typical parameters of a tight oil reservoir. During the test series, we observed the change in the deviation from the linear Darcy’s law over a long time. We realized that in the sample with the most pronounced deviation from linear flow, there was a most significant decrease in net permeability in the entire series. We believe that our method can provide more precise evaluation of the permeability of tight rocks at the near real flow rate.

POSSIBILITY TO ESTIMATE BULK PERMEABILITY FROM SEISMIC DATA

Permeability is one of the most important reservoir parameter which determines the reserves. Unlike the porosity which is considered to be the static property of oil and gas reservoir, permeability is the dynamic property. It reflects the ability of reservoir rock to transmit fluid (oil gas or water). The reservoir rock is located deep below the subsurface, so that the measurement of the permeability is usually carried out from the cores. So far there is no well logging tool which measures the rock permeability directly. Physically, there is no direct relationship between porosity and permeability, the estimation of permeability from the cross-plot between porosity and permeability is just a rough estimate, but it is usually accepted in practical application. Apart from cross- correlation method, there is now exist a sophisticated approach to estimate permeability based on well log data using artificial neural network. There now exist a method which is widely accepted to estimate the porosity of the subsurface layer using seismic method. This method exploits the relationship between porosity and acoustic impedance of the sub- surface layer. And since acoustic impedance can be derived from the seismic amplitudes, it means that the rock porosity can be estimated from seismic data. A new question can be exposed as follows : "Is there any seismic wave parameter or quantity which is theoretically can be releted to permability ?" (so that the estimation of reservoir permeability can be estimated from seimic data). This paper tries to propose an idea to use a specific seismic wave parameter whis is theoretically can be related to the permeability of a reservoir rock, with a hope that more detail research can be pointed to that direction. This idea is supported by qualitative analysis and some theoretical findings

Reservoir permeability estimation from seismic amplitudes using variational mode decomposition

A new qualitative Reservoir Permeability estimation approach using variational mode decomposition named RPE-VMD is proposed which directly use seismic amplitude and frequency attributes. The method reveals amplitude anomalies that may reflect details about the heterogenous distribution of reservoir permeability. The main procedure is carried out in the time-frequency domain which generates by using a generalized S transform. Reflection coefficient information is used as seismic amplitude attribute and extracted by using variational mode decomposition combined with mutual information theory from seismic data using the logarithmic spectrum. The dominant frequency extracted in the time-frequency domain is used as seismic frequency attribute for estimating reservoir thickness information. The RPE-VMD approach uses dominant reflection coefficient information from the seismic data combined with reservoir thickness information for permeability estimation. Application of this approach to field data from a sandstone reservoir in China illustrates that it can provide permeability estimation with a high resolution and accuracy. The proposed method directly uses seismic data to measure the reservoir permeability avoiding the need of seismic velocity in the conventional estimation methods.

Real-Time Interpretation Model of Reservoir Characteristics While Underbalanced Drilling Based on UKF

This study presents a novel interpretation model for reservoir characteristics while underbalanced drilling (UBD), by incorporating an unscented Kalman filter (UKF) algorithm in a three-phase variable mass flow model of oil, gas, and liquid. In the model, the measurement parameters are simplified to bottomhole pressure and liquid outlet flow, for decreasing the amount of the computation and time. By taking into account real-time measurements, the permeability and reservoir pressure along the well can be continuously updated. Three cases including single-parameter and double-parameter estimations have been simulated, and the performance is tested against the extended Kalman filter (EKF). The results show that single-parameter estimation of reservoir permeability or pressure achieves superior performance. The filtered values of bottomhole pressure and outlet flow trace the measured values in real time. When a new section of a reservoir is opened, the estimated reservoir permeability or pressure can always be quickly and accurately returned to its true value. However, it is not possible for the double-parameter estimation to obtain good results; its interpretation accuracy is low. UKF is superior to EKF in both estimation accuracy and convergence speed, which further illustrates the superiority and accuracy of the novel interpretation model based on UKF. Benefits from this model are seen in accurate bottomhole pressure and reservoir characteristic predictions, which are of major importance for safety and economic reasons during UBD and follow-up completion operations.

Rigorous prognostication of permeability of heterogeneous carbonate oil reservoirs: Smart modeling and correlation development

Permeability estimation has a major role in mapping quality of the reservoir, reservoir engineering calculation, reserve estimation, numerical reservoir simulation and planning for the drilling operations. In carbonate formations, it is of great challenge to predict permeability by reason of natural heterogeneity, nonuniformity of rock, complexity and nonlinearity of parameters. Various approaches have been developed for measuring/predicting this parameter, which are associated with high expenditures, time consuming processes and low accuracy. In this study, comprehensive efforts have been made to the development of radial basis function neural network (RBF-ANN), multilayer perceptron neural network (MLP-ANN), least square support vector machine (LSSVM), adaptive neuro-fuzzy inference system (ANFIS), genetic programming (GP), and committee machine intelligent system (CMIS). For this purpose, a widespread databank of 701 core permeability datapoints as a function of well log data was adopted from the open literature for heterogonous formations. Moreover, several optimization techniques like genetic algorithm (GA), particle swarm optimization (PSO), and levenberg marquardt (LM) were employed to enhance the prediction capability of the proposed tools in this study. For assessing the models efficiency, several tools like crossplot and error distribution diagram were applied in association with statistical calculation. As a result, the CMIS model is identified as the most accurate model with the highest determination coefficient (R2 near to unity) and the lowest root mean square error (RMSE near to zero). As a result of GP mathematical strategy, a new user-friendly empirically-derived correlation was developed for rapid and accurate estimation of reservoir permeability. The outcome of outlier detection shows the validity of dataset used for modeling, and the effective porosity is perceived to be the most affecting parameter on the permeability estimation in terms of sensitivity analysis. The main novelty of this modeling study was the proposal of CMIS and GP-based empirically-derived models for the first time in literature. To this end, the outcome of this study can be of great value for reservoir engineers dealing with simulation and characterization of the heterogonous carbonate reservoirs.

Oil Reservoir Permeability Estimation from Well Logging Data Using Statistical Methods (A Case Study: South Pars Oil Reservoir)

Permeability is a key parameter that affects fluids flow in reservoir and its accurate determination is a significant task. Permeability usually is measured using practical approaches such as either core analysis or well test which both are time and cost consuming. For these reasons applying well logging data in order to obtaining petrophysical properties of oil reservoir such as permeability and porosity is common. Most of petrophysical parameters generally have relationship with one of well logged data. But reservoir permeability does not show clear and meaningful correlation with any of logged data. Sonic log, density log, neutron log, resistivity log, photo electric factor log and gamma log, are the logs which effect on permeability. It is clear that all of above logs do not effect on permeability with same degree. Hence determination of which log or logs have more effect on permeability is essential task. In order to obtaining mathematical relationship between permeability and affected log data, fitting statistical nonlinear models on measured geophysical data logs as input data and measured vertical and horizontal permeability data as output, was studied. Results indicate that sonic log, density log, neutron log and resistivity log have most effect on permeability, so nonlinear relationships between these logs and permeability was done.

The Use of Artificial Neural Networks in Reservoir Permeability Estimation From Well Logs: Focus on Different Network Training Algorithms

This article demonstrates how to estimate permeability in different levels of a reservoir using artificial neural networks. Well logs data from one of the Iranian oil fields are used as inputs to networks. In this article, results of five common training algorithms, including Levenberg-Marquart, Bayesian regularization, gradient descent, resilient back-propagation, and one step secant, have been compared. Among all training algorithms, resilient back-propagation had the best performance by a mean squared error of 0.294. Correlation coefficient of estimated permeability values derived from the resilient back-propagation algorithm versus core permeability values have been presented. In addition, the permeability is estimated by the multiple linear regression method. Results demonstrate that artificial neural network is more efficient and trustworthy in permeability estimation than multiple linear regression method.

A Physical-based Model of Permeability/Porosity Relationship for the Rock Data of Iran Southern Carbonate Reservoirs

The prediction of porosity is achieved by using ava ilable core and log data; however, the estimation of permeability is limited to the scare core data. Hence, porosity and saturation data through the framework of flow units can be used to make an esti mation of reservoir permeability. The purpose of this study is to predict the permeability of a c arbonate gas reservoir by using physical-based empirical dependence on porosity and other reservoi r rock properties. It is emphasized that this new relationship has a theoretical background and is ba sed on molecular theories. It is found out that if rock samples with different types are separated pro perly and samples with similar fluid-flow properties are classified in the same group, then t his leads to finding an appropriate permeability/porosity relationship. In particular, the concept of hydraulic flow units (HFU) is used to characterize different rock types. This leads to a new physical-based permeability/porosity relationship that has two regression constants whic h are determined from the HFU method. These coefficients, which are obtained for several rock t ypes in this study, may not be applicable to other carbonate rocks; but, by using the general form of the model presented here, based on the HFU method, one may obtain the value of these coefficie nts for any carbonate rock types. Finally, we used the data of cored wells for the validation of the permeability results.

Estimate Permeability from Nuclear Magnetic Resonance Measurements Using Improved Artificial Neural Network Based on Genetic Algorithm

Knowledge of the permeability distribution is critical to a successful reservoir model. Nuclear Magnetic Resonance (NMR) measurements can be used for permeability prediction because the T2 relaxation time is proportional to pore size. Due to the conventional estimators have difficult and complex problems in simulating the relationship between permeability and NMR measurements, an intelligent technique using artificial neural network and genetic algorithm to estimate permeability from NMR measurements is developed. Neural network is used as a nonlinear regression method to develop transformation between the permeability and NMR measurements. Genetic algorithm is used for selecting the best parameters and initial value for the neural network, which solved two major problems of the network: local minima and parameter selection depend on experience. Information gain principle is introduced to select the neural network's input parameters automatically from data. The technique is demonstrated with an application to the well data in Northeast China. The results show that the refined technique make more accurate and reliable reservoir permeability estimation compared with conventional methods. This intelligent technique can be utilized a powerful tool for estimate permeability from NMR logs in oil and gas industry.

Correlation Between Microstructure and Flow Behavior in Porous Sandstones

The correlation between permeability and petrographical parameters in cored samples can be used by extrapolations to predict permeability in uncored intervals. The core analysis described here is concerned with the study of fluid-rock interactions in rock samples from three sandstone reservoirs, in particular the effect of petrographical parameters on flow behavior. A positive correlation between liquid permeability and the volume fraction of silica is clearly demonstrated. Liquid permeability was correlated using multivariate regressions to one to five petrographical parameters, the results of which have useful application in the estimation of reservoir permeability where samples are not available for experimental testing.

PRESSURE AND PRESSURE DERIVATIVE ANALYSIS FOR INJECTION TESTS WITH VARIABLE TEMPERATURE WITHOUT TYPE-CURVE MATCHING

The analysis of injection tests under nonisothermic conditions is important for the accurate estimation of the reservoir permeability and the well's skin factor; since previously an isothermical system was assumed without taking into account a moving temperature front which expands with time plus the consequent changes in both viscosity and mobility between the cold and the hot zone of the reservoir which leads to unreliable estimation of the reservoir and well parameters. To construct the solution an analytical approach presented by Boughrara and Peres (2007) was used. That solution was initially introduced for the calculation of the injection pressure in an isothermic system. It was later modified by Boughrara and Reynolds (2007) to consider a system with variable temperature in vertical wells. In this work, the pressure response was obtained by numerical solution of the anisothermical model using the Gauss Quadrature method to solve the integrals, and assuming that both injection and reservoir temperatures were kept constant during the injection process and the water saturation is uniform throughout the reservoir. For interpretation purposes, a technique based upon the unique features of the pressure and pressure derivative curves were used without employing type-curve matching (TDS technique). The formulation was verified by its application to field and synthetic examples. As expected, increasing reservoir temperature causes a decrement in the mobility ratio, then estimation of reservoir permeability is some less accurate from the second radial flow, especially, as the mobility ratio increases.

Elliptical-Flow-Based Pressure-Transient Interpretation Technique

This article, written by Assistant Technology Editor Karen Bybee, contains highlights of paper SPE 105767, "A New Approach for Reliable Estimation of Hydraulic-Fracture Properties in Tight Gas Wells," by Y. Cheng, SPE, W.J. Lee, SPE, and D.A. McVay, SPE, Texas A&M U., prepared for the 2007 SPE Hydraulic Fracturing Technology Conference, College Station, Texas, 29–31 January. Pressure-transient analysis in hydraulically fractured tight gas wells commonly is based on analysis of three flow regimes: bilinear, linear, and pseudoradial. With current well-test-analysis techniques, a reliable estimation of hydraulic-fracture and reservoir properties relies on occurrence of all three flow regimes. Because pseudoradial flow often is absent, the estimation of reservoir permeability, k, and fracture half-length, xf, is uncertain and unreliable. Elliptical flow, which exists between linear flow and pseudoradial flow, is of long duration (typically months to years). Much rate and pressure data can be acquired during this flow regime, but no practical well-test-analysis technique is available currently to interpret these data. Introduction Hydraulic fracturing is used widely to complete wells in tight gas reservoirs. Fracturing increases well productivity significantly so the well can achieve economic production rates. To evaluate stimulation effectiveness, reservoir and hydraulic-fracture properties, such as k, xf, and fracture conductivity, CfD, need to be estimated. Knowledge of these parameters not only is important for predicting future production performance of fractured wells, but also has significant influence in determining development strategies in exploitation of tight gas resources. Reservoir and fracture properties determine the drainage pattern of tight gas wells. With different estimates of reservoir and fracture properties, different well patterns and effective drainage areas can be expected. Post-fracture pressure-transient testing is the technique most commonly used to estimate reservoir and fracture parameters for evaluating the stimulation effectiveness in hydraulically fractured gas wells. Current pressure-transient-test analysis in hydraulically fractured wells typically consists of conventional graphical-interpretation techniques and type-curve matching methods. The conventional graphical-interpretation techniques are based on analysis of three flow regimes: bilinear, linear, and pseudoradial. With test data during the bilinear-flow period, CfD can be estimated, and with test data during the linear-flow period, xf can be estimated. However, these estimates depend on the permeability estimate. Without independent knowledge of k, none of these estimates can be obtained reliably. The presence of pseudoradial flow is a prerequisite to determine k uniquely. However, impractically long times are required to reach pseudoradial flow in a tight gas reservoir. For example, for a typical tight gas well intersected by a hydraulic fracture with an xf of 100 ft in a reservoir with 0.01-md k, 0.15 porosity, and 0.03-cp gas viscosity, it takes 213 days to reach pseudoradial flow. If xf is 1,000 ft and other conditions are the same, 58 years are required to reach pseudoradial flow. Thus, it is very common in practice that pseudoradial flow is absent in post-fracture pressure-transient tests. As a result, the reservoir and fracture parameters usually are not determined independently from post-fracture pressure-transient analysis.

Reservoir Permeability Determination Using After-Closure Period Analysis of Calibration Tests

Summary Hydraulic fracturing generally has been limited to relatively low-permeability reservoirs. In recent years, the use of hydraulic fracturing has expanded significantly to high-permeability reservoirs. The objectives of fracturing low-permeability reservoirs and high-permeability reservoirs are defined by reservoir parameters. The estimation of reservoir permeability, a variable of great importance in hydraulic fracturing design, is frequently unknown because either candidate wells do not flow or pretreatment pressure-transient testing is required. Consequently, Nolte et al.1 introduced a new method for adding after-closure fracturing analysis to the pretreatment calibration testing sequence that defines fracture geometry and fluid-loss characteristics. The exhibition of the radial flow is ensured by conducting a specialized calibration test called the minifalloff test. Using the theory of impulse testing and the principle of superposition, Nolte et al.1 developed a method that allows the identification of radial flow and, thus, the determination of reservoir transmissibility and reservoir pressure. This work proposes a new method for determining reservoir permeability. The method also offers the potential for determining the average reservoir pressure. This procedure is based on the use of the pressure derivative, and it requires only one log-log plot for the identification of the radial-flow regime and the determination of reservoir parameters. The application of the proposed method is demonstrated on real field data from calibration tests performed on several oil and gas wells. The reservoir parameters (particularly permeability) determined with this method are verified by comparison with results obtained from pressure-buildup tests. Other sources, such as core analysis, lend support to the permeability estimated with the proposed technique.

PERMEABILITY PREDICTION FROM WELL LOGS USING AN EVOLUTIONARY NEURAL NETWORK

ABSTRACT Reservoir permeability estimation from well logs is a difficult task for petrophysicists. Use of neural networks, in recent years, has shown great promises over many traditional techniques. However, the conventional learning algorithms (e.g. backpropagation) do not give satisfactory results when the solution is trapped in local minima. This paper presents an application of an evolution algorithm that allows trapped networks to achieve the optimal solution. The proposed technique is applied to two examples from China and Indonesia. The results show that the evolutionary network has a better chance in finding the optimal solution in much shorter time. The log-derived permeability estimations are realistic and geologically meaningful.

Integrating Multiphase Production History in Stochastic Reservoir Characterization

Summary This paper focuses on integrating field-production history into reservoir characterization through stochastic inverse modeling. A key element of our approach is a three-dimensional (3D) streamline simulator that is orders of magnitude faster than traditional numerical simulators and thus allows for rapid inversion of multiphase production data. Stochastic permeability fields, conditioned to field-production history, are then generated with simulated annealing. We also explore the spatial resolution associated with estimates of reservoir permeability variations derived by use of field-production history. On the basis of techniques from geophysical inverse theory, we address such issues as data sensitivity, spatial resolution, averaging kernels, and uncertainties associated with our estimates of reservoir permeability. The proposed inversion technique has been applied to synthetic as well as field cases. The synthetic example involves a sensitivity analysis of multiphase production history in heterogeneous five-spot and nine-spot patterns. The field example consists of production history from a five-spot pattern in the north Robertson unit, a low-permeability carbonate reservoir in West Texas. Watercut history at the producers are used to estimate permeability variations in a two-layer (matrix-fracture) model of the reservoir. All computations were performed on a 125 MHz Pentium with an average run time of about 4 wall-clock hours, indicating the feasibility of our approach.

Statistical analysis of the porous microstructure as a method for estimating reservoir permeability

Back-scatter scanning electron microscope images of cross-sections of several porous rocks were analyzed to determine the statistical properties of the porous microstructure. For statistically homogeneous media these properties are the porosity and autocorrelation function. A length scale (integral correlation scale), characteristic of the spatial distribution of porosity, was obtained as the integral of the autocorrelation function. The permeability of a wide variety of rock samples, including those investigated by Coskun and Wardlaw (1993), was adequately described by an empirical equation of the form k α φ a I S b , where φ is the porosity and I S is the integral correlation scale. The results obtained have useful application in the estimation of reservoir permeability from samples not amenable to experimental testing (e.g., drill cuttings) and provide support for the use of statistical methods for the generation of 3-D model porous media.