Identification Of Flow Units Research Articles

Rock Typing and Flow Unit Identification in the Upper Cretaceous Carbonate Reservoirs in Jambur Oil Field

The effectiveness of dynamic models in field development and predicting future reservoir performance depends on the accuracy and reliability of geological models. These models are constructed based on an accurate categorization of reservoir rock types and the identification of flow units. understanding the geological structure, lithological characteristics, and depositional processes is crucial in differentiating rock types and determining flow units within strata. This study focuses on identifying rock types and flow units within the upper Cretaceous reservoir/Qamchuqa formation of the Jambur oilfield. By employing four petrophysical techniques across six wells, it was determined that the Rock Fabric Number technique and the Winland porosity-permeability relation were insufficient for accurately estimating permeability, primarily due to constraints in regression analysis. The classification of flow zone indicators resulted in the identification of five distinct rock classes that offer a more reliable means of assessing permeability in intervals lacking core data. Subsequently, the cluster analysis process sorted the reservoir rock into cohesive groups based on the raw log data and calibrated it with the Flow Zone Indicator (FZI) method. In conclusion, permeability can be determined through equations derived from the FZI method, and the establishment of petrophysical characteristics within a geological model can be achieved by utilizing rock facies derived from cluster analysis.

Enhancing Reservoir Zonation through Triple Porosity System: A Case Study

Summary The Asmari-Jahrom reservoirs, located in southwest Iran, are recognized as one of the major fractured reservoirs in the world. Understanding the role of fractures in enhancing hydrocarbon flow and permeability is of utmost importance. In this study, petrophysical conventional logs [neutron porosity (NPHI), density (RHOB), sonic (DT), and gamma ray (GR)] and advanced image logs [formation microresistivity imaging (FMI)] were used to investigate the reservoir properties. The novelty of this study lies in the implementation of triple porosity on reservoir quality and identification of flow units in Asmari-Jahrom reservoirs using petrophysical and borehole image logs. By quantifying fracture and vuggy porosity and correlating them with velocity deviation log and fracture parameters [fracture aperture (VAH) and fracture density (VDC)], it was demonstrated that fracture porosity is directly related to VAH. High peaks were observed in fracture parameters, particularly in VAH diagrams where the velocity deviation log was negative and low. Total porosity from density logs was found to match secondary porosity from petrophysical logs, validating FMI results. However, FMI log resolution was higher, enabling clearer identification of fracture porosity peaks. The velocity deviation log indicated that the predominant type of porosity in the reservoir was matrix (primary) porosity. However, fracture and vuggy porosity were also observed in certain zones. Based on indirect evidence such as drilling mud loss, porosity type (matrix, fracture, and vuggy), porosity amount, and oil saturation, 18 zones were identified to determine quality zones with appropriate reservoir quality. Asmari-Jahrum reservoirs were found to possess high storage and flow capacity. The presence of multiple fracture types, especially longitudinal fractures, contributed to the development of secondary porosity and enhanced flow unit quality. Despite their complexity, these fractured carbonate reservoirs were analyzed comprehensively through integrated petrophysical and FMI log interpretation, enabling optimized reservoir performance and facilitating hydrocarbon production.

Petrographical and petrophysical rock typing for flow unit identification and permeability prediction in lower cretaceous reservoir AEB_IIIG, Western Desert, Egypt

The primary objective of this study is to identify and analyze the petrophysical properties of the newly investigated AEB_IIIG member reservoir in Meleiha West Deep (MWD) Field and to classify it into different rock types. Additionally, this research intends to develop mathematical equations that may be utilized to estimate permeability in uncored sections of the same well or in other wells where core samples are unavailable. The analysis focused on the pore hole records of ten wells that were drilled in MWD Field. The reservoir levels were identified, and their petrophysical parameters were evaluated using well logs and core data. We were able to recognize seven different types of rocks (petrophysical static rock type 1 (PSRT1) to PSRT7) using petrography data, the reservoir quality index (RQI), the flow zone index (FZI), R35, hydraulic flow units (HFUs), and stratigraphy modified Lorenz (SML) plots. The analysis of the petrophysical data shows that AEB_IIIG has unsteady net pay thicknesses over the area. It has a range of 8–25% shale volume, 12–17% effective porosity, and 72–92% hydrocarbon saturation. The RQI results show that psrt1, psrt2 and psrt3 have a good reservoir quality as indicated by high R35 and helium porosity, respectively. They contribute with more than 75% of the reservoir production. The equation derived for each rock type of AEB_IIIG reservoir can be employed to forecast the permeability value distribution inside the reservoir.

Identification of flow units using the gas flow experiments and static data in heterogeneous oolitic reservoirs of the Puguang gas field

Identification of flow units in heterogeneous carbonate reservoirs is challengeable. The core wells, thin section, physical property measurements, and logging data from Feixianguan (Triassic) oolitic reservoirs of the Puguang gas field are studied in detail. Three pore types are identified: intergranular pore (Inter-P), intergranular and intragranular pore (Inter-Intra-P), and intragranular pore (Intra-P). There is a discrete correlation between porosity and permeability, but a relatively good linear relationship between Inter-P and Inter-Intra-P pore types. Combining the method with flow experiments, a new method for flow unit identification based on the flow experiments under simulated reservoir conditions is developed. The flow capability index (FCI) obtained from flow experiments can directly reflect the varieties of gas flow behavior in different reservoirs and can distinguish flow unit types. From the experiment results of 20 cylinder samples, similar FCI values are considered to be the same flow unit; meanwhile, the FCI, reservoir quality index (RQI), and permeability thresholds are obtained from related crossplots, thereby dividing four types of flow units and their thresholds. The RQI prediction formula is established by interpreting the measured physical property data and related logging data in Inter-P and Inter-Intra-P. The flow unit identification of coring well using predicted RQI and measured RQI is close to the observed results, especially in FU1 and FU3. This finds that the combination of the gas flow experiments and static data can effectively support flow unit identification and reservoir division in the Puguang gas field.

Research on Identification of Flow Units Based on FZI

The FZI expression is obtained by derivation, and the geological significance of FZI is proposed for the first time - it is a parameter reflecting the micro-pore structure of the rock, determined by the microstructure of the rock. Further analysis of FZI indicates that there are misconceptions in it, and it is feasible to identify flow units based on FZI. It points out the advantages and disadvantages of FZI. With the introduction of limited condition, better division of flow units can be realized based on FZI.

Determination of Reservoir Flow Units from Core Data: A Case Study of the Lower Cretaceous Sandstone Reservoirs, Western Bredasdorp Basin Offshore in South Africa

The overarching aim of this study is to use core measurements of porosity and permeability in three wells (MO1, MO2, and MO3) to generate a scheme of sandstone reservoir zonation for identification of flow units in the E-M gas field of the Western Bredasdorp Basin Offshore in South Africa. The evaluation method began by establishing rock types within a geological framework that allowed the definition of five facies, grouped as facies A, B, C, D, and E. Facies A was recognized as the best petrophysical rock type. In contrast, facies E was recognized as impervious rock. The results of independent reservoir classification methods were integrated to identify flow zones that yielded positive results. The results ultimately culminated in a zonation scheme for the Basin. Twelve flow zones were identified and were broadly classified as high, moderate, low, very low, and tight zones. The high zone was characterized by pore throat radius of ≥ 10 μm, flow zone index (FZI) of ≥ 5.0 μm, and flow unit efficiency (FUE) of ≥ 0.8. In contrast, very low efficiency zones had pore throat radius and FZI of < 2 μm, and FUE of ≤ 0.2. The high-efficiency zones were comparable to facies A and the tight zone to facies E. Facie C provided sand–sand contacts that allowed flow between the zones. One high, two moderate, four low, and five very low efficiency zones were identified. The plot of FUE can be compared directly with flowmeter logs. The results obtained from this study will serve as an input parameter for reservoir studies in the western Bredasdorp Basin.

Bayesian-Based Approach for Hydraulic Flow Unit Identification and Permeability Prediction: A Field Case Application in a Tight Carbonate Reservoir

Summary Flow zonation and permeability estimation is a common task in reservoir characterization. Typically, integration of openhole log data with a conventional and special core analysis solves this problem. We present a Bayesian-based method for identifying hydraulic flow units in uncored wells using the theory of hydraulic flow units (HFUs) and subsequently compute permeability using wireline log data. We use a nonlinear optimization scheme on the basis of the probability plot to determine pertinent statistical parameters of each flow unit. Next, we couple these results with the F-test and the Akaike's criteria with the purpose of establishing the optimal number of HFUs present in the core data set. Then, we allocate the core data into their respective HFUs using the Bayes’ theorem as clustering rule. Finally, we apply an inversion algorithm on the basis of Bayesian inference to predict permeability using only wireline data. We illustrate the application of the procedure with a carbonate reservoir having extensive conventional core data. The results show that the Bayesian-based clustering and inversion technique delivers permeability estimates that agree with the core data and with the results obtained from a pressure transient analysis.

Identifying flow units by FA-assisted SSOM—An example from the Eocene basin-floor-fan turbidite reservoirs in the Daluhu Oilfield, Dongying Depression, Bohai Bay Basin, China

Accurate identification of flow units is essential in oil and gas development. In this study, integrated core, log, and production data from the Eocene basin-floor-fan turbidite reservoir demonstrates a new method to identify flow unit using factor analysis (FA) and supervised-mode self-organizing-map neural network (SSOM). The reservoirs were classified into four types of flow units (I, II, III and IV). Five principal factors were extracted through factor analysis on thirteen evaluation parameters for reflecting the characteristics of basin-floor-fan turbidite reservoirs. Then using the five principal factors as the input, the flow unit prediction model was established based on SSOM. The prediction results of flow unit based on FA-SSOM are consistent with the results of core analysis and test oil conclusion, which have a good classification effect. Therefore, the prediction model based on FA and SSOM provides an effective way for fine reservoir interpretation. The established FA-SSOM model is further compared with Linear Discriminant Analysis (LDA) and Back Propagation (BP) neural network and has the best prediction. This study also sheds light on the remaining oil development by linking the identified flow unit type with daily production data.

An Integrated Method of Data Mining and Flow Unit Identification for Typical Low Permeability Reservoir Prediction

With the development of oilfield exploration and mining, the research on continental oil and gas reservoirs has been gradually refined, and the exploration target of offshore reservoir has also entered the hot studystage of small sand bodies, small fault blocks, complex structures, low permeability and various heterogeneous geological bodies. Thus, the marine oil and gas development will inevitably enter thecomplicated reservoir stage; meanwhile the corresponding assessment technologies, engineering measures andexploration method should be designed delicately. Studying on hydraulic flow unit of low permeability reservoir of offshore oilfield has practical significance for connectivity degree and remaining oil distribution. An integrated method which contains the data mining and flow unit identification part was used on the flow unit prediction of low permeability reservoir; the predicted results were compared with mature commercial system results for verifying its application. This strategy is successfully applied to increase the accuracy by choosing the outstanding prediction result. Excellent computing system could provide more accurate geological information for reservoir characterization.

Identification of flow units using the joint of WT and LSSVM based on FZI in a heterogeneous carbonate reservoir

The objective of this study was to develop an accurate method for predicting hydraulic unit types in a heterogeneous carbonate reservoir. There is a significant practical potential in the use of the flow unit characterization. Identification of flow units in inhomogeneous carbonate reservoir presents a great challenge to geologists and engineers. A new method for dividing the flow units was proposed in this study based on the joint of wavelet transform (WT) and least squares support vector machine (LSSVM) within the most productive carbonate reservoir of the Minghuazhen Formation in Region A, Block X in the Petrochina Dagang oilfield. Petrophysical properties derived from core data and logging from 21 representative wells were analyzed. The flow units were classified as five types based on the flow zone index (FZI) approach. The WT and LSSVM were jointly used for learning and training each unit. The well logs were broken down into high and low frequency data using WT. Sensitivity analysis of parameters of training samples to select the largest impact was performed with C5.0 decision tree to obtain a WT-trained set. A predictive model was then established by training LSSVM model. The final trained model with the identification rule and criterion for the classification of flow units was used for identifying flow units in the cored and non-cored intervals of reservoir. The result from this study is consistent with core data and is more accurate than that from the previous investigations. It is concluded that using the combination of the WT and LSSVM improved the accuracy of classification of flow units in the Minghuazhen Formation.

A Reversed Geo–Dynamic Approach for Brownfield Rejuvenation

Summary Brownfields in this paper are defined as mature fields where production declined to less than 35–40% of the plateau rate and where primary and secondary reserves have been largely depleted. Big data, high field complexity after a long production history, and slim economic margins are typical brownfield challenges. In the exploration-and-production (E&amp;P) industry, “sequential” field-evaluation approaches (first “static,” then “dynamic”), have proved successful for greenfield development, but often do not achieve satisfying results for brownfields. This paper presents a new work flow for brownfield re-evaluation and rejuvenation. The “reversed” geo-dynamic field modeling (GDFM) rearranges existing elements of reservoir evaluation to obtain a purpose-driven, deterministic reservoir model, which can be quickly translated into development scenarios. The GDFM work flow is novel because (1) it turns upside down the discipline-driven sequential work flow (i.e., starts with the history match) and (2) it uses dynamic data as input to calibrate seismic (re-) interpretation that acts as a main integration step. It combines all available data already during horizon and fault mapping. Field diagnosis, flow-unit identification, well-test reanalysis, and petrophysical and geological interpretations are all combined in a cross-discipline interaction to guarantee data consistency. This directly ensures a fully integrated, “geo-dynamic” model that forms the basis for reservoir modeling. The full dynamic/static data coupling at an early stage is the main strength of the GDFM. It reduces the model complexity, and narrows the uncertainties. Project-execution time is considerably shortened by avoidance of the characteristic full-cycle loop iterations of the sequential approaches. A brownfield example illustrates the benefits of GDFM: a consistent history match with high model accuracy and confidence. In the field example, the GDFM work flow has facilitated a turnover at only 70% of the original time budget. The ongoing drilling has confirmed model validity (“attic oil” predictions), thus further postponing the economic limit of the brownfield.

A new technique for identification of flow units of shaly sandstone reservoirs

The available techniques for identification of flow units of shaly sandstone reservoirs are based on different concepts, do not provide satisfactory results, and usually show discrepancy. This research is devoted to evaluating current techniques and developing a new concept capable to identify flow units constituting shaly sandstone reservoirs. A new mathematical model has been developed and validated to identify flow units in shaly sandstone reservoirs. The newly developed concept is called shale number (SN) and combines rock properties, fluid properties, petrophysical properties, and hydraulic flow conditions of shaly reservoir rocks. Fifteen actual, reserved, and fracture-free reservoir samples of shaly sandstone reservoir are used to develop SEM images and measure required rock properties of porosity, permeability, and oil/brine relative permeability. The developed data using SEM images and lab experiments are used to characterize the rocks, test feasibility of permeability-porosity crossplot and flow zone index (FZI) techniques, and validate the newly developed mathematical model. The results revealed that the SEM images are capable to identify the clay types as detrital clays and hairy illites plus clay distribution as filling micropores and coating grains of shaly sandstone formations. The results have proven that techniques of porosity–permeability crossplot and FZI have severe scattering degree of data and poor capability for identification of flow units of shaly sandstone reservoirs. The developed model of SN has been validated and showed superior accuracy in application. A plot of SN versus reservoir quality left( {sqrt {K/varphi } } right) provided unique straight lines passing through the origin and capable to delineate flow units of shaly sandstone reservoirs. The newly developed shale number overcomes several drawbacks of previous and current techniques used for identification of flow units. Therefore, the application of the new technique provides improved reservoir description of shaly sandstone reservoirs, which secures better development planning.

Flow prediction using well log data and intelligent techniques

ABSTRACTSpatial distribution of petrophysical properties within the reservoirs is important to provide a reliable reservoir model. The most complete reservoir description is generally provided through the identification of flow units. Then, the constructed model can be used for reservoir simulation of different production scenarios within the reservoir. However, before performing modeling and simulation jobs, accurate prediction of flow units is an important task and to reach a reliable petrophysical description first of all one should be able to predict flow units with high precision. Achieving this goal will be discussed in this article.This paper suggests intelligent techniques using artificial neural network (ANN), fuzzy logic (FL), and neuro-fuzzy (NF) to determine flow units from well log data. For this purpose, available routine core and wireline log data from five wells in Ilam Formation at the Ahwaz super giant oil field are used to find the best intelligent formulation between core-derived flow units and well log data. Validation of the predictive capability of the models was evaluated in one separate cored well (blind test well). The results show that FL-derived flow units had better accuracy with respect to other techniques. This comparison showed that FL can be used as the most reliable intelligent technique for flow unit prediction from well log data in uncored wells.

Identification of flow units using methods of Testerman statistical zonation, flow zone index, and cluster analysis in Tabnaak gas field

The relation between porosity and permeability parameters in carbonated rocks is complicated and indistinct. Flow units are defined with aim of better understanding reservoir unit flow behavior and relation between porosity and permeability. Flow units reflect a group of rocks with same geological and physical properties which affect fluid flow, but they do not necessarily coincide with boundary of facies. In each flow unit homogeneity of data is preserved and this homogeneity fades in the boundaries. Here, in this study, three methods are used for identification of flow units and estimation of average porosity and permeability in three wells of Tabnaak gas field located in south of Iran. These methods include Testerman statistical zonation, flow zone index (FZI), and cluster analysis. To identify these units, compilation of core porosity and permeability are used. After comparing results of flow units developed by these three methods, a good accordance in permeable zones was obtained for them, but for general evaluation of flow units in field scale, the methods of FZI and cluster analysis are more relevant than Testerman statistical zonation.

Identification of Flow-units using Methods of Testerman Statistical Zonation, Flow Zone Index, and Cluster Analysis in Tabnaak Gas Field

The relation between porosity and permeability parameters in carbonated rocks is complicated and indistinct. Flow units are defined with aim of better understanding reservoir unit flow behavior and relation between porosity and permeability. Flow-units reflect a group of rocks with same geological and physical properties which affect fluid flow, but they do not necessarily coincide with boundary of facies. In each flow-unit homogeneity of data preserved and this homogeneity fades in the boundaries. Here in this study three methods are used for identification of flowunits and estimation of average porosity and permeability in three wells of Tabnaak gas field located in south of Iran. These methods include Testerman statistical zonation, flow zone index (FZI), and cluster Analysis. To identify these units, compilation of core porosity and permeability are used. After comparing results of flow units developed by these three methods, a good accordance in permeable zones obtained for them, but for general evaluation of flowunits in field scale, the methods of FZI and cluster analysis are more relevant than Testerman statistical zonation.

Reservoir flow unit identification of the Mishrif formation in North Rumaila Field

North Rumaila Oil Field is located about 54 km to the west of the Basra in southern Iraq. The Mishrif Formation in North Rumaila is one of the most important reservoirs in southern Iraq. Identifying and characterizing petrophysical flow units are the key to understanding and improving reservoir description, exploitation, and production and predicting the performance of carbonate reservoirs to represent them as combinations of different flow units, each with uniform pore throat size distribution and similar performance. The present work is a reservoir unit identification of the Mishrif Formation in North Rumaila Field. It is based on 14 wells, nine of which are from North Rumaila Field, three wells from West Qurna Field, and two wells form South Rumaila Field. In this study, the main reservoir units of Mishrif Formation are subdivided into subunits depending on the similarity of the log curves, water saturation–porosity–depth relationship, capillary pressure data, porosity–permeability cross plots (R35), flow zone indictor approach, and computer-processed interpretation. Unit MI, which is equal to unit MA, was subdivided into two secondary reservoir units (MI.1 and MI.2), and unit MII, which is equal to unit MB1, was subdivided into four secondary reservoir units (MII.1, MII.2, MII.3, and MII.4), while unit MIII, equal to unit MB2, was considered as one flow unit. As a result, we conclude that of the subunits, MII.4 is considered the most important reservoir in the field, subunits MI.1 and MII.2 may be classified as good reservoirs, whereas subunits MI.2 and MII.1 are of moderate reservoir characteristics. On the other hand, subunit MII.3 and unit MIII may be classified as intermediate–poor reservoir characteristics.

Innovative methods for flow-unit and pore-structure analyses in a tight siltstone and shale gas reservoir

Tight gas reservoirs are notoriously difficult to characterize; routine methods developed for conventional reservoirs are not appropriate for tight gas reservoirs. In this article, we investigate the use of nonroutine methods to characterize permeability heterogeneity and pore structure of a tight gas reservoir for use in flow-unit identification. Profile permeability is used to characterize fine-scale (1 in. [2.5 cm]) vertical heterogeneity in a tight gas core; more than 500 measurements were made. Profile permeability, although useful for characterizing heterogeneity, will not provide in-situ estimates of permeability; furthermore, the scale of measurement is much smaller than log scale. Pulse-decay permeability measurements collected on core plugs under confining pressure were used to correct the profile permeability measurements to in-situ stress conditions, and 13-point averages of profile permeability were used to relate to log-derived porosity measurements. Finally, N2 adsorption, a new method for tight gas was used to estimate the pore-size distribution of several tight gas samples. A unimodal or bimodal distribution was observed for the samples, with the larger peak corresponding to the dominant pore-throat size, as confirmed by independent methods. Furthermore, the adsorption-desorption hysteresis loop shape was used to interpret the dominant pore shape as slot-shaped pores, which is typical of many tight gas reservoirs. The N2 adsorption method provides rapid analysis and does not suffer from some of the same limitations of Hg injection. In the future, we hope that the N2 adsorption method may prove useful for flow-unit characterization (based on dominant pore size) of fine-grained (siltstone-shale) tight gas reservoirs.

Efficient Detection of Productive Intervals in Oil and Gas Reservoirs

Abstract The utility and evaluation of cutoff values for net pay or net-to-gross determination have been hotly debated topics since the 1950s. There are numerous subtleties to cutoffs, but exactly how the values are calculated has largely been overlooked. Most cutoff users have been content to use a regression line to calculate the cutoff value. We show that cutoffs obtained using a regression line are likely to be inferior to estimates produced by other methods. When four methods were applied to two field datasets and compared, regression-based porosity cutoffs were between 1 and 2 pu different than the values that give the smallest number of errors. Monte Carlo simulations broadly support the results obtained from the datasets. One method, the "trial-and-error?? method, performed well through most of the tests, reducing errors by 40% from those obtained using the regression line-based cutoff. All cutoff estimation methods have errors, caused by the imperfect relationships we have between variables, such as porosity and permeability. This study shows we have a choice of methods. Because the better method can be easily applied in spreadsheet software, this should be a valuable addition to the petrophysicist's toolbox. Introduction Net pay (NP) may be defined as any interval containing economically producible hydrocarbon using a specific production method. This represents the portion of the reservoir that contains sufficient porosity, permeability and hydrocarbons for economic exploitation. NP can be interpreted as an effective thickness that is pertinent to identification of flow units and target intervals for well completions and stimulation programs [Worthington and Cosen­tino(1)]. The associated net-to-gross ratio (NGR) corresponds to the proportion of the total or gross thickness, which is composed of net pay. Numerous papers have reviewed and proposed methods for NP and NGR determination. Snyder(2) covers many of the methods in use up to the early 1970s, which used the self-potential (SP) or gamma ray (GR) logs and core analysis. More recent proposals include using capillary pressure [Vavra et al.(3)], probe permeameter measurements [Flølo et al.(4)] and percolation modelling [Li et al.(5)]. Of the large variety of possible methods, one approach is much more commonly discussed than any other. This method involves defining threshold values (or cutoffs) for the characteristics of interest and their surrogates. These limiting values are designed to define those rock intervals that show potential to contribute significantly to economic hydrocarbon production.

A FUZZY LOGIC APPROACH TO ESTIMATING HYDRAULIC FLOW UNITS FROM WELL LOG DATA: A CASE STUDY FROM THE AHWAZ OILFIELD, SOUTH IRAN

Porosity‐permeability relationships in the framework of hydraulic flow units can be used to characterize heterogeneous reservoir rocks. Porosity is a volumetric parameter whereas permeability is a measure of a rock's flow properties and depends on pore distribution and connectivity. Thus zonation of a reservoir using flow zone indicators and the identification of flow units can be used to evaluate reservoir quality based on porosity‐permeability relationships.In the present study, we attempt to make a quantitative correlation between flow units and well log responses using fuzzy logic in the mixed carbonate‐clastic Asmari Formation at the Ahwaz oilfield, South Iran. A hybrid neuro‐fuzzy approach was used to verify the results of fuzzy modelling. For this purpose, well log and core data from three wells at Ahwaz were used to make an intelligent formulation between core‐derived flow units and well log responses. Data from a separate well was used for evaluation and validation of the results.The results of this study demonstrate that there is a good agreement between core‐derived and fuzzy‐logic derived flow units. Fuzzy logic was successful in modelling flow units from well logs at well locations for which no core data was available.

Flow unit modeling and fine-scale predicted permeability validation in Atokan sandstones: Norcan East field, Kansas

Characterizing the reservoir interval into flow units is an effective way to subdivide the net-pay zone into layers for reservoir simulation. Commonly used flow unit identification techniques require a reliable estimate of permeability in the net pay on a foot-by-foot basis. Most of the wells do not have cores, and the literature is replete with different kinds of correlations, transforms, and prediction methods for profiling permeability in pay. However, for robust flow unit determination, predicted permeability at noncored wells requires validation and, if necessary, refinement. This study outlines the use of a spreadsheet-based permeability validation technique to characterize flow units in wells from the Norcan East field, Clark County, Kansas, that produce from Atokan aged fine- to very fine-grained quartzarenite sandstones interpreted to have been deposited in brackish-water, tidally dominated restricted tidal-flat, tidal-channel, tidal-bar, and estuary bay environments within a small incised-valley-fill system. The methodology outlined enables the identification of fieldwide free-water level and validates and refines predicted permeability at 0.5-ft (0.15-m) intervals by iteratively reconciling differences in water saturation calculated from wire-line log and a capillary-pressure formulation that models fine- to very fine-grained sandstone with diagenetic clay and silt or shale laminae. The effectiveness of this methodology was confirmed by successfully matching primary and secondary production histories using a flow unit-based reservoir model of the Norcan East field without permeability modifications. The methodologies discussed should prove useful for robust flow unit characterization of different kinds of reservoirs.