Flow Zone Indicator Values Research Articles

Utilizing machine learning for flow zone indicators prediction and hydraulic flow unit classification

Reservoir characterization, essential for understanding subsurface heterogeneity, often faces challenges due to scale-dependent variations. This study addresses this issue by utilizing hydraulic flow unit (HFU) zonation to group rocks with similar petrophysical and flow characteristics. Flow Zone Indicator (FZI), a crucial measure derived from pore throat size, permeability, and porosity, serves as a key parameter, but its determination is time-consuming and expensive. The objective is to employ supervised and unsupervised machine learning to predict FZI and classify the reservoir into distinct HFUs. Unsupervised learning using K-means clustering and supervised algorithms including Random Forest (RF), Extreme Gradient Boosting (XGB), Support Vector Machines (SVM), and Artificial Neural Networks (ANN) were employed. FZI values from RCAL data formed the basis for model training and testing, then the developed models were used to predict FZI in unsampled locations. A methodical approach involves 3 k-fold cross-validation and hyper-parameter tuning, utilizing the random search cross-validation technique over 50 iterations was applied to optimize each model. The four applied algorithms indicate high performance with coefficients determination (R2) of 0.89 and 0.91 in training and testing datasets, respectively. RF showed the heist performance with training and testing R2 values of 0.957 and 0.908, respectively. Elbow analysis guided the successful clustering of 212 data points into 10 HFUs using k-means clustering and Gaussian mixture techniques. The high-quality reservoir zone was successfully unlocked using the unsupervised technique. It has been discovered that the areas between 2370–2380 feet and 2463–2466 feet are predicted to be high-quality reservoir potential areas, with average FZI values of 500 and 800, consecutively. The application of machine learning in reservoir characterization is deemed highly valuable, offering rapid, cost-effective, and precise results, revolutionizing decision-making in field development compared to conventional methods.

Estimating scale dependence of saturated hydraulic conductivity in soils

Understanding the effect of sample volume on hydraulic and physical properties of soils has broad applications to problems in hydrogeology, soil physics, and environmental engineering. The scale dependence of flow and transport is attributed to spatial heterogeneities, such as pore-size distribution and pore connectivity at small scales (e.g., core), fracture orientation and long-range correlations at large scales (e.g., field). In this study, we apply concepts from percolation theory to estimate the scale dependence of saturated hydraulic conductivity, Ksat. For this purpose, we use a database of undisturbed soil samples from four Danish sites (Jyndevad, Tylstrup, Estrup, and Silstrup) where the value of Ksat was measured on small (100 cm3) and large (6280 cm3) sample volumes, while porosity and soil water retention data were only available on small sample volumes. First, we apply a classification approach, widely used in petroleum engineering, to group soils based on their similarities in hydraulic properties using porosity and Ksat measurements at the small sample volume. We detect nine different soil classes with the average flow zone indicator (FZI) from 0.05 μm in class 1 to 9 μm in class 9. Next, using percolation theory, we characterize the scale dependence of critical pore-throat radius. We use the critical path analysis to link the critical pore-throat radius to the Ksat and, consequently, determine its scale dependency. Theoretical estimates compare reasonably well with the experimental measurements at the large sample volume based on the soil water retention curve and Ksat measured at the small sample volume. We find the root mean square log-transformed error (RMSLE) values 0.45, 0.77, 1.9, and 2.05 for sites Jyndevad, Tylstrup, Silstrup, and Estrup, respectively. Results show that the theory tends to provide more accurate estimations in coarser textures and unstructured soils as well as soil classes with FZI values greater than 0.7 μm (soil classes 6 to 9).

A Novel Energy Lifting Approach Using J-Function and Flow Zone Indicator for Oil Fields

The X field is located in the southwestern part of block NX89 of Kentan Basin in Libya. This field is produced from Hailan multilayer consolidated sandstone with moderate rock property and a relatively low energy supplying. The reserve of subsurface energy sources is declining with years. Therefore, techniques were combined to achieve the energy optimization and increase hydrocarbon recovery. In order to understand the subsurface formation of the reservoir and facilitate oil production, global hydraulic element technique was used to quantify the reservoir rock types. In addition, stratigraphic modified Lorenz plot was used for reservoir layering. Reservoir heterogeneity was identified using stratigraphic modified Lorenz plot and Dykstra-Parsons coefficient. Leverett J-function was used to average the 13 capillary pressure curves into four main curves to represent the whole reservoir based on flow zone indicator values. Capillary pressure was calculated and plotted with normalized water saturation; a single average curve was defined to represent the rest of the curves. Water saturation was calculated using single and multiple J-functions and compared with the available logs. With multiple J-functions, the matching results were good for both high and low-quality layers, whereas using a single J-function, the match was poor, especially for low FZI layers such as H4c and H6a. Four rock types were identified for this reservoir ranging from medium to good reservoir quality and six different layers were obtained. The reservoir was heterogeneous with a Lorenz coefficient value of approximately 0.72 and a Dykstra-Parsons value of 0.70. All approaches used in this paper were validated and showed improved hydrocarbon recovery factor.

Predicting the flow zone indicator of carbonate reservoirs using NMR echo transforms, and routine open-hole log measurements: Insights from a field case study spanning extreme micro-structure properties

Predicting the flow zone indicator is essential for identifying the hydraulic flow units of hydrocarbon reservoirs. Delineation of hydraulic flow units is crucial for mapping petrophysical and rock mechanical properties. Precise prediction of the flow zone indicator (FZI) of carbonate rocks using well log measurements in un-cored intervals is still a daunting challenge for petrophysicists. This study presents a data mining methodology for predicting the rock FZI using NMR echo transforms, and conventional open-hole log measurements. The methodology is applied on a carbonate reservoir with extreme microstructure properties, from an oil “M” field characterized by a relatively high-permeability with a median of approximately 167 mD, and a maximum of 3480 mD. The reservoir from the M field features detritic, or vuggy structure, covering a wide range of rock fabrics varying from microcrystalline mudstones to coarse-grained grainstones. Porosity has a median of approximately 22%. Dimensional analysis and regression analysis are applied for the derivation of four transforms that appear to capture approximately 80% of the FZI variance. These four transforms are formulated using the geometric mean of the transverse NMR relaxation time (T2lm), the ratio of the free fluid index (FFI) to the bulk volume irreducible (BVI), the bulk density, the sonic compressional travel time, the true resistivity, the photo-electric absorption, and the effective porosity. Non-linear regression models have been developed for predicting the FZI using the derived transforms, for the carbonate reservoir from the M field. The average relative error for the estimated FZI values is approximately 52%. The same transforms are used as input for training a developed general regression neural network (GRNN), built for the purpose of predicting rock FZI. The constructed GRNN predicts FZI with a notable precision. The average absolute relative error on FZI for the training set is approximately 3.1%. The average absolute relative error on FZI for the blind testing set is approximately 22.0 %. The data mining approach presented in this study appears to suggest that (i) the relationship between the flow zone indicator and open-hole log attributes is highly non-linear, (ii) the FZI is highly affected by parameters that reflect rock texture, rock micro-structure geometry, and diagenetic alterations, and (iii) the derived transforms provide a means for further enhancement of the flow zone indicator prediction in carbonate reservoirs.

Comparative Permeability Estimation Method and Identification of Rock Types using Cluster Analysis from Well Logs and Core Analysis Data in Tertiary Carbonate Reservoir-Khabaz Oil Field

Characterization of the heterogonous reservoir is complex representation and evaluation of petrophysical properties and application of the relationships between porosity-permeability within the framework of hydraulic flow units is used to estimate permeability in un-cored wells. Techniques of flow unit or hydraulic flow unit (HFU) divided the reservoir into zones laterally and vertically which can be managed and control fluid flow within flow unit and considerably is entirely different with other flow units through reservoir. Each flow unit can be distinguished by applying the relationships of flow zone indicator (FZI) method. Supporting the relationship between porosity and permeability by using flow zone indictor is carried out for evaluating the reservoir quality and identification of flow unit used in reservoir zonation. In this study, flow zone indicator has been used to identify five layers belonging to Tertiary reservoirs. Consequently, the porosity-permeability cross plot has been done depending on FZI values as groups and for each group denoted to reservoir rock types. On the other hand, extending rock type identification in un-cored wells should apply a cluster analysis approach by using well logs data. Reservoir zonation has been achieved by cluster analysis approach and for each group known as cluster which variation and different with others. Five clusters generated in this study and permeability estimated depend on these groups in un-cored wells by using well log data that gives good results compared with different empirical methods.

An adaptive neuro-fuzzy inference system for prediction of hydraulic flow units in uncored wells: a carbonate reservoir

ABSTRACTGeoscientists have always sought various approaches to improve reservoir characterisation by compartmentalising the depth interval into subsections with the highest consistency in pore throat size and distribution. Hydraulic flow units have demonstrated success in segmenting the depth interval of interest into subsections with distinguishable rock and fluid properties. At the primary stage, flow zone indicator values are calculated from core data within the reservoir of interest. A flow zone indicator is an acceptably unique measurement of the flow character of a reservoir interval, giving the relationship between petrophysical properties at the pore scale, like tortuosity and surface area, and the formation scale, say porosity and permeability. The next step segments the reservoir into more accurately delineated depth intervals, or hydraulic flow units. Several statistical approaches have been used successfully to group data into subsections of high similarity and consistency, herein referred to as hydraulic flow units. In this paper, a robust method was proposed for the prediction of flow zone indicators in uncored wells, which may lead to advances in reservoir management, saving a considerable amount of revenue merely by accurately predicting depth interval flow properties without the need for expensive coring operations. The results of this study show that adaptive neuro-fuzzy inference systems can be used with higher levels of confidence to model the unknown, but invaluable, data in uncored but logged wells. The results of this study proved the success of machine-learning approaches in identifying underlying trends and relationships within the data, as well as predicting unknown properties based on training data validated by blind test data. This study shows that soft computing and machine-learning approaches can be used to prognosticate the underlying hydraulic flow units based on well log responses in carbonate reservoirs.

Sandstone reservoir zonation using conventional core data: A case study of lower cretaceous sandstones, Orange Basin, South Africa

This study outlines the different methodologies used to identify and classify different rock units in the sandstone reservoirs of the Orange Basin in South Africa into flow units. The flow units are defined on the basis of conventional core porosity and the permeability of the reservoir occurring in the fluvial and shallow marine depositional environments within the lower Cretaceous period. A total of six facies were identified from the core data, and they were corroborated with gamma ray log data. Facies A1, A2, A3, and A4 are the main reservoir units with generally fine-to medium-grained sands, which dominantly comprise quartz grains. Facies A5 mainly comprises silts with lenses of fine-grained sands, while Facies A6 is claystone laminated with minor burrows filled with shale and occasional siltstone presence. The porosity and permeability were used to compute the permeability/porosity relationship, the rock quality index, pore spaces, storage and flow capacity values, and to indicate the flow zone. The highest porosity and permeability values correspond to facies A1 and A2, which are represented by mega pore spaces, and they displayed high flow capacity values and moderate to high storage capacities. The lowest porosity and permeability values belonged to core facies A6 and A5, which mostly comprise siltstone and clay with Nano pore spaces. The flow zone indicator (FZI) values determined ranges from 0.01 to 13.9 μm, which were grouped into four different hydraulic flow units (HFUs) within the cored intervals. The defined hydraulic units do not always coincide with the facies. The Windland pore throat radius (r35) calculated that pore spaces correlated better with the Lorenz flow zone plot and facies. It is recommended that the statistically derived reservoir quality index (RQI) equation of facies A1 and A2 be used to extrapolate the RQI and FZI values for other wells in the field. It is thus recommended that dynamic data be used to validate flow units.

A Review of Reservoir Rock Typing Methods in Carbonate Reservoirs: Relation between Geological, Seismic, and Reservoir Rock Types

Carbonate reservoirs rock typing plays a pivotal role in the construction of reservoir static models and volumetric calculations. The procedure for rock type determination starts with the determination of depositional and diagenetic rock types through petrographic studies of the thin sections prepared from core plugs and cuttings. In the second step of rock typing study, electrofacies are determined based on the classification of well log responses using an appropriate clustering algorithm. The well logs used for electrofacies determination include porosity logs (NPHI, DT, and RHOB), lithodensity log (PEF), and gamma ray log. The third step deals with flow unit determination and pore size distribution analysis. To this end, flow zone indicator (FZI) is calculated from available core analysis data. Through the application of appropriate cutoffs to FZI values, reservoir rock types are classified for the studying interval. In the last step, representative capillary pressure and relative permeability curves are assigned to the reservoir rock types (RRT) based upon a detailed analysis of available laboratory data. Through the analysis of drill stem test (DST) and GDT (gas down to) and ODT (oil down to) data, necessary adjustments are made on the generated PC curves so that they are representative of reservoir conditions. Via the estimation of permeability by using a suitable method, RRT log is generated throughout the logged interval. Finally, by making a link between RRT’s and an appropriate set of seismic attributes, a cube of reservoir rock types is generated in time or depth domain. The current paper reviews different reservoir rock typing approaches from geology to seismic and dynamic and proposes an integrated rock typing workflow for worldwide carbonate reservoirs.

Petrophysical and microfacies analysis as a tool for reservoir rock typing and modeling: Rudeis Formation, off-shore October Oil Field, Sinai

Petrographically, the Lower Miocene Rudeis reservoir rock samples in October oil field can be summed up into three reservoir rock types RRTs. The RRT1 samples are mostly calcareous to ferruginated sandstones, sometimes are fossiliferous, whereas the RRT2 and RRT3 samples are carbonate rocks of grainstone and packstone microfacies, respectively. Cementation by sparite and compaction are the most important porosity-reducing diagenetic factors, whereas dissolution and leaching out as well as fracturing are the most dominant porosity-enhancing factors. The petrophysical and reservoir quality studies include measuring density, porosity, permeability measurements as well as reservoir quality index (RQI), flow zone indicator (FZI) and reservoir potential index (RPI) calculations. The permeability-porosity plot is the key-factor for discriminating the petrophysical behaviour into these three rock reservoir types. Petrophysically, the studied sandstone RRT1 samples have the best storage capacity and reservoir quality (the RQI, FZI and the RPI indicate poor to very good reservoir quality), whereas the RRT3 samples have the least prospective properties (the RQI, FZI and the RPI indicates impervious reservoir quality). Based on the FZI values, the studied Rudeis Formation was discriminated into a number of hydraulic flow units (6 HFUs). HFU 5–6 are the best reservoir zones; they are assigned in the central parts of the studied area, whereas the least quality (HFU 1–3) was assigned to the south. In addition, the mercury injection capillary pressure tests (MICP) indicates that, the RRT1 samples are ranked as IV (fair) to VI (impervious) ranks, the RRT2 as V (poor) to VI, whereas the RRT3 samples are impervious samples. Wettability of the studied RRT1 samples is mostly neutral.

Reservoir zonation, rock typing and compartmentalization of the Tortonian-Serravallian sequence, Temsah Gas Field, offshore Nile Delta, Egypt

The present study aims to evaluate the petrophysical properties and to discriminate the Tortonian Wakar and the Serravallian Sidi Salem formations in the offshore Temsah gas field, north Egypt into distinctive rock types. The measured conventional core data includes helium and summation fluids porosities, grain and bulk densities, horizontal and vertical permeabilities as well as the water saturation. Also, the Reservoir Quality Index (RQI), Flow Zone Indicator (FZI) and Reservoir Potentiality Index (RPI) were calculated to establish a successful reservoir zonation. In the Temsah field, The Tortonian- Serravallian sequence has been discriminated into four reservoir rock types (RRTs) which are described based on its lithologic composition as 1) Sandy to argillaceous carbonates (RRT1), 2) Siliceous Pebbly sandstones (RRT2), and 3) Argillaceous to Calcareous sandstones (RRT3), and 4) Calcareous to Dolomitic Sandstones (RRT4). Based on the petrophysical interpretation and ranking for the studied samples, samples of RRT2 have very good rank due to presence of very good porosity, excellent permeability, and good to very good RQI and FZI values which are assigned as the best reservoir rock type in the Tortonian-Serravallian sequence. The petrophysical behavior of RRT4 shows the lowest reservoir quality (impervious RQI and poor FZI). The examined sequence has been sliced into reservoir zones; each zone has its hydraulic flow unit range that represents the studied rock types in each zone. Based on the applied pressure tools, including Repeat Formation Tester (RFT) and the Modular Formation Dynamic Tester (MDT), both the Wakar and Sidi Salem reservoirs represent completely isolated compartments. Therefore, ranking both reservoirs and estimating their hydrocarbon potential must be done separately for each reservoir.

Permeability Estimation by Using the Modified and Conventional FZI Methods

There many methods for estimation of permeability. In this Paper, permeability has been estimated by two methods. The conventional and modified methods are used to calculate flow zone indicator (FZI). The hydraulic flow unit (HU) was identified by FZI technique. This technique is effective in predicting the permeability in un-cored intervals/wells. HU is related with FZI and rock quality index (RQI). All available cores from 7 wells (Su -4, Su -5, Su -7, Su -8, Su -9, Su -12, and Su -14) were used to be database for HU classification. The plot of probability cumulative of FZI is used. The plot of core-derived probability FZI for both modified and conventional method which indicates 4 Hu (A, B, C and D) for Nahr Umr formation based on the four straight lines. The permeability was calculated by two methods for comparison and choosing the best. The modified FZI method gives better results because the predicted permeability by this method demonstrates a coefficient of correlation (R2) higher than that of the conventional approach, where the value of R2 is 0.9645 of modified FZI method while 0.892 of the conventional approach. When plotting RQI versus on a log-log scale, all core samples with similar FZI values will lie on a straight line with a unit slope. Other core samples that have different FZI values will lie on other parallel lines. All lines in (RQI and ) plot of modified FZI method have unit slop and more parallel than these of the conventional approach. The plot of probability cumulative of FZIm is used to determine number of hydraulic flow unit for Nahr Umr formation. The plot of core-derived probability FZI for both modified and conventional method which indicates 4 Hus for Nahr Umr formation based on the four straight lines, these four straight lines of modified FZI method was more distinguished than these of the conventional approach.

Factors influencing oil saturation and exploration fairways in the lower cretaceous Quantou Formation tight sandstones, Southern Songliao Basin, China

Favorable exploration fairway prediction becomes crucial for efficient exploration and development of tight sandstone oil plays due to their relatively poor reservoir quality and strong heterogeneous oil saturation. In order to better understand the factors influencing oil saturation and favorable exploration fairway distribution, petrographic investigation, reservoir properties testing, X-ray diffraction analysis, oil saturation measurement, pressure-controlled mercury injection, and rate-controlled mercury injection were performed on a suite of tight reservoir from the fourth member of the Lower Cretaceous Quantou Formation (K1q4) in the southern Songliao Basin, China. The sandstone reservoirs are characterized by poor reservoir properties and low oil saturations. Reservoir properties between laboratory pressure conditions and in situ conditions are approximately the same, and oil saturations are not controlled by porosity and permeability obviously. Pores are mainly micro-scale, and throats are mainly nano-scale, forming micro- to nano-scale pore–throat system with effective connected pore–throat mainly less than 40%. Oil emplacement mainly occurs through the throats with average radius larger than 0.25 µm under original geological condition. Moreover, the samples with higher oil saturation show more scattered pore and throat distributions, but centered pore–throat radius ratio distribution. Pore–throat volume ratio about 2.3–3.0 is best for oil emplacement, forming high oil saturation. Quartz overgrowth, carbonate cements, and authigenic clays are the major diagenetic minerals. The reservoirs containing about 4–5% carbonate cements are most preferable for oil accumulation, and oil saturation increases with increasing of chlorite as well. The flow zone indicator is a reasonable parameter to predict favorable exploration targets in tight sandstone reservoirs. The reservoirs with flow zone indicator values larger than 0.05 can be regarded as favorable exploration targets in the K1q4 tight sandstones. According to the planar isoline of average flow zone indicator value, the favorable exploration targets mainly distribute in the delta plain distributary channel and deltaic front subaqueous distributary channel.

Investigation on the pore structure and multifractal characteristics of tight oil reservoirs using NMR measurements: Permian Lucaogou Formation in Jimusaer Sag, Junggar Basin

Pore structure is a critical parameter to estimate the reservoir quality and evaluate the resource potential. However, the pore structure and heterogeneity of the unconventional tight oil reservoirs of Permian Lucaogou Formation of Jimusaer Sag, Junggar Basin are not studied well. In this paper, the multifractal analysis based on the NMR T2 distributions was applied to investigate the pore structure and heterogeneity. The reservoir quality index (RQI) and flow zone indicator (FZI) were calculated using porosity and permeability. Two parameters T35 and T50 were newly defined as relaxation times corresponding to the 35% and 50% saturation on the T2 reverse accumulative curve, respectively. The results showed that the RQI, and FZI values were extremely low, and that the NMR T2 distributions of water saturated samples had much short relaxation components with no or little long relaxation components, indicating a complex and poor microscopic pore structure of tight oil rocks. The typical multifractal characteristics, such as the large values of singularity strength range (Δα), revealed that the pore structures of samples were strongly heterogeneous. It was also found that the clay contents have an obvious influence on the multifractal parameters, and the FZI and new defined T35 are two good indicators for heterogeneity of pore structure. In addition, the feasibility and applicability of calculating the multifractal parameters from NMR and other well logs were discussed.

Heterogeneities and Intra Sand-Body Compartmentalization in Late Oligocene Delta-Front Deposit, Niger Delta, Nigeria

This paper presents the study of the heterogeneity in lithofacies, porosity, permeability, mineral grain density, pore-throats sizes and hydraulic rock types and the intra sand-body compartmentalization in cored delta-front deposit of Greater Ughelli depobelt of Niger Delta. Sedimentological study of core samples results in the identification of nine lithofacies and the interpretation of environments of deposition as mainly proximal and distal delta-front mouth bar. The sand unit indicates moderate to excellent reservoir quality with core porosity between 16.2 and 29.5% and permeability between 16.8 and 7,560 md. Dykstra-Parsons core permeability distribution coefficient of 0.97 indicates that the studied reservoir sand-body is vertically highly heterogeneous, with high potential for vertical intra sand-body compartmentalization. Graphical cluster analysis of Flow Zone Indicators (FZI) led to the identification of nine Hydraulic Flow Units (HFU) with distribution controlled by depositional facies. The prediction of permeability, porethroats (r35) and flow zone indicator values of the reservoir in an uncored-well using predictive mathematical models developed with multiple regression analysis enabled the inter-well correlation of hydraulic flow units and indicates lateral continuity of reservoir compartments. An intra sand-body compartmentalization evaluated with core permeability values, Winland r35 coefficients and flow zone indicators was corroborated with formation pressure data analysis. Results show that the studied delta front-mouth bar reservoir is vertically compartmentalized by intercalated shales. Fractures in over-pressured zones were found to reduce vertical reservoir fluid compartmentalization by intercalated shales. However, reduction in reservoir pressure due to hydrocarbon production can result in dynamic compartmentalization and consequently, reduction in hydrocarbon recovery.

Tight-Gas-Sand Permeability Estimation From Nuclear-Magnetic-Resonance (NMR) Logs Based on the Hydraulic-Flow-Unit (HFU) Approach

Summary The crossplot of porosity vs. Klinkenberg permeability (PERM) for 378 core samples, drilled from tight gas sands in the Xujiahe formation of the Anlu region-central Sichuan basin, southwest China, showed that tight-gas-sand permeability cannot be estimated effectively from porosity directly because only a poor relationship can be found between core-derived porosity and permeability because of the strong heterogeneity, especially for reservoirs with dominant microfractures (with porosities lower than 6.5%). However, the problem can be solved by introducing the HFU approach. In this paper, the 378 core samples were divided into five types on the basis of the difference of the flow-zone indicator (FZI), and then relationships of rock porosity and permeability were established for every type of core sample. By virtue of the analysis of the expression of FZI and the classical Schlumberger Doll Research (SDR) Center model, a novel technique used to obtain FZI from NMR logs was proposed and a corresponding model was established. The model parameters were calibrated by use of the laboratory NMR measurements of 54 plug samples taken from the Xujiahe formation. Carried out on the experimental data sets, this procedure can be extended to reservoir conditions to estimate consecutive formation permeability along the intervals through which NMR logs were acquired. The processing results of field examples illustrate that the calculated FZI values from field NMR logs match very well with the core analyzed results; the absolute errors among them are within the scope of 60.15. Moreover, permeability, estimated by use of the proposed technique and the core analyzed results are consistent. However, the calibrated SDR model is exclusive to the cases where formation permeability ranges from 0.2 to 0.4 md. To improve permeability prediction with the SDR model, many more core samples drilled from formations with dominant microfractures needed to be tested for laboratory NMR experiments to calibrate the SDR model for each HFU.