Flow Zone Indicator Research Articles

Rock Typing and Characterization of the Late Cretaceous Abu Roash "G" Reservoirs at East Alam El-Shawish Field, Western Desert, Egypt

Rock typing and petrophysical characterization play a vital role in constructing reservoir models for petroleum exploration and development. This study focuses on evaluating the petrophysical characteristics of the Late Cretaceous Abu Roash "G" Reservoirs at the East Alam El Shawish field in Egypt's Western Desert. The study involved five vertical wells and employed various techniques and analyses to investigate the reservoir. Lithology determination utilizing well logs and core analysis helps identify the lithology types and corresponding porosity of the Abu Roash "G" reservoirs. Sandstone and limestone lithologies with varying porosity ranges were identified, along with the influence of shale on neutron porosity values. Facies analysis of the Abu Roash "G" Member identified seven lithofacies types, categorized into shallow marine and deeper marine depositional environments. The petrophysical analysis involves evaluating gamma-ray logs, porosity, permeability, flow zone indicator (FZI), and reservoir quality index (RQI) values for each lithofacies type. This analysis classifies the core samples into seven reservoir rock types (RRT1 to RRT7) based on petrophysical attributes, providing a clear classification of the Abu Roash "G" reservoir interval. RRT1, RRT2, and RRT3 exhibit the highest reservoir quality, while RRT4 and RRT5 indicate moderate reservoir quality. RRT6 and RRT7 exhibit low reservoir quality due to unfavorable petrophysical behavior. The findings of this study provide valuable insights into the Abu Roash "G" reservoir, including its lithofacies, reservoir properties, and depositional environments. This knowledge is crucial for reservoir characterization and optimizing oil production strategies in the region.

Evaluating Petrophysical Properties of Sa'di Reservoir in Halfaya Oil Field

The petrophysical characteristics of five wells drilled into the Sa'di Formation in the Halfaya oil field were evaluated using IP software to determine a reservoir and explore hydrocarbon reserve zones. The lithology was evaluated using the M-N cross-plot method. The diagram showed that the Sa'di Formation was mainly composed of calcite (represented by the limestone region) is the main mineral in the Sa′di Reservoir. Using a density-neutron cross plot to identify the lithology showed that the formation mainly consists of limestone with minor shale. Gamma-ray logs were employed to calculate the shale quantity in each well. The porosity at weak hole intervals was calculated using a sonic log and neutron-density log at the reservoir unit. Additionally, Archie's equation is applied to calculate fluid saturation using resistivity data. The reservoir water saturation in the uninvaded zone is calculated using Archie's equation, which determines the most essential element utilized in log evaluation. Finally, the permeability was measured using a flow zone indicator. The results indicate that the limestone and shale that the cuttings description report enhanced comprise most of the Sa'di reservoir. At the HF-316 and HF-21 wells, the core porosity was verified. In an uninvaded zone, the Archie equation offers the best estimation. Three equations were derived from the core data's porosity-permeability connection using a cross-plot of the reservoir quality index and normalized porosity index. According to the general interpretation, zones B1, B2, and B3 contain the most hydrocarbons; however, the B2 zone, the best layer in the Sa'di reservoir, has the highest hydrocarbon concentration. This is close to previous studies and field results. Finally, Sa'di A is formed mainly of mud lime and contains no hydrocarbons. As a result, Sa'di A was separated into A1 and A2 based on the percentage of shale difference.

Deep learning prediction of waterflooding-based alteration of reservoir hydraulic flow unit

The hydraulic flow unit (HFU) is a comprehensive representation of reservoir quality, and the type of reservoir HFU alters dramatically due to the impact of waterflooding. The accurate prediction of HFU characteristic alterations based-waterflooding is crucial but difficult because the alterations are influenced by many factors like geological conditions and production history. In this study, we propose a deep learning-based method using both static geological data and dynamic production history data to predict HFU characteristic alterations waterflooding-based. We classify six HFU types using the k-means clustering of geological variables, including the flow zone indicator (FZI), permeability, and shale volume. The deep-learning model uses the initial static geological variables, including FZI, permeability, and shale volume, and dynamic production data as inputs, whereas the HFU type after waterflooding is used as the target. Convolutional neural networks (CNNs) are adopted for the prediction of HFU characteristic alterations and also compared with support vector machines (SVM) and back propagation neural networks (BPNNs). The prediction accuracy of the CNN model is 93.3% and 87.1% for the validation and testing datasets, respectively, which performs much better than other machine learning methods. In addition, we explore the factors affecting the performance of different models. CNN model can extract useful information from complicated production history data more effectively than other machine learning methods. Finally, the trained CNN model is used to predict the spatial distribution of HFU at different development stages in the study area. In summary, the proposed method can accurately predict dynamic alterations in the reservoir HFU type after waterflooding, which is very useful for the adjustment of oilfield production measures.

Permeability Estimation for Upper Shale Member in Southern Iraqi Oil Field Using Machine learning and Hydraulic Flow Units Methods

Permeability estimation in the Upper Shale Member is rather complex due to the heterogeneity. Conventional logs do not provide permeability directly, but the core data does. Core operations and analysis processing are all costly. An alternative method is used by dividing the reservoir rock into groups (rock types) with similar characteristics in porosity and permeability for cored wells. These identified groups for the core wells can be applied to the uncored wells to predict their groups and permeabilities. Four wells were used in this study: one cored well and three uncored wells. Two classification methods of rock types were used; the first is the machine learning method (supervised and unsupervised), a new technics invention, and the second method is the hydraulic flow unit. Four groups were selected by using Machine Learning and hydraulic flow unit methods. A regression equation for each rock type was obtained to predict permeability. Then, establishing a relationship between the results of the core data groups and the logs' responses for the core well is referred to as "correlation," which is training the logs that provide an estimating model. A validation check was completed by comparing the rock type and permeability estimation from the core with the rock type and permeability estimation from the logs, which were highly matched. Finally, The rock type and permeability can be estimated for the uncored wells using only the logs that were learned from the generated training data set for the cored wells. Due to the use of more than one model to predict permeability, it was clear that one was more compatible than the other. The flow zone indicator, a classical method, still has a better rock type and permeability estimation than the new techniques, which use data mining and mathematical algorithms on a clustering basis.

Application of unsupervised learning and deep learning for rock type prediction and petrophysical characterization using multi-scale data

This study integrates well log data, routine core analyses, microcomputed X-ray tomography (μCT) images, and sedimentary petrography to accurately characterize and evaluate the carbonate reservoirs of the Barra Velha formation (Aptian) of the Santos Basin within Brazilian pre-salt region. In these carbonate reservoirs, the porous system is extremely diverse and variable, making it challenging to establish rock typing with comparable petrophysical properties. Based on this integrated study, the reservoir sequences were characterized and a precise definition of four reservoir rock types (RRTs) was performed by integrating the petrophysical values from the plugs and their corresponding well log data of two cored wells using K-means unsupervised classification algorithm. The classification results were combined with various conventional techniques to evaluate the quality and geological characteristics of the studied sequence. This evaluation encompassed different parameters such as flow and storage capacity, reservoir quality index, flow zone indicator, pore spaces interpretation, and average pore and throat radius. The study involved a detailed analysis of thin sections to identify various facies, including shrubstones, reworked, and spherulitestone, and to classify various forms of porosity such as interparticle, intraparticle, intercrystalline, vug, moldic, fracture, and growth framework porosity. Pore Network Modeling from μCT analysis of plugs was used specifically for the characterization of pores and throats of plug samples from each RRT. These datasets were utilized as supporting evidence to offer a more accurate and inclusive knowledge of reservoir quality. The study aimed to develop predictive models by implementing deep learning and machine learning algorithms trained on well log data to estimate plug porosity and rock type. Two deep learning models, ResNet and 1D CNN, were trained and evaluated for plug porosity prediction, with the 1D CNN model showing superior performance. Additionally, the XGBoost algorithm was applied to predict rock type, achieving high accuracy on both the training and validation datasets. The predicted results were compared with actual data to evaluate the effectiveness of the models and were then utilized to estimate plug permeability values. The results demonstrate the potential of deep learning and machine learning approaches in reservoir characterization and management, enabling the evaluation of subsurface reservoir properties even with incomplete datasets, which could lead to an improved understanding of the reservoir properties and better management of the reservoir. This integrated study provides deeper insight into the complex reservoir properties and can help improve decision-making processes and optimize management and production strategies in the challenging pre-salt carbonate reservoirs or similar complex reservoirs.

Integrating hydraulic flow unit concept and adaptive neuro-fuzzy inference system to accurately estimate permeability in heterogeneous reservoirs: Case study Sif Fatima oilfield, southern Algeria

Formation characteristics are a crucial requirement for reservoir modeling and they need to be constantly updated as new static and/or dynamic reservoir-property details become available. This improves reservoir interpretation while achieving a good history-matching for a better representation of the reservoir. This paper proposes a new method to estimate reservoir permeability in an oil-producing reservoir by applying two distinct cases: partially cored and completely non-cored wellbores. The approach involves the determination of hydraulic-flow units (HFU) using a flow-zone indicator (FZI) distribution to consider the dynamic aspect of the permeability, which strongly influences fluid flow through porous media. The FZI is clustered by normal probability analysis to identify the optimum number of HFU clusters. The estimated reservoir permeability is validated in static and dynamic reservoir models. For an accurate prediction of the FZI over the entire reservoir thickness, an adaptive-neuro-fuzzy-inference system (ANFIS) is developed using conventional well-log variables: (i) gamma-ray (GR), (ii) sonic (DT), (iii) density (RHOB), (iv) deep resistivity (DLL), and (iv) neutron porosity (NPHI) calibrated with the available core data. The static regression parameters root-mean-squared error (RMSE), mean absolute error (MAE), coefficient of determination (R2), and R2adjusted are used to evaluate the performance of the ANFIS model in predicting FZI to determine HFU for estimating reservoir permeability. To validate this approach, a matching quality check of the downhole and reservoir pressure is carried out using a dynamic reservoir simulation model. Historical data collected from wellbore OPW-1 drilled in the Sif Fatima oilfield (Algeria) are used to evaluate the accuracy of the proposed permeability-prediction framework. The ANFIS model can accurately and efficiently predict the FZI exhibiting R2, R2adjusted, RMSE, and MAE of approximately (≈0.981, ≈0.979, ≈0.014, ≈4,600E-08)), (≈0.933, ≈0.891, ≈0.032, ≈1.615E-03), and (≈0.970, ≈0.968, ≈0.018, ≈2.381E-04)) for the training, testing and complete dataset, respectively. When applied to predict reservoir permeability, the Hydraulic Flow Unit concept was able to generate accurate results from the predicted FZI data by applying the modified Kozeny-Carman equation with, R2, RMSE and MAE values of approximately ≈0.9702, ≈0.969, ≈9.604, and ≈4.398, respectively. The calculated permeability was validated successfully at well and field levels. Additionally, it generated more accurate history matches of the well and the reservoir pressure trends than the previous permeability model, and consequently improved the reservoir modelling workflow.

Reservoir quality evaluation of the Narimba Formation in Bass Basin, Australia: Implications from petrophysical analysis, sedimentological features, capillary pressure and wetting fluid saturation

The evaluation of reservoir quality was accomplished on the Late Paleocene to Early Eocene Narimba Formation in Bass Basin, Australia. This study involved combination methods such as petrophysical analysis, petrography and sedimentological studies, reservoir quality and fluid flow units from derivative parameters, and capillary pressure and wetting fluid saturation relationship.Textural and diagenetic features are affecting the reservoir quality. Cementation, compaction, and presence of clay minerals such as kaolinite are found to reduce the quality while dissolution and secondary porosity are noticed to improve it. It is believed that the Narimba Formation is a potential reservoir with a wide range of porosity and permeability. Porosity ranges from 3.1% to 25.4% with a mean of 15.84%, while permeability ranges between 0.01 mD and 510 mD, with a mean of 31.05 mD. Based on the heterogenous lithology, the formation has been categorized into five groups based on permeability variations. Group I showed an excellent to good quality reservoir with coarse grains. The impacts of both textural and diagenetic features improve the reservoir and producing higher reservoir quality index (RQI) and flow zone indicators (FZI) as well as mostly mega pores. The non-wetting fluid migration has the higher possibility to flow in the formation while displacement pressure recorded as zero. Group II showed a fair quality reservoir with lower petrophysical properties in macro pores. The irreducible water saturation is increasing while the textural and digenetic properties are still enhancing the reservoir quality. Group III reflects lower quality reservoir with mostly macro pores and higher displacement pressure. It may indicate smaller grain size and increasing amount of cement and clay minerals. Group IV, and V are interpreted as a poor-quality reservoir that has lower RQI and FZI. The textural and digenetic features are negatively affecting the reservoir and are leading to smaller pore size and pore throat radii (r35) values to be within the range of macro, meso-, micro-, and nano pores. The capillary displacement pressure curves of the three groups show increases reaching the maximum value of 400 psia in group V. Agreement with the classification of permeability, r35 values, and pore type can be used in identifying the quality of reservoir.

A new method in reservoir rock classification in carbonate and sandstone formations

Abstract This study aims to improve rock-type classification by analyzing core data based on the water–oil primary drainage capillary pressure method. A modified empirical equation is proposed using permeability, porosity, and irreducible water saturation to classify rock types based on water–oil primary drainage capillary pressure. We used primary drainage capillary pressure data measured in carbonate and sandstone samples in the Ahvaz Asmari and Mansouri oilfields to evaluate the characterization number (Cn) method. This study consists of two main parts. First, the Cn method is introduced to rock typing, and the permeability is calculated from well log data. In the second part, we present rock-type classification when the water saturation of the formation is more than the irreducible water saturation. The novelty of this work is a simple and efficient technique to rock-type classification using the Cn method. In addition, we present a procedure to assign rock types for the transition zone using the Cn method. Moreover, this study systematically investigates the role of irreducible water saturation in rock typing. The innovation of this work lies in its ability to classify rocks in heterogeneous reservoirs for carbonate and sandstone lithology and allow for the calculation of permeability more accurately from well log data. The comparison results between the Cn method and the flow zone indicator method show the robust clustering ability of the Cn method.

An integrated workflow for seismic interpretation, petrophysical and petrographical characterization for the clastic Mangahewa reservoir in Pohokura gas field, Taranaki Basin, New Zealand

This research aims at delineating the main structural elements controlling the geologic setting of Taranaki Basin in New Zealand which in turn control the hydrocarbon potential of this basin and in particular of Pohokura Field which is considered one of the main gas-producing fields in the basin. This can be achieved utilizing the seismic data including three different marine seismic data sets covering 514.6 km2 with 12.5 m horizontal resolution. A synthetic seismogram and a set of seismic attributes were created to achieve this target. In addition, an integrated reservoir characterization study is applied to the Mangahewa reservoir in the Pohokura Field. This integrated characterization includes a detailed petrographical lithofacies analysis, well logging, and core analysis studies. Based on the petrographical study, eight lithofacies are described in Pohokura Field: 1) Laminated sandstone; 2) Massive sandstones; 3) Granular laminated sandstones; 4) Bioturbated sandstones; 5) Sand dominated and mud rich bioturbated sandstones; 6) Muddy bioturbated sandstone; 7) Laminated and rippled laminated mud rocks, and 8) Coal lithofacies which have been deposited in high energy shoreface setting. On the other side, the available well logging data includes caliper, GR (gamma-ray), sonic, neutron, density, and resistivity (shallow and deep resistivities). Logging interpretation enabled the estimation of the effective porosity (av. ∅ = 11.4%), hydrocarbon saturation (av. Shc = 62.87%), and the shale volume (av. VSh = 18.02%) with a total net pay thickness of all wells = 196 m. The available core data includes porosity, density, and permeability which are used to estimate the reservoir quality ranks; the effective pore radius (R35); the flow zone indicator, the discrete rock type (DRT), and the reservoir quality index (RQI). Considering the porosity, permeability, and the DRT values, the available core data are grouped into five reservoir rock types (RRTs) with the best reservoir quality assigned for the RRT1 (bioturbated, laminated and massive sandstones), while RRT5 has the lowest reservoir quality (muddy bioturbated sandstone, Granulated laminated sandstone, and laminated and rippled mudstone). This integrated study is applicable and extendable to the other basins in New Zealand and the southwestern Pacific. The proposed workflow is also applicable to the other analogs in the various basins elsewhere.

Moveability and Nature of the Reservoir Hydrocarbons in the Lower Miocene Jeribe Formation in X-Oilfield, Northern Iraq

The flow capacity of the Lower Miocene Jeribe Formation was investigated in the two selected wells, A and B, of an oilfield northeast of Tikrit City. The available wireline logs and core test data are adapted for characterizing the reservoir potentiality of the formation. Three Hydraulic Flow Units was identified by calculating the values of the Flow Zone Indicators. Most parts of the formation parts have the lowest flow capacity among the three determined flow types. The Jeribe Formation contains hydrocarbons in various ratios in both wells of the study. The Reservoir Unit RUJ-1 at the upper part of the formation has the highest moveable hydrocarbon saturation, especially in the well A. Most hydrocarbons in the RUJ-3 at the lower part of the formation in both wells are immoveable. The oil-water contact was expected within Dhiban Formation in the well B and was expected to be somewhere in the underlying Dhiban or Euphrates Formations in the well A. The nature of the hydrocarbons at the uppermost few meters of the formation consists of gas or light hydrocarbons and oil at the rest of the formation(residual and moveable oils).

Integrated Core and Log Data to Determine the Reservoir Flow Unit and Rock Facies for Mishrif Formation in South Eastern Iraq

This work represents study the rock facies and flow unit classification for the Mishrif carbonate reservoir in Buzurgan oil Field, which located n the south eastern Iraq, using wire line logs, core samples and petrophysical data (log porosity and core permeability). Hydraulic flow units were identified using flow zone indicator approach and assessed within each rock type to reach better understanding of the controlling role of pore types and geometry in reservoir quality variations. Additionally, distribution of sedimentary facies and Rock Fabric Number along with porosity and permeability was analyzed in three wells (BU-1, BU-2, and BU-3). The interactive Petrophysics - IP software is used to assess the rock fabric number, flow zones, and reservoir quality index. The Mishrif Formation has four distinct rock types and groups that have been recognized using the approach. The first rock type represents a poor reservoir quality (mud-dominated fabrics) with rock-fabric numbers equals to 2.5-4, the second rock type reveals a moderate reservoir quality (wackestone-Packstone Microfacies), the third rock type depicts a good reservoir quality (grain-dominated packstone) with rock-fabric numbers equals to 1.5-2.5 and 0.5-1.5.

Prediction of Hydraulic Flow Units for Jeribe Reservoir in Jambour Oil Field Applying Flow Zone Indicator Method

The Jeribe reservoir in the Jambour Oil Field is a complex and heterogeneous carbonate reservoir characterized by a wide range of permeability variations. Due to limited availability of core plugs in most wells, it becomes crucial to establish correlations between cored wells and apply them to uncored wells for predicting permeability. In recent years, the Flow Zone Indicator (FZI) approach has gained significant applicability for predicting hydraulic flow units (HFUs) and identifying rock types within the reservoir units. This paper aims to develop a permeability model based on the principles of the Flow Zone Indicator. Analysis of core permeability versus core porosity plot and Reservoir Quality Index (RQI) - Normalized porosity log-log plot reveals the presence of three distinct Hydraulic Flow Units and corresponding rock types within the Jeribe reservoir. These rock types can be identified if known. The reservoir can be divided into three groups of rock types, namely good, moderate, and bad quality. The bad rock type represents a restricted section within the reservoir, while the upper and lower parts predominantly consist of moderate-quality rock types. Conversely, the central section of the reservoir exhibits a good-quality rock type. By utilizing the Flow Zone Indicator principles, this study provides valuable insights into the hydraulic flow behavior and rock types present in the Jeribe reservoir. The proposed permeability model derived from this method can aid in predicting permeability values for uncored wells, contributing to a better understanding of the reservoir's heterogeneity and facilitating reservoir characterization and management decisions.

Determination of Reservoir Hydraulic Flow Units and Permeability Estimation Using Flow Zone Indicator Method

Reservoir characterization plays a crucial role in comprehending the distribution of formation properties and fluids within heterogeneous reservoirs. This knowledge is instrumental in constructing an accurate three-dimensional model of the reservoir, facilitating predictions regarding porosity, permeability, and fluid flow distribution. Among the various methods employed for reservoir characterization, the hydraulic flow unit stands out as a widely adopted approach. By effectively subdividing the reservoir into distinct zones, each characterized by unique petrophysical and geological properties, hydraulic flow units enable comprehensive reservoir analysis. The concept of the flow unit is closely tied to the flow zone indicator, a critical parameter that defines the porosity-permeability relationships of each hydraulic flow unit. Additionally, the flow zone indicator method proves valuable in estimating permeability accurately. In this study, we demonstrate the application of the flow zone indicator method to determine hydraulic flow units within the Khasib formation. By analyzing core data and calculating the Rock Quality Index (RQI) and Flow Zone Indicator (∅Z), we differentiate the formation into four hydraulic flow units based on FZI values. Specifically, HFU 1 represents a rock of poor quality, corresponding to compact and chalky limestone. HFU 2 represents intermediate quality, corresponding to argillaceous limestone, while HFU 3 represents good quality, corresponding to porous limestone. Lastly, HFU 4 signifies an excellent reservoir rock quality characterized by vuggy limestone. By establishing a permeability equation that correlates with effective porosity for each rock type, we successfully estimate permeability. Comparing these estimated permeability values with core permeability reveals a strong agreement with a high correlation coefficient of 0.96%. Consequently, the flow zone indicator method effectively classifies the Khasib formation into four distinct hydraulic flow units and provides an accurate and reliable means of determining permeability in the reservoir. The resulting permeability equations can be applied to wells and depth intervals lacking core measurements, further emphasizing the practical utility of the FZI method.

Prediction of cementation factor using different methods of reservoir rock classification in heterogeneous carbonate reservoirs

Cementation exponent or factor is one of most crucial factors in the Archie’s equations that should be estimated precisely in order to accurately determine relevant petrophysical characteristics of the reservoir. Inaccurate estimation of this factor leads to incorrect petrophysical analysis and inaccurate determination of water saturation which is highly important for the economic forecasting of hydrocarbon reservoirs. An alternative to the cementation factor precise estimation is to classify rocks based on a common petrophysical property such as permeability. This research aims to investigate the cementation factor in different rock types and eventually to provide a suitable model for estimating this parameter based on different approaches of rock classification. In this paper, first, conventional methods of rock classification (e.g., current zone indicator (CZI), flow zone indicator (FZI), permeability and electrical efficiency) have been used for cementation factor estimation. Then, the data, taken from Regnet et al. (J Geophys Res Solid Earth 120:790–811, 2015), have been analyzed by these methods in order to find the best way of rock classification. The analyzed data show that only electrical efficiency method is accurate enough to classify rocks into different groups as high values of determination coefficient (i.e., 0.9828 and 0.9725) are obtained as a result of classification by this method in which the data are classified into distinct groups. The obtained values of determination coefficient as a result of classification by permeability, FZI and CZI methods are 0.805, 0.809 and 0.7568 respectively. Due to this result and high scattering of the classified data, it can be concluded that these three methods have not been precise enough for rock classification of the data. In addition to the above-mentioned existing methods, a new rock classification method based on tortuosity coefficient or factor has also been proposed in this paper. This new method has been proven to be accurate due to low scattering of the data results and falling them into distinct groups in addition to obtaining high values of determination coefficient (i.e., 0.9996, 0.9986 and 0.9951) in the classification by this new method.

Implication of the micro- and lithofacies types on the quality of a gas-bearing deltaic reservoir in the Nile Delta, Egypt

The Messinian Qawasim sequence, as one of the most important reservoirs in the Egyptian Nile Delta, represents a typical gas-bearing deltaic reservoir sequence. It aims at delineating the implication of the litho- and microfacies associations on the reservoir characteristics of the different stages of the fluvial deltas including the prodelta, proximal/distal delta front, and the delta plain depositional sequences. The studied reservoir sequence was divided into two units; upper and lower clastic units. The petrophysical properties of these two units were studied on the borehole scale using the gamma-ray, caliper, sonic, density, neutron, and resistivity logs to estimate the reservoir parameters including the total and effective porosities, water saturation, shale volume, and the net-pay thickness. For more details, they are also examined on the plug-scale using the core dataset including helium porosity, density, permeability, and fluids saturations, where the flow zone indicator, the reservoir potentiality index, the reservoir quality index, and the average reservoir pore radius were then estimated. The entire sequence is divided into five reservoir rock types (RRT1-RRT5) where, the best quality is assigned to the first RRT (the upper reservoir unit), and the lowest quality is assigned to the RRT5 of the lower reservoir unit. Based on core description and the petrographical studies five lithofacies and four microfacies have been identified. The lithofacies are (1) laminated mudstones/siltstones, (2) ripple laminated sandstones, (3) cross-laminated sandstones, (4) cross-bedded sandstones, and (5) pebbly massive sandstones. These lithofacies are primarily composed of four microfacies; sublithic arenites, subarkose arenite, glauconitic quartz wacke, and sandy mudstone/siltstone. Based on this study, the pebbly massive and the cross-bedded sandstones of the upper unit which is composed of the sublithic and subarkose arenites (RRT1-RRT2) have the best reservoir quality. On the other hand, the lowest quality is assigned to the RRT5 (sandy mudstone/siltstone microfacies) which is represented on the macro scale by the laminated mudstones/siltstones lithofacies. The integration between multi-scale datasets (core-well, petrography, well logs, and seismic) gives a precise picture of the deltaic Qawasim reservoir rock units in the Nile Delta. This workflow has never been applied to the deltaic system in the onshore Nile delta and North Africa. Thereby, this study is considered a standard case study for the deltaic sequences and its proposed workflow is applicable to the Nile Delta elsewhere and worldwide for similar reservoir sequences.

Studying the Effect of Permeability Prediction on Reservoir History Matching by Using Artificial Intelligence and Flow Zone Indicator Methods

The map of permeability distribution in the reservoirs is considered one of the most essential steps of the geologic model building due to its governing the fluid flow through the reservoir which makes it the most influential parameter on the history matching than other parameters. For that, it is the most petrophysical properties that are tuned during the history matching. Unfortunately, the prediction of the relationship between static petrophysics (porosity) and dynamic petrophysics (permeability) from conventional wells logs has a sophisticated problem to solve by conventional statistical methods for heterogeneous formations. For that, this paper examines the ability and performance of the artificial intelligence method in permeability prediction and compared its results with the flow zone indicator methods for a carbonate heterogeneous Iraqi formation. The methodology of the research can be Summarized by permeability was estimated by using two methods: Flow zone indicator and Artificial intelligence, two reservoir models are built, where the difference between them is in permeability method estimation, and the simulation run will be conducted on both of the models, and the permeability estimation methods will be examined by comparing their effect on the model history matching. The results showed that the model with permeability predicted by using artificial intelligence matched the observed data for different reservoir responses more accurately than the model with permeability predicted by the flow zone indicator method. That conclusion is represented by good matching between observed data and simulated results for all reservoir responses such for the artificial intelligence model than the flow zone indicator model.

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).

Electrical Tortuosity Index: A New Approach for Identifying Rock Typing to Enhance Reservoir Characterization Using Well-Log Data of Uncored Wells.

The world is gradually moving toward a severe energy crisis, with an ever-increasing demand for energy overstepping its supply. Therefore, the energy crisis in the world has shed important light on the need for enhanced oil recovery to provide an affordable energy supply. Inaccurate reservoir characterization may lead to the failure of enhanced oil recovery projects. Thus, the accurate establishment of reservoir characterization techniques is required to successfully plan and execute the enhanced oil recovery projects. The main objective of this research is to obtain an accurate approach that can be used to estimate rock types, flow zone indicators, permeability, tortuosity, and irreducible water saturation for uncored wells based on electrical rock properties that were obtained from only logging tools. The new technique is obtained by modifying the Resistivity Zone Index (RZI) equation that was presented by Shahat et al. by taking the tortuosity factor into consideration. When true formation resistivity (Rt) and inverse porosity (1/Φ) are correlated on a log-log scale, unit slope parallel straight lines are produced, where each line represents a distinct electrical flow unit (EFU). Each line's intercept with the y-axis at 1/Φ = 1 yields a unique parameter specified as the Electrical Tortuosity Index (ETI). The proposed approach was validated successfully by testing it on log data from 21 logged wells and comparing it to the Amaefule technique, which was applied to 1135 core samples taken from the same reservoir. Electrical Tortuosity Index (ETI) values show marked accuracy for representing reservoir compared with Flow Zone Indicator (FZI) values obtained by the Amaefule technique and Resistivity Zone Index (RZI) values obtained by the Shahat et al. technique, with correlation coefficients of determination (R2) values equal to 0.98 and 0.99, respectively. Hence, by using the new technique, the Flow Zone Indicator, permeability, tortuosity, and irreducible water saturation were estimated and then compared with the obtained results from the core analysis, which showed a great match with the R2-values of 0.98, 0.96, 0.98, and 0.99, respectively.

Pore structure characterization based on NMR experiment: A case from the Shanxi Formation tight sandstones in the Daning-Jixian area, eastern Ordos Basin

The study of pore structure requires consideration of important factors including pore throat size, pore radius composition, and pore-throat configuration. As the nuclear magnetic resonance (NMR) experimental results contain rich information about pore structures and fluid occurrence states, this study investigated the pore structures of the tight sandstone reservoirs of the Shanxi Formation in the Daning-Jixian area, eastern Ordos Basin. Firstly, by making the inverse cumulative curve of the NMR T2 spectrum coincide with the capillary pressure curves which were obtained by the mercury injection capillary pressure (MICP) technique, this study derived a conversion coefficient that can be used to convert the NMR T2 spectrum into the pore throat radius distribution curves based on the NMR experimental results. Subsequently, we determined the pore radius intervals corresponding to irreducible water distribution using the NMR-derived pore radius distribution curves. Finally, the NMR T2 distribution curves based on the fractal theory were analyzed and the relationships between fractal dimensions and parameters, including permeability, porosity, reservoir quality index (RQI), flow zone indicator (FZI), irreducible water saturation, RT35, and RT50, were also discussed. The NMR-derived pore throat radius distribution curves of the study area are mainly unimodal, with some curves showing slightly bimodal distributions. The irreducible water mainly occurs in small pores with a pore radius less than 100 nm. As the permeability decreases, the contribution rate of small pores to the irreducible water gradually increases. The NMR-based fractal dimensions of pores show a two-segment distribution. Small pores have small fractal dimensions and are evenly distributed, while large pores have large fractal dimensions and complex pore structures. The fractal dimension of large pores (Dmax) is poorly correlated with porosity but strongly correlated with FZI, RQI, RT35, and RT50. These results indicate that large pores are the main pore zones that determine the seepage capacity of the reservoirs. Additionally, there is a certain correlation between Dmax and the irreducible water saturation.

An automated and universal approach for characterising cored formation intervals: gaining more insight from comparing zonation results – according to geological facie, rock type and hydraulic units

Formation characterisation of cored intervals presents an important challenge in defining reservoir parameters for integration of subsurface data, to gain understanding and subsequently use this information for other tasks, for example geological correlation, petrophysical calibration with open-hole logs and vertical grid definition for dynamic reservoir simulation. This paper demonstrates a fully automated and comparative analysis approach to define formation characteristics: based on geological and core description, linked to logs and pore structure – pore throat size and sorting, based on hydraulic parameters. Such comparison leads to greater understanding and more meaningful integration, taking into account all available data and ensuring consistency. Methodologies employed make use of cluster analysis and pattern recognition and use a six-parameter model space, a so-called global characteristics envelope (GCE): considering porosity, permeability, hydraulic radius, porosity group – porosity/grain volume, flow zone indicator – linked to pore-throat grain size/sorting. Comparative perspectives presented give greater insight to formation characteristics than more conventional approaches. The extended catalogue of models presented, not previously published, can be universally applied to all conventional formations, clastics and carbonates. Facie types are identified by different cluster situations exhibited in GCEs. Consistent diagenetic intervals form a base model. Very uniform intervals are those with uniform porosity or permeability, or both (a cluster). More heterogeneous intervals are highly varying diagenetic intervals; interbedded diagenetically-driven intervals, e.g. pyritisation; and alternating thinly-bedded intervals of different lithologies and fining sequences. A detailed case history is presented involving a cored formation interval exceeding 110 m plus one other example.