scholarly journals Numerical Simulation Modeling of Carbonate Reservoir Based on Rock Type

2017 ◽  
Vol 2017 ◽  
pp. 1-6 ◽  
Author(s):  
Peiqing Lian ◽  
Cuiyu Ma ◽  
Bingyu Ji ◽  
Taizhong Duan ◽  
Xuequn Tan

There are many types of carbonate reservoir rock spaces with complex shapes, and their primary pore structure changes dramatically. In order to describe the heterogeneity of K carbonate reservoir, equations of porosity, permeability and pore throat radii under different mercury injection saturations are fitted, and it shows that 30% is the best percentile. R30 method is presented for rock typing, and six rock types are divided according to R30 value of plugs. The porosity-permeability relationship is established for each rock type, and their relevant flow characteristics of each rock type have been studied. Logs are utilized to predict rock types of noncored wells, and a three-dimensional (3D) rock type model has been established based on the well rock type curves and the sedimentary facies constraint. Based on the relationship between J function and water saturation, the formula of water saturation, porosity, permeability, and oil column height can be obtained by multiple regressions for each rock type. Then, the water saturation is calculated for each grid, and a 3D water saturation model is established. The model can reflect the formation heterogeneity and the fluid distribution, and its accuracy is verified by the history matching.

2021 ◽  
Vol 11 (4) ◽  
pp. 1577-1595
Author(s):  
Rasoul Ranjbar-Karami ◽  
Parisa Tavoosi Iraj ◽  
Hamzeh Mehrabi

AbstractKnowledge of initial fluids saturation has great importance in hydrocarbon reservoir analysis and modelling. Distribution of initial water saturation (Swi) in 3D models dictates the original oil in place (STOIIP), which consequently influences reserve estimation and dynamic modelling. Calculation of initial water saturation in heterogeneous carbonate reservoirs always is a challenging task, because these reservoirs have complex depositional and diagenetic history with a complex pore network. This paper aims to model the initial water saturation in a pore facies framework, in a heterogeneous carbonate reservoir. Petrographic studies were accomplished to define depositional facies, diagenetic features and pore types. Accordingly, isolated pores are dominant in the upper parts, while the lower intervals contain more interconnected interparticle pore types. Generally, in the upper and middle parts of the reservoir, diagenetic alterations such as cementation and compaction decreased the primary reservoir potential. However, in the lower interval, which mainly includes high-energy shoal facies, high reservoir quality was formed by primary interparticle pores and secondary dissolution moulds and vugs. Using huge number of primary drainage mercury injection capillary pressure tests, we evaluate the ability of FZI, r35Winland, r35Pittman, FZI* and Lucia’s petrophysical classes in definition of rock types. Results show that recently introduced rock typing method is an efficient way to classify samples into petrophysical rock types with same pore characteristics. Moreover, as in this study MICP data were available from every one meter of reservoir interval, results show that using FZI* method much more representative sample can be selected for SCAL laboratory tests, in case of limitation in number of SCAL tests samples. Integration of petrographic analyses with routine (RCAL) and special (SCAL) core data resulted in recognition of four pore facies in the studied reservoir. Finally, in order to model initial water saturation, capillary pressure data were averaged in each pore facies which was defined by FZI* method and using a nonlinear curve fitting approach, fitting parameters (M and C) were extracted. Finally, relationship between fitting parameters and porosity in core samples was used to model initial water saturation in wells and between wells. As permeability prediction and reservoir rock typing are challenging tasks, findings of this study help to model initial water saturation using log-derived porosity.


2021 ◽  
Vol 11 (2) ◽  
pp. 49-73
Author(s):  
Buraq Adnan Hussein Al-Baldawi

This study presents is a classification of reservoir properties (porosity and shale volume) into rock types for carbonate Rumaila reservoir in Central Iraq (Ahdeb Field). The Cluster analysis method is used to identify rock types and to recognize well log clusters of similar characteristics. For most subsurface research, the determination of the rock type (lithofacies and petrofacies) is not adequate enough because of a lack of cores and cuttings. An interactive petrophysics software program was used to get the results of a cluster analysis technique, in order to determine the rock typing (log facies) in Rumaila formation units in the Ahdeb oil field. Initially, petrophysical parameters such as porosity, shale volume and quantity of various reservoir minerals were determined using the probabilistic evaluation process. In the second stage, the multi-resolution graphic clustering method was employed to separate the sequential electrofacies which resulted in the identification of four electrofacies with different geological reservoir properties. The vertical variations of the rock type for Rumaila formation are based on four log facial groups. These log groups are categorized according to porosity and shale volume of formation based on responses to well logs after division of Rumaila formation into four units (Ru-1, Ru-2, Ru-3, and Ru-4).A 3D rock type model for Rumaila Formation was performed using Petrel software in order to illustrate the horizontal distribution of rock type along the Ahdeb field and showing the best characterized of reservoir rock type in any unit of Rumaila Formation. Cluster analysis technique classified porosity and shale volume, which were calculated for Rumaila Formation using well logs, into four similar characteristics rock types: rocktype-1, rock type-2, rock type-3 and rock type-4. A 3D Petrel model of rock type shows that rock type-2 has better reservoir quality than other rock types in Rumaila Formation which is characterized by high porosity and low shale volume. The model clarifies the distribution of rock type-2 in the Ahdeb field at units Ru-1 and Ru-3 of Rumaila Formation


2021 ◽  
Vol 6 (4) ◽  
pp. 62-70
Author(s):  
Mariia A. Kuntsevich ◽  
Sergey V. Kuznetsov ◽  
Igor V. Perevozkin

The goal of carbonate rock typing is a realistic distribution of well data in a 3D model and the distribution of the corresponding rock types, on which the volume of hydrocarbon reserves and the dynamic characteristics of the flow will depend. Common rock typing approaches for carbonate rocks are based on texture, pore classification, electrofacies, or flow unit localization (FZI) and are often misleading because they based on sedimentation processes or mathematical justification. As a result, the identified rock types may poorly reflect the real distribution of reservoir rock characteristics. Materials and methods. The approach described in the work allows to eliminate such effects by identifying integrated rock types that control the static properties and dynamic behavior of the reservoir, while optimally linking with geological characteristics (diagenetic transformations, sedimentation features, as well as their union effect) and petrophysical characteristics (reservoir properties, relationship between the porosity and permeability, water saturation, radius of pore channels and others). The integrated algorithm consists of 8 steps, allowing the output to obtain rock-types in the maximum possible way connecting together all the characteristics of the rock, available initial information. The first test in the Middle East field confirmed the applicability of this technique. Results. The result of the work was the creation of a software product (certificate of state registration of the computer program “Lucia”, registration number 2021612075 dated 02/11/2021), which allows automating the process of identifying rock types in order to quickly select the most optimal method, as well as the possibility of their integration. As part of the product, machine learning technologies were introduced to predict rock types based on well logs in intervals not covered by coring studies, as well as in wells in which there is no coring.


2018 ◽  
Vol 6 (3) ◽  
pp. T555-T567
Author(s):  
Zhuoying Fan ◽  
Jiagen Hou ◽  
Chengyan Lin ◽  
Xinmin Ge

Classification and well-logging evaluation of carbonate reservoir rock is very difficult. On one side, there are many reservoir pore spaces developed in carbonate reservoirs, including large karst caves, dissolved pores, fractures, intergranular dissolved pores, intragranular dissolved pores, and micropores. On the other side, conventional well-logging response characteristics of the various pore systems can be similar, making it difficult to identify the type of pore systems. We have developed a new reservoir rock-type characterization workflow. First, outcrop observations, cores, well logs, and multiscale data were used to clarify the carbonate reservoir types in the Ordovician carbonates of the Tahe Oilfield. Three reservoir rock types were divided based on outcrop, core observation, and thin section analysis. Microscopic and macroscopic characteristics of various rock types and their corresponding well-log responses were evaluated. Second, conventional well-log data were decomposed into multiple band sets of intrinsic mode functions using empirical mode decomposition method. The energy entropy of each log curve was then investigated. Based on the decomposition results, the characteristics of each reservoir type were summarized. Finally, by using the Fisher discriminant, the rock types of the carbonate reservoirs could be identified reliably. Comparing with conventional rock type identification methods based on conventional well-log responses only, the new workflow proposed in this paper can effectively cluster data within each rock types and increase the accuracy of reservoir type-based hydrocarbon production prediction. The workflow was applied to 213 reservoir intervals from 146 wells in the Tahe Oilfield. The results can improve the accuracy of oil-production interval prediction using well logs over conventional methods.


2020 ◽  
Vol 1 (1) ◽  
pp. 48-55
Author(s):  
Lena Maretha Salindeho

The carbonate reservoir is one of the reservoir characters found in hydrocarbon fields in Indonesia. Carbonate reservoirs have complex porosity and permeability relationships. So it is necessary to do a special reservoir character that is different from the siliciclastic reservoir. Efforts that can be made to assist the development of this hydrocarbon field are to analyze the reservoir character in more detail using the petrophysical rock type (PRT) approach. This approach is used by combining geological elements such as the depositional environment, the petrophysical properties of the rock, as well as the fluid flow in it which is reflected by capillary pressure and water saturation. Modeling with this method is expected to be a method that can increase hydrocarbon production optimally in Xena Field. The object of research from Xena Field is Zone A2 which is included in the Parigi Formation. The Parigi Formation is one of the main hydrocarbon-producing reservoirs. The data used in this study are routine core analysis (RCAL) rock data on JLB-07, JLB-08, JLB-02, JLB-23 wells, wire log data (gamma-ray log, resistivity log, density log, neutron log) of 30 wells, and 2D seismic data. The depositional facies are divided into 2 facies, namely the margin reef platform facies and the interior platform facies. Identification of rock type (RT) using the flow zone indicator (FZI) method. The rock type in this field can be divided into 4 rock types, namely RT 1, RT 2, RT 3, RT 4 with RT 1 being able to drain the best fluid and RT 4 to drain the worst fluid. Reservoir property modeling is controlled by facies and rock type (RT) models. The margin reef platform facies are associated with RT 1 and RT 2. The interior platform facies are associated with RT 2 and RT 3.


2021 ◽  
Author(s):  
Mohamed Masoud ◽  
W. Scott Meddaugh ◽  
Masoud Eljaroshi ◽  
Khaled Elghanduri

Abstract The Harash Formation was previously known as the Ruaga A and is considered to be one of the most productive reservoirs in the Zelten field in terms of reservoir quality, areal extent, and hydrocarbon quantity. To date, nearly 70 wells were drilled targeting the Harash reservoir. A few wells initially naturally produced but most had to be stimulated which reflected the field drilling and development plan. The Harash reservoir rock typing identification was essential in understanding the reservoir geology implementation of reservoir development drilling program, the construction of representative reservoir models, hydrocarbons volumetric calculations, and historical pressure-production matching in the flow modelling processes. The objectives of this study are to predict the permeability at un-cored wells and unsampled locations, to classify the reservoir rocks into main rock typing, and to build robust reservoir properties models in which static petrophysical properties and fluid properties are assigned for identified rock type and assessed the existed vertical and lateral heterogeneity within the Palaeocene Harash carbonate reservoir. Initially, an objective-based workflow was developed by generating a training dataset from open hole logs and core samples which were conventionally and specially analyzed of six wells. The developed dataset was used to predict permeability at cored wells through a K-mod model that applies Neural Network Analysis (NNA) and Declustring (DC) algorithms to generate representative permeability and electro-facies. Equal statistical weights were given to log responses without analytical supervision taking into account the significant log response variations. The core data was grouped on petrophysical basis to compute pore throat size aiming at deriving and enlarging the interpretation process from the core to log domain using Indexation and Probabilities of Self-Organized Maps (IPSOM) classification model to develop a reliable representation of rock type classification at the well scale. Permeability and rock typing derived from the open-hole logs and core samples analysis are the main K-mod and IPSOM classification model outputs. The results were propagated to more than 70 un-cored wells. Rock typing techniques were also conducted to classify the Harash reservoir rocks in a consistent manner. Depositional rock typing using a stratigraphic modified Lorenz plot and electro-facies suggest three different rock types that are probably linked to three flow zones. The defined rock types are dominated by specifc reservoir parameters. Electro-facies enables subdivision of the formation into petrophysical groups in which properties were assigned to and were characterized by dynamic behavior and the rock-fluid interaction. Capillary pressure and relative permeability data proved the complexity in rock capillarity. Subsequently, Swc is really rock typing dependent. The use of a consistent representative petrophysical rock type classification led to a significant improvement of geological and flow models.


2021 ◽  
Author(s):  
E. P. Putra

The Globigerina Limestone (GL) is the main reservoir of the seven gas fields that will be developed in the Madura Strait Block. The GL is a heterogeneous and unique clastic carbonate. However, the understanding of reservoir rock type of this reservoir are quite limited. Rock type definition in heterogeneous GL is very important aspect for reservoir modeling and will influences field development strategy. Rock type analysis in this study is using integration of core data, wireline logs and formation test data. Rock type determination applies porosity and permeability relationship approach from core data, which related to pore size distribution, lithofacies, and diagenesis. The analysis resulted eight rock types in the Globigerina Limestone reservoir. Result suggests that rock type definition is strongly influenced by lithofacies, which is dominated by packstone and wackestone - packstone. The diagenetic process in the deep burial environment causes decreasing of reservoir quality. Then the diagenesis process turns to be shallower in marine phreatic zone and causes dissolution which increasing the reservoir quality. Moreover, the analysis of rock type properties consist of clay volume, porosity, permeability, and water saturation. The good quality of a rock type will have the higher the porosity and permeability. The dominant rock type in this study area is RT4, which is identical to packstone lithofasies that has 0.40 v/v porosity and 5.2 mD as average permeability. The packstone litofacies could be found in RT 5, 6, 7, even 8 due to the increased of secondary porosity. It could also be found at a lower RT which is caused by intensive cementation.


1999 ◽  
Vol 2 (02) ◽  
pp. 149-160 ◽  
Author(s):  
D.K. Davies ◽  
R.K. Vessell ◽  
J.B. Auman

Summary This paper presents a cost effective, quantitative methodology for reservoir characterization that results in improved prediction of permeability, production and injection behavior during primary and enhanced recovery operations. The method is based fundamentally on the identification of rock types (intervals of rock with unique pore geometry). This approach uses image analysis of core material to quantitatively identify various pore geometries. When combined with more traditional petrophysical measurements, such as porosity, permeability and capillary pressure, intervals of rock with various pore geometries (rock types) can be recognized from conventional wireline logs in noncored wells or intervals. This allows for calculation of rock type and improved estimation of permeability and saturation. Based on geological input, the reservoirs can then be divided into flow units (hydrodynamically continuous layers) and grid blocks for simulation. Results are presented of detailed studies in two, distinctly different, complex reservoirs: a low porosity carbonate reservoir and a high porosity sandstone reservoir. When combined with production data, the improved characterization and predictability of performance obtained using this unique technique have provided a means of targeting the highest quality development drilling locations, improving pattern design, rapidly recognizing conformance and formation damage problems, identifying bypassed pay intervals, and improving assessments of present and future value. Introduction This paper presents a technique for improved prediction of permeability and flow unit distribution that can be used in reservoirs of widely differing lithologies and differing porosity characteristics. The technique focuses on the use and integration of pore geometrical data and wireline log data to predict permeability and define hydraulic flow units in complex reservoirs. The two studies presented here include a low porosity, complex carbonate reservoir and a high porosity, heterogeneous sandstone reservoir. These reservoir classes represent end-members in the spectrum of hydrocarbon reservoirs. Additionally, these reservoirs are often difficult to characterize (due to their geological complexity) and frequently contain significant volumes of remaining reserves.1 The two reservoir studies are funded by the U.S. Department of Energy as part of the Class II and Class III Oil Programs for shallow shelf carbonate (SSC) reservoirs and slope/basin clastic (SBC) reservoirs. The technique described in this paper has also been used to characterize a wide range of other carbonate and sandstone reservoirs including tight gas sands (Wilcox, Vicksburg, and Cotton Valley Formations, Texas), moderate porosity sandstones (Middle Magdalena Valley, Colombia and San Jorge Basin, Argentina), and high porosity reservoirs (Offshore Gulf Coast and Middle East). The techniques used for reservoir description in this paper meet three basic requirements that are important in mature, heterogeneous fields.The reservoir descriptions are log-based. Flow units are identified using wireline logs because few wells have cores. Integration of data from analysis of cores is an essential component of the log models.Accurate values of permeability are derived from logs. In complex reservoirs, values of porosity and saturation derived from routine log analysis often do not accurately identify productivity. It is therefore necessary to develop a log model that will allow the prediction of another producibility parameter. In these studies we have derived foot-by-foot values of permeability for cored and non-cored intervals in all wells with suitable wireline logs.Use only the existing databases. No new wells will be drilled to aid reservoir description. Methodology Techniques of reservoir description used in these studies are based on the identification of rock types (intervals of rock with unique petrophysical properties). Rock types are identified on the basis of measured pore geometrical characteristics, principally pore body size (average diameter), pore body shape, aspect ratio (size of pore body: size of pore throat) and coordination number (number of throats per pore). This involves the detailed analysis of small rock samples taken from existing cores (conventional cores and sidewall cores). The rock type information is used to develop the vertical layering profile in cored intervals. Integration of rock type data with wireline log data allows field-wide extrapolation of the reservoir model from cored to non-cored wells. Emphasis is placed on measurement of pore geometrical characteristics using a scanning electron microscope specially equipped for automated image analysis procedures.2–4 A knowledge of pore geometrical characteristics is of fundamental importance to reservoir characterization because the displacement of hydrocarbons is controlled at the pore level; the petrophysical properties of rocks are controlled by the pore geometry.5–8 The specific procedure includes the following steps.Routine measurement of porosity and permeability.Detailed macroscopic core description to identify vertical changes in texture and lithology for all cores.Detailed thin section and scanning electron microscope analyses (secondary electron imaging mode) of 100 to 150 small rock samples taken from the same locations as the plugs used in routine core analysis. In the SBC reservoir, x-ray diffraction analysis is also used. The combination of thin section and x-ray analyses provides direct measurement of the shale volume, clay volume, grain size, sorting and mineral composition for the core samples analyzed.Rock types are identified for each rock sample using measured data on pore body size, pore throat size and pore interconnectivity (coordination number and pore arrangement).


2019 ◽  
Vol 142 (6) ◽  
Author(s):  
Xiangnan Liu ◽  
Daoyong Yang

Abstract In this paper, techniques have been developed to interpret three-phase relative permeability and water–oil capillary pressure simultaneously in a tight carbonate reservoir from numerically simulating wireline formation tester (WFT) measurements. A high-resolution cylindrical near-wellbore model is built based on a set of pressures and flow rates collected by dual packer WFT in a tight carbonate reservoir. The grid quality is validated, the effective thickness of the WFT measurements is examined, and the effectiveness of the techniques is confirmed prior to performing history matching for both the measured pressure drawdown and buildup profiles. Water–oil relative permeability, oil–gas relative permeability, and water–oil capillary pressure are interpreted based on power-law functions and under the assumption of a water-wet reservoir and an oil-wet reservoir, respectively. Subsequently, three-phase relative permeability for the oil phase is determined using the modified Stone II model. Both the relative permeability and the capillary pressure of a water–oil system interpreted under an oil-wet condition match well with the measured relative permeability and capillary pressure of a similar reservoir rock type collected from the literature, while the relative permeability of an oil–gas system and the three-phase relative permeability bear a relatively high uncertainty. Not only is the reservoir determined as oil-wet but also the initial oil saturation is found to impose an impact on the interpreted water relative permeability under an oil-wet condition. Changes in water and oil viscosities and mud filtrate invasion depth affect the range of the movable fluid saturation of the interpreted water–oil relative permeabilities.


2007 ◽  
Vol 10 (06) ◽  
pp. 730-739 ◽  
Author(s):  
Genliang Guo ◽  
Marlon A. Diaz ◽  
Francisco Jose Paz ◽  
Joe Smalley ◽  
Eric A. Waninger

Summary In clastic reservoirs in the Oriente basin, South America, the rock-quality index (RQI) and flow-zone indicator (FZI) have proved to be effective techniques for rock-type classifications. It has long been recognized that excellent permeability/porosity relationships can be obtained once the conventional core data are grouped according to their rock types. Furthermore, it was also observed from this study that the capillary pressure curves, as well as the relative permeability curves, show close relationships with the defined rock types in the basin. These results lead us to believe that if the rock type is defined properly, then a realistic permeability model, a unique set of relative permeability curves, and a consistent J function can be developed for a given rock type. The primary purpose of this paper is to demonstrate the procedure for implementing this technique in our reservoir modeling. First, conventional core data were used to define the rock types for the cored intervals. The wireline log measurements at the cored depths were extracted, normalized, and subsequently analyzed together with the calculated rock types. A mathematical model was then built to predict the rock type in uncored intervals and in uncored wells. This allows the generation of a synthetic rock-type log for all wells with modern log suites. Geostatistical techniques can then be used to populate the rock type throughout a reservoir. After rock type and porosity are populated properly, the permeability can be estimated by use of the unique permeability/porosity relationship for a given rock type. The initial water saturation for a reservoir can be estimated subsequently by use of the corresponding rock-type, porosity, and permeability models as well as the rock-type-based J functions. We observed that a global permeability multiplier became unnecessary in our reservoir-simulation models when the permeability model is constructed with this technique. Consistent initial-water-saturation models (i.e., calculated and log-measured water saturations are in excellent agreement) can be obtained when the proper J function is used for a given rock type. As a result, the uncertainty associated with volumetric calculations is greatly reduced as a more accurate initial-water-saturation model is used. The true dynamic characteristics (i.e., the flow capacity) of the reservoir are captured in the reservoir-simulation model when a more reliable permeability model is used. Introduction Rock typing is a process of classifying reservoir rocks into distinct units, each of which was deposited under similar geological conditions and has undergone similar diagenetic alterations (Gunter et al. 1997). When properly classified, a given rock type is imprinted by a unique permeability/porosity relationship, capillary pressure profile (or J function), and set of relative permeability curves (Gunter et al. 1997; Hartmann and Farina 2004; Amaefule et al. 1993). As a result, when properly applied, rock typing can lead to the accurate estimation of formation permeability in uncored intervals and in uncored wells; reliable generation of initial-water-saturation profile; and subsequently, the consistent and realistic simulation of reservoir dynamic behavior and production performance. Of the various quantitative rock-typing techniques (Gunter et al. 1997; Hartmann and Farina 2004; Amaefule et al. 1993; Porras and Campos 2001; Jennings and Lucia 2001; Rincones et al. 2000; Soto et al. 2001) presented in the literature, two techniques (RQI/FZI and Winland's R35) appear to be used more widely than the others for clastic reservoirs (Gunter et al. 1997, Amaefule et al. 1993). In the RQI/FZI approach (Amaefule et al. 1993), rock types are classified with the following three equations: [equations]


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