New Insights From Old Data: Identification of Rock Types and Permeability Prediction Within a Heterogeneous Carbonate Reservoir Using Diplog and Openhole Log Data

2002 ◽  
Author(s):  
Graham F. Aplin ◽  
Jean-Michel L. Dawans ◽  
Ajay K. Sapru
Geofluids ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Hao Lu ◽  
Hongming Tang ◽  
Meng Wang ◽  
Xin Li ◽  
Liehui Zhang ◽  
...  

Due to the diversity of pore types, it is challenging to characterize the Middle East’s Cretaceous carbonate reservoir or accurately predict its petrophysical properties. In this paper, pore structure in the reservoir is first classified using a comprehensive method. Then, based on the identified pore structure types, a new permeability model with high prediction precision is established. The reservoir is dominated by 6 pore types, such as intergrain pores and moldic pores, and 6 rock types. Grainstone, algal packstone, algal wackestone, and foraminifera wackestone are porous rock types, and echinoderm wackestone and mudstone are nonporous rock types. The types of pore structure in the study area can be divided into four types. Type I has midhigh porosity and medium-high permeability due to its large throat, while type II has a fine throat type with midhigh porosity and midpermeability. Due to their isolated pores, the permeability is low in types III and IV, and out of these two, type III has better storage capacity. Movable fluid saturation calculated by the spectral coefficient method and r apex can characterize the boundary between the connected pores and unconnected pores very well in the research area. It is not accurate enough to simply classify the pore structure by permeability and porosity. The combination of porosity, permeability, r apex , flow zone indicator, and the reservoir quality index can effectively distinguish and classify pore structure types in noncoring wells. The characteristics of each pore structure type are consistent with those of the fractal dimension, which thereby proves the effectiveness of the pore structure classification. New permeability prediction models are proposed for different pore structure types, and good prediction results have been obtained. This study is of great significance for enhancing oil recovery.


Author(s):  
Anditya Sapta Rahesthi ◽  
Ratnayu Sitaresmi ◽  
Sigit Rahmawan

<em>Rock permeability is an important rock characteristic because it can help determine the rate of fluid production. Permeability can only be determined by direct measurement of core samples in the laboratory. Even though coring gives good results, the disadvantage is that it takes a lot of time and costs so it is not possible to do coring at all intervals. So that the well log is required to predict the level of permeability indirectly. However, the calculation of permeability prediction using well log data has a high uncertainty value, so rock typing is required so that the calculation of permeability prediction becomes more detailed. This research was conducted in an effort to determine the Hydraulic Flow Unit (HFU) of the reservoir in the well that has core data using the Flow Zone Indicator (FZI) parameter and FZI value propagation on wells that do not have core data so that the type of rock and permeability value are obtained from every well interval. From the results of the study, the reservoirs on the ASR field can be grouped into six rock types. The six rock types each have permeability as a function of validated porosity by applying it at all intervals. After FZI is calculated from log data and validated with core data, it can be seen that the results of the method produce a fairly good correlation (R<sup>2</sup> = 0.92). Furthermore, from the permeability equation values for each different rock type, the predicted permeability results are also quite good (R<sup>2 </sup>= 0.81).</em>


KURVATEK ◽  
2017 ◽  
Vol 1 (2) ◽  
pp. 21-31
Author(s):  
Fatimah Miharno

ABSTRACT*Zefara* Field formation Baturaja on South Sumatra Basin is a reservoir carbonate and prospective gas. Data used in this research were 3D seismik data, well logs, and geological information. According to geological report known that hidrocarbon traps in research area were limestone lithological layer as stratigraphical trap and faulted anticline as structural trap. The study restricted in effort to make a hydrocarbon accumulation and a potential carbonate reservoir area maps with seismic attribute. All of the data used in this study are 3D seismic data set, well-log data and check-shot data. The result of the analysis are compared to the result derived from log data calculation as a control analysis. Hydrocarbon prospect area generated from seismic attribute and are divided into three compartments. The seismic attribute analysis using RMS amplitude method and instantaneous frequency is very effective to determine hydrocarbon accumulation in *Zefara* field, because low amplitude from Baturaja reservoir. Low amplitude hints low AI, determined high porosity and high hydrocarbon contact (HC).  Keyword: Baturaja Formation, RMS amplitude seismic attribute, instantaneous frequency seismic attribute


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.


GeoArabia ◽  
1996 ◽  
Vol 1 (4) ◽  
pp. 551-566
Author(s):  
Anthony Kirkham ◽  
Mohamed Bin Juma ◽  
Tilden A.M. McKean ◽  
Anthony F. Palmer ◽  
Michael J. Smith ◽  
...  

ABSTRACT The field is a low amplitude structure with a chalky, Lower Cretaceous, Thamama reservoir characterised by a large hydrocarbon transition zone. Porosity generally decreases with depth within the trap although porosity versus depth trends are skewed by tilting. Porosity and permeability mapping was therefore achieved using templates based on seismic amplitudes. Special core analysis data were used to construct algorithms of Leverett J functions versus saturation for a variety of rock types mapped throughout the 3-D geological model of the field. The templated poroperms were then combined with capillary pressures to predict fluid saturations from these algorithms. The modelling of fluid distributions was therefore dependent upon heterogeneities imposed by the rock fabrics. Calibrating the model-predicted saturations against log-derived saturations at the wells involved regression techniques which were complicated by: notional structural tilting of the free water level, imbibition, hysteresis and permeability averaging procedures. Filtered “stick displays” proved useful in assessing the quality of the calibrations and were invaluable tools for highlighting and investigating data anomalies.


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).


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