scholarly journals The Effect of Pore Geometry on the Flow Unit and Its Impact to Permeability of the Reservoir Rock

Author(s):  
G Yasmaniar ◽  
S Prakoso ◽  
R Sitaresmi
2020 ◽  
Vol 6 (1) ◽  
pp. 3-17
Author(s):  
Ayu Yuliani ◽  
Ordas Dewanto ◽  
Karyanto Karyanto ◽  
Ade Yogi

Determination of reservoir rock properties is very important to be able to understand the reservoir better. One of these rock properties is permeability. Permeability is the ability of a rock to pass fluid. In this study, the calculation of permeability was carried out using log and PGS (Pore Geometry Structure) methods based on core data, logs, and CT scans. In the log method, the calculation of permeability is done by petrophysical analysis which aims to evaluate the target zone formation in the form of calculation of the distribution of shale content (effective volume), effective porosity, water saturation, and permeability. Next, the determination of porosity values from CT Scan. Performed on 2 data cores of 20 tubes, each tube was plotted as many as 15 points. The output of this stage is the CT Porosity value that will be used for the distribution of predictions of PGS permeability values. In the PGS method, rock typing is based on geological descriptions, then calculation of permeability predictions. Using these two methods, permeability can be calculated in the study area. The results of log and PGS permeability calculations that show good correlation are the results of calculation of PGS permeability. It can be seen from the data from the calculation of PGS permeability approaching a gradient of one value with R2 of 0.906, it will increasingly approach the core rock permeability value. Whereas the log permeability calculation for core rock permeability is 0.845.


2018 ◽  
Vol 3 (2) ◽  
pp. 93-100
Author(s):  
Abdul Haris ◽  
Agus Riyanto ◽  
Tri Aji Adi Harsanto ◽  
Ambar Rachmanto ◽  
Adang Sukmatiawa

In the last few years, the use of flow unit technique in the oil and gas industry has shown a great deal of success. Porosity and permeability from wire-line log and special core data analysis (SCAL) along with its cementation exponent value were integrated to characterize the reservoir in terms of pore volume caused by facies changing. In this work, we determine flow unit of the carbonate reservoir, which is applied to the Northwest Java Basin Field, Indonesia by performing the flow unit analysis, which allows approximating absolute permeability. Furthermore, the quantity and the flow unit of the reservoir rock is also determined to identify the secondary porosity. To reduce the level of uncertainty, wire-line logs data were validated with core data before it is used to interpret the reservoir. Subsequently, the result can be extrapolated to un-cored wells. Our experiment shows that flow units can be determined reliably from the integration between porosity and permeability, which have defined two different rock types in term of flow unit zone. The correlation of the flow units between wells leads to the definition of reservoir quality.


2021 ◽  
Vol 62 (3) ◽  
pp. 29-36
Author(s):  

Permeability and porosity are essential parameters for estimating hydrocarbon production from reservoir rocks. They are combined in an additional factor, the Flow Zone Index (FZI), which is the basis for defining the hydraulic flow unit (HFU). Each HFU is a homogeneous section of a reservoir rock with stable parameters that allow for media flow. Hydraulic flow units are determined from the porosity and permeability of core or well logs. The simple statistical methods are applied for HFU classification and then improve permeability prediction. This paper also shows how to quickly apply the global hydraulic elements (GHE) method for HFU classification. The methodology is tested on the Miocene formation of a deltaic facies from the Carpathian Foredeep in South-Eastern Poland.


1982 ◽  
Vol 22 (03) ◽  
pp. 429-444 ◽  
Author(s):  
Kenneth Ruzyla ◽  
Gerald M. Friedman

Abstract Several different pore systems are present in dolomite reservoir rocks of the Red River formation (Upper Ordovician) at Cabin Creek field, MT. Each system is associated with particular depositional environments and diagenetic regimes. Pore geometry is mostly a function of the size and shape of the dolomite crystals composing the rock matrix. Mean pore-throat size, a statistical measure of pore geometry, increases as porosity percent increases, depending on the type of dolomite. This relationship permits prediction of reservoir pore geometry and a better assessment of recovery efficiency once lithofacies distribution, porosity origin, and diagenetic history have been determined for the reservoir by study of cores and rock thin-sections. Introduction The reservoir characteristics of any rock type depend on the arrangement of the pore space and how the pores are interconnected. The pore-system geometry of a reservoir rock must be understood to determine fully its response to primary or enhanced recovery. To predict pore geometry trends, it is necessary to establish relationships between measures of pore geometry and petrophysical parameters. which are measurable by electric-log surveys of boreholes. This is because cores, which are necessary for pore geometry determination, are usually available for select wells of any given field. Pore geometry is mostly a function of depositional environment and diagenetic processes such as cementation, recrystallization, mineralogical alterations, and selective leaching of rock components. This study presents an approach for determining heterogeneities of carbonate-reservoir pore geometry and for delineating pore geometry throughout the reservoir. The application to future enhanced recovery also is discussed. The formation under study is the Red River formation (Upper Ordovician) of Cabin Creek field, a producing oil field located in southeast Montana (Fig. 1). The Red River formation is a major producing reservoir in the area, and Cabin Creek is a potential candidate for tertiary recovery. Structurally, Cabin Creek is on the Cedar Creek anticline, a long asymmetrical feature on the southwest margin of the Williston basin (Fig. 1). Fig. 2 is a structure contour map of Cabin Creek field. The Red River formation averages about 500 ft (153 m) in thickness and consists of a sequence of alternating limestones and dolostones (Fig. 3). Production is from the U2, U4, and U6 dolostone units in the upper 150 ft (46 m) of the formation. The interstratified U1, U3, and US limestone units are nonproductive and nonporous (Fig. 4). Lateral and vertical variations in degree of dolomitization are mostly responsible for variations of reservoir properties. Commingled Ordovician and Silurian oil production was 61,570,000 bbl as of Sept. 1979, with reserves of 13,425,000 bbl (2 134 405 m ). The field has been on waterflood since April 1964. Approximately 1,450 ft (444 m) of core from 12 different wells was studied to delineate field stratigraphy, distribution of lithofacies, and depositional environments. Core slabs were ground with abrasive grit, then etched in diluted hydrochloric acid to enhance sedimentary structures and aid in identification of carbonate grains and matrix material. Staining methods were used to aid mineralogical identification. Diagenesis, porosity types, and origin of porosity were determined by petrographic analysis of thin-sections. Values of porosity percentage, permeability, and saturations are from core-plug analyses. Size and shape of pore throats were determined from mercury capillary-pressure data and from scanning-electron micrographs of resin pore casts, respectively. Plots were made of porosity percentage vs. parameters of pore geometry for producing zones within the Red River formation. SPEJ P. 429^


2021 ◽  
Author(s):  
Budi Priyatna Kantaatmadja ◽  
Fadzlin H. Kasim ◽  
W. Nur Zainudin ◽  
Emad Elsebakhi ◽  
Ernest A. Jones Jr ◽  
...  

Abstract Predicting permeability in low-medium quality reservoirs (> 10 md to <100mD) is important in brownfields since many of them can still produce hydrocarbons. Developing an approach relating geologic properties to permeability prediction can increase field reserves and extend producing life. The common practice of predicting permeability includes linear regressions of core-porosities vs. core-permeabilities applying different lithofacies. However, these methods discount data scattering around regression-lines. This paper describes an innovative-technique for permeability prediction that combines rock-types, flow-zone-indicator (FZI), and machine-learning techniques (ML). FZI is a reservoir-flow-unit that controls hydraulic fluid-flow and is influenced by pore-geometry resulting from diagenetic-processes. In reservoirs, pore-geometry usually is heterogenous due to mineral-composition, rock-texture, cementation, and compaction. Thus,the commonly used permeability equation of Kozeny-Carman (KC) equation still can be used but it needs to be modified for better connecting FZI to hydraulic-flow-units. The modified KC equation incorporates heterogeneous poregeometry as a non-linear-function of porosity by adding cementation-exponent (m) into the equation, where the original KC equation assumes m is equal to one. The semi-log cross-plot between Reservoir-Quality-Index (RQI) vs. PHIZ*Por(m-1) (or FZIm) from the modified KC equation can be constructed using rock-type class. The ML approach was applied to predict FZI groups using 4 standard-logs: gamma-ray, resistivity, density, and neutron-porosity. Cross-plots of RQI vs. PHIZ (conventional FZI) can be compared to RQI vs. PHIZ*Por(m-1) (modified FZI model) usingdata from 11cored wells in oil field offshore Malaysia. The modified FZI model shows less data clustering compared to the conventional FZI model, shown by higher R2 coefficient correlation accuracy. The proposed modified FZI model shows narrower permeability range at low porosity which is a good indication of more accurate hydraulic-flow-unit interpretation. When applying the original and modified FZI models, each lithofacies may occur in more than one hydraulic-flow-unit due to pore-geometry difference within the same lithofacies. Furthermore, the hydraulic-flow-unit generated by the modified FZI model is more sensitive to total porosity when comparing to original FZI model. Each generated hydraulic-flow-unit has better correlation to total porosity and with less scattered permeability at the same porosity. The permeability calculated by modified FZI model was then verified with core permeability showing an excellent overall match. On the ML technique, the "Random Forests" technique will be utilized due to recognized as one of the most recent ML algorithm(s) developed as an innovative technique based on both classifications and regression trees techniques. The Random Forests technique has shown its great accuracy on predictive exactness for these challenge permeability estimations. The prediction quality was benchmark by R2 value of > 0.9 for all crossplots (porosity, permeability, and water saturation) when comparing to routine core analysis lab measurements.


2020 ◽  
Vol 43 (1) ◽  
pp. 17-29
Author(s):  
Sugihardjo Sugihardjo

These paper contains a highlight of laboratory experiment to evaluate the work of chemical for sand consolidation to strengthen the bonding between grains of rock while do not cause permeability reduction significantly. This experiment used reservoir rock and fluids to understand the interaction between the chemical solution and the reservoir rock and fluid. Firstly, the reservoir rock and fluid were analyzed their properties. The rock has been analyzed using CT Scan to drill the best representative core plug for the experiments, using SEM to identify the pore throat and pore geometry of the rock, using XRD to determine the minerals composition which mostly quartz. While the fluids have been analyzed for the anions and cations content, viscosity and other important properties. The brine particle content and also particle size distribution of the rock have been also over lied in the graph in order to know the possibility of bridging particle in the pore throat, but the graph looks good that no problem may arise from the bridging particle. Chemical for Sand Consolidation has been used in this experiment. Sand consolidation chemical normally contain plastic resin that has a property of bonding between solid materials. It sticks on the surface of solid materials and bonding together.The core flooding experiments have been run for 4 times, 2 times using synthetic cores and the other two using native cores. The experiments used synthetic cores reduce the permeability significantly. However, after cutting both ends of the core the permeability has indicated improvement. The other 2 experiments using native cores have reduced the permeability approximately 4 times down. The last two experiments have no cutting the ends of core for further experiments, so they cannot be compared to the first two experiment. So, the experiment procedures must be improved for the next evaluation, such as during curing time the rate of injected oil may be increased to reduce the adsorption of chemical to the surface area of the pore and also to hinder the flocculation of chemical in the pore space.


Author(s):  
Douglas L. Dorset ◽  
Andrew K. Massalski

Matrix porin, the ompF gene product of E. coli, has been the object of a electron crystallographic study of its pore geometry in an attempt to understand its function as a membrane molecular sieve. Three polymorphic forms have been found for two-dimensional crystals reconstituted in phospholipid, two hexagonal forms with different lipid content and an orthorhombic form coexisting with and similar to the hexagonal form found after lipid loss. In projection these have been shown to retain the same three-fold pore triplet geometry and analyses of three-dimensional data reveal that the small hexagonal and orthorhombic polymorphs have similar structure as well as unit cell spacings.


Author(s):  
C.J. Stuart ◽  
B.E. Viani ◽  
J. Walker ◽  
T.H. Levesque

Many techniques of imaging used to characterize petroleum reservoir rocks are applied to dehydrated specimens. In order to directly study behavior of fines in reservoir rock at conditions similar to those found in-situ these materials need to be characterized in a fluid saturated state.Standard light microscopy can be used on wet specimens but depth of field and focus cannot be obtained; by using the Tandem Scanning Confocal Microscope (TSM) images can be produced from thin focused layers with high contrast and resolution. Optical sectioning and extended focus images are then produced with the microscope. The TSM uses reflected light, bulk specimens, and wet samples as opposed to thin section analysis used in standard light microscopy. The TSM also has additional advantages: the high scan speed, the ability to use a variety of light sources to produce real color images, and the simple, small size scanning system. The TSM has frame rates in excess of normal TV rates with many more lines of resolution. This is accomplished by incorporating a method of parallel image scanning and detection. The parallel scanning in the TSM is accomplished by means of multiple apertures in a disk which is positioned in the intermediate image plane of the objective lens. Thousands of apertures are distributed in an annulus, so that as the disk is spun, the specimen is illuminated simultaneously by a large number of scanning beams with uniform illumination. The high frame speeds greatly simplify the task of image recording since any of the normally used devices such as photographic cameras, normal or low light TV cameras, VCR or optical disks can be used without modification. Any frame store device compatible with a standard TV camera may be used to digitize TSM images.


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