scholarly journals Reservoir rock classification, Arab-D reservoir, Ghawar field, Saudi Arabia

GeoArabia ◽  
2003 ◽  
Vol 8 (3) ◽  
pp. 435-462
Author(s):  
Dave L. Cantrell ◽  
Royal M. Hagerty
Keyword(s):  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Rock Type ◽  
Reservoir Rock ◽  
Rock Classification ◽  
Crystal Texture ◽  
Rock Types ◽  
Lime Mud ◽  
Ghawar Field

ABSTRACT An integrated petrographic and petrophysical study of Arab-D carbonates in Ghawar field has provided a new reservoir rock classification. This classification provides a simple but practical method of dividing the complex carbonate rocks of the Arab-D into meaningful reservoir rock types. Each rock type has a distinct pore network as defined by porosity-permeability relationships and capillarity expressed as pore-size distributions and J-function curves. The classification divides the Arab-D carbonates into seven limestone and four dolomite rock types. The amount of matrix (lime mud) and the pore types are the primary controlling parameters for the limestones. The dolomites are divided according to their crystal texture. The seven limestone reservoir rock types are based on the values of five petrographic parameters: (1) the amount of cement, (2) the amount of matrix (lime mud), (3) the grain sorting, (4) the dominant pore type, and (5) the size of the largest molds. The amount of matrix is the most important of these five parameters. In general terms, six of these seven types fall into two broad families, A and B, each of which can then be subdivided into three members (Types I, II, and III) according to their matrix content. The first family, A, is a fairly coarse-grained, poorly sorted rock with relatively large molds. The second family, B, is a generally fine to medium-grained, well sorted rock with few or small molds. The seventh rock type contains more than 10 percent cement which modifies the pore size distribution enough to warrant a separate reservoir rock type. Each of the reservoir rock types exhibits a distinctive pore-size distribution and, in turn, Leverett J-function or capillarity. The seven types are also characterized by distinctive porosity-permeability relationships. The four dolomite reservoir rock types are classified according to their dolomite crystal texture, although stratigraphic position and porosity can also be effective in their classification. The four textures are: fabric preserving (Vfp), sucrosic (Vs), intermediate (Vi) and mosaic (Vm). The Vfp dolomite is only found in Zone 1 of the Arab-D where it is the major dolomite type. Vs dolomite occurs in dolomites with more than 12 percent porosity, Vm less than 5 percent and Vi between 5 and 12 percent. Vfp dolomites have pore systems similar to their precursor limestone but the pore systems of the other dolomite types are unique. A significant finding of this evaluation is that the micropore system in all major limestone rock types in Zones 1 and 2 (upper Arab-D) is consistently an order of magnitude larger than for the same rock types in Zones 3 and 4 (lower Arab-D). The increase in size is believed to be a result of increased leaching in the upper Arab-D. This difference suggests that rocks of similar type from the upper and lower Arab-D will behave differently in terms of their fluid flow and saturation characteristics, and will have different ultimate recoveries.

A New Approach for Building Composite Cores for Corefloods in Complex Carbonate Rocks

2021 ◽  
Author(s):  
Yildiray Cinar ◽  
Ahmed Zayer ◽  
Naseem Dawood ◽  
Dimitris Krinis
Keyword(s):  
Pore Size Distribution ◽  
Pore Size ◽  
Capillary Pressure ◽  
Size Distribution ◽  
Carbonate Reservoir ◽  
Reservoir Rock ◽  
Rock Types ◽  
Pore Structures ◽  
Irreducible Water Saturation ◽  
Wide Range

Abstract Carbonate reservoir rocks are composed of complex pore structures and networks, forming a wide range of sedimentary facies. Considering this complexity, we present a novel approach for a better selection of coreflood composites. In this approach, reservoir plugs undergo a thorough filtration process by completing several lab tests before they get classified into reservoir rock types. Those tests include conventional core analysis (CCA), liquid permeability, plug computed tomography (CT), nuclear magnetic resonance (NMR), end-trim mercury injection capillary pressure (MICP), X-ray diffraction (XRD), thin-section analysis (TS), scanning electron microscopy (SEM), and drainage capillary pressure (Pc). We recommend starting with a large pool of plugs and narrowing down the selection as they complete different stages of the screening process. The CT scans help to exclude plugs exhibiting composite-like behavior or containing vugs and fractures that potentially influence coreflood results. After that, the plugs are categorized into separate groups representing the available reservoir rock types. Then, we look into each rock type and determine whether the selected plugs share similar pore-structures, rock texture, and mineral content. The end-trim MICP is usually helpful in clustering plugs having similar pore-throat size distributions. Nevertheless, it also poses a challenge because it may not represent the whole plug, especially for heterogeneous carbonates. In such a case, we recommend harnessing the NMR capabilities to verify the pore-size distribution. After pore-size distribution verification, plugs are further screened for textural and mineral similarity using the petrographic data (XRD, TS, and SEM). Finally, we evaluate the similarity of brine permeability (Kb), irreducible water saturation (Swir) from Pc, and effective oil permeability data at Swir (Koe, after wettability restoration for unpreserved plugs) before finalizing the composite selection. The paper demonstrates significant aspects of applying the proposed approach to carbonate reservoir rock samples. It integrates geology, petrophysics, and reservoir engineering elements when deciding the best possible composite for coreflood experiments. By practicing this workflow, we also observe considerable differences in rock types depending on the data source, suggesting that careful use of end-trim data for carbonates is advisable compared to more representative full-plug MICP and NMR test results. In addition, we generally observe that Kb and Koe are usually lower than the Klinkenberg permeability with a varying degree that is plug-specific, highlighting the benefit of incorporating these measurements as additional criteria in coreflood composite selection for carbonate reservoirs.

scholarly journals Thermal Diffusion Characteristics in Permafrost during the Exploitation of Gas Hydrate

Geofluids ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yusen Wei ◽  
Youming Xiong ◽  
Zhiqiang Liu ◽  
Jingsheng Lu
Keyword(s):  
Phase Transition ◽  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Thermal Diffusion ◽  
Gas Hydrate ◽  
Reservoir Rock ◽  
Maximum Temperature ◽  
Endothermic Reaction ◽  
Surface Relaxivity

Methane hydrate is the vast potential resources of natural gas in the permafrost and marine areas. Due to the occurrence of phase transition, the gas hydrate is dissociated into gas and water and absorbs lots of heat. The incomprehensive knowledge of endothermic reaction in permafrost sediments still restricted the production efficiency of hydrate commercial development. This endothermic reaction leads to a complex thermal diffusion in permafrost, which directly influences the phase transition in turn. In this research, the heat during the exploitation is transferred in two forms (specific heat and latent heat). Besides, the melting point is not constant but depends on the pore size of the reservoir rock. According to these features, a thermal diffusion model with phase transition is established. To calculate the governing equation, the pore size distribution is obtained by using the nuclear magnetic resonance (NMR) method. The heating tests are conducted and simulated to calibrate the coefficient (i.e., transverse surface relaxivity) of NMR. Then, the temperature field evolution of the hydrate reservoir during the exploitation is simulated by using the calibrated values. The results show that the temperature curves have a typical plateau related to the pore size distribution, which is effective to obtain the surface relaxivity. The heat transfer is remarkably limited by the endothermic effect of the phase transition. The hydrate recovery efficiency may depend largely on the heating capacity of the engineering operation and the rate of gas production. Compared to the conventional petroleum industry, it is significant to control the maximum temperature and temperature distribution in engineering operations during hydrate development. This research on the temperature behavior during onshore permafrost hydrate production could provide the theoretical support to control heat behavior of offshore hydrate production.

scholarly journals Permeability and Rock Fabric from Wireline Logs, Arab-D Reservoir, Ghawar Field, Saudi Arabia

GeoArabia ◽  
2001 ◽  
Vol 6 (4) ◽  
pp. 619-646
Author(s):  
F. Jerry Lucia ◽  
James W. Jennings ◽  
Michael Rahnis ◽  
Franz O. Meyer
Keyword(s):  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Spatial Information ◽  
Water Saturation ◽  
Reservoir Rock ◽  
Petrophysical Properties ◽  
Rock Fabric ◽  
Interparticle Porosity ◽  
Wireline Logs

ABSTRACT The goal of reservoir characterization is to distribute petrophysical properties in 3-D. Porosity, permeability, and saturation values have no intrinsic spatial information and must be linked to a 3-D geologic model to be distributed in space. This link is provided by relating petrophysical properties to rock fabrics. The vertical succession of rock fabrics was shown to be useful in constructing a geologic framework for distributing porosity, permeability, and saturation in 3-D. Permeability is perhaps the most difficult petrophysical property to obtain and image because its calculation from wireline logs requires the estimation of pore-size distribution. In this study of the Arab-D reservoir, rock fabric and interparticle porosity were used to estimate pore-size distribution. Cross-plots of water saturation and porosity, calibrated with rock-fabric descriptions, formed the basis for determining the distribution of rock fabric and pore size from resistivity and porosity logs. Interparticle porosity was obtained from travel-time/porosity, cross-plot relationships. A global porosity-permeability transform that related rock fabric, interparticle porosity, and permeability was the basis for calculating permeability from wireline logs. Calculated permeability values compared well with core permeability. In uncored wells, permeability was summed vertically and the horizontal permeability profile compared with flow-meter data. The results showed good correlation in most wells.

scholarly journals Fractal Properties of Various Clay Minerals Obtained from SEM Images

Geofluids ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Naser Golsanami ◽  
Shanilka Gimhan Fernando ◽  
Madusanka Nirosh Jayasuriya ◽  
Weichao Yan ◽  
Huaimin Dong ◽  
...  
Keyword(s):  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Clay Minerals ◽  
Pore Space ◽  
Reservoir Rock ◽  
Distribution Area ◽  
Sem Images ◽  
Fractal Properties ◽  
Internal Pore

Clay minerals significantly alter the pore size distribution (PSD) of the gas hydrate-bearing sediments and sandstone reservoir rock by adding an intense amount of micropores to the existing intragranular pore space. Therefore, in the present study, the internal pore space of various clay groups is investigated by manually segmenting Scanning Electron Microscopy (SEM) images. We focused on kaolinite, smectite, chlorite, and dissolution holes and characterized their specific pore space using fractal geometry theory and parameters such as pore count, pore size distribution, area, perimeter, circularity, and density. Herein, the fractal properties of different clay groups and dissolution holes were extracted using the box counting technique and were introduced for each group. It was observed that the presence of clays complicates the original PSD of the reservoir by adding about 1.31-61.30 pores/100 μm2 with sizes in the range of 0.003-87.69 μm2. Meanwhile, dissolution holes complicate the pore space by adding 4.88-8.17 extra pores/100 μm2 with sizes in the range of 0.06-119.75 μm2. The fractal dimension ( D ) and lacunarity ( L ) values of the clays’ internal pore structure fell in the ranges of 1.51-1.85 and 0.18-0.99, respectively. Likewise, D and L of the dissolution holes were in the ranges of, respectively, 1.63-1.65 and 0.56-0.62. The obtained results of the present study lay the foundation for developing improved fractal models of the reservoir properties which would help to better understand the fluid flow, irreducible fluid saturation, and capillary pressure. These issues are of significant importance for reservoir quality and calculating the accurate amount of producible oil and gas.

WELL LOGS IN CARBONATE RESERVOIRS

Geophysics ◽  
1960 ◽  
Vol 25 (4) ◽  
pp. 779-853 ◽  
Author(s):  
L. G. Chombart
Keyword(s):  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Water Saturation ◽  
Decisive Role ◽  
Minimum Cost ◽  
Carbonate Reservoirs ◽  
Well Logs ◽  
Rock Types ◽  
Pore Structures

Modern well logs can play an important, often decisive role in the evaluation of carbonate reservoirs, and in well completions therein. To do so however, the logs must be selected and interpreted with due regard for the specific rock “types” and pore structures encountered by each well. Indeed, the basic condition stated applies to all evaluation and completion techniques now in use or conceivable. It becomes vitally important in carbonate reservoirs, however, because of their extraordinary heterogeneity. Characteristically, these reservoirs exhibit significant, often extreme, and always unpredictable variations in pore structure, pore size distribution and fluid content, within very short distances, in any direction. To cope with such a reservoir, an evaluation and logging program adhering to certain principles is most likely to yield valid results and insure better completions and greater ultimate recovery, at minimum cost. First, in every well, the cuttings or cores should be described precisely as to rock types and depths. Second, any techniques used should permit the largest possible number of determinations through the reservoir, so that any existing relationships between pore size distribution, porosity and water saturation may be established on a sound statistical basis. Among logging devices, “focusing” tools meet this requirement best. Third, starting very early in the development of the reservoir, the latter should be cored and logged in key wells, the cores subjected to capillary pressure and other petrophysical tests, and all potentially diagnostic logs run and analyzed in the light of all other data. Fourth, in non‐key wells, the logging program should include only those logs proved most reliable in the key wells for the pore structures encountered and the data desired (usually porosity, water saturation, net ft of pay).

pyGAPS: A Python-Based Framework for Adsorption Isotherm Processing and Material Characterisation

2019 ◽  
Author(s):  
Paul Iacomi ◽  
Philip L. Llewellyn
Keyword(s):  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Surface Area ◽  
Isosteric Heat ◽  
Material Characterisation ◽  
Mixture Adsorption ◽  
Surface Energetics ◽  
Adsorption Processing ◽  
User Friendly

Material characterisation through adsorption is a widely-used laboratory technique. The isotherms obtained through volumetric or gravimetric experiments impart insight through their features but can also be analysed to determine material characteristics such as specific surface area, pore size distribution, surface energetics, or used for predicting mixture adsorption. The pyGAPS (python General Adsorption Processing Suite) framework was developed to address the need for high-throughput processing of such adsorption data, independent of the origin, while also being capable of presenting individual results in a user-friendly manner. It contains many common characterisation methods such as: BET and Langmuir surface area, t and α plots, pore size distribution calculations (BJH, Dollimore-Heal, Horvath-Kawazoe, DFT/NLDFT kernel fitting), isosteric heat calculations, IAST calculations, isotherm modelling and more, as well as the ability to import and store data from Excel, CSV, JSON and sqlite databases. In this work, a description of the capabilities of pyGAPS is presented. The code is then be used in two case studies: a routine characterisation of a UiO-66(Zr) sample and in the processing of an adsorption dataset of a commercial carbon (Takeda 5A) for applications in gas separation.

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