PETROPHYSICS OF THE COOPER BASIN, SOUTH AUSTRALIA

1972 ◽  
Vol 12 (1) ◽  
pp. 23
Author(s):  
Chris R. Porter ◽  
Hugh Crocker
Keyword(s):  
Water Saturation ◽  
South Australia ◽  
Clay Content ◽  
Computer Applications ◽  
Test Results ◽  
Upper Limits ◽  
Porosity And Permeability ◽  
Log Interpretation ◽  
Logging Tool ◽  
Cooper Basin

In the Cooper Basin lithologic types can be recognised and related to logging tool response. Empirical relationships between log readings and porosity and permeability can be developed locally by comparing log readings with core data. Water saturation/porosity relationships are found to be convergently hyperbolic. The effects of clay minerals upon water saturation determination show that only in the low porosity range is correction for clay necessary.A plot of Sw versus Sxo allows prediction of probable test results and has been confirmed by actual Cooper Basin well tests.An attempt to relate clay content to permeability has been successful in estimating tentative upper limits of permeability. Computer applications to log interpretation are utilized in achieving petrophysical parameters.

POROSITY AND PERMEABILITY OF RESERVOIRS AND CAPROCKS IN THE EROMANGA BASIN, SOUTH AUSTRALIA

1986 ◽  
Vol 26 (1) ◽  
pp. 202
Author(s):  
D.I. Gravestock ◽  
E.M. Alexander
Keyword(s):  
Grain Size ◽  
Gamma Ray ◽  
Size Variation ◽  
South Australia ◽  
Clay Content ◽  
Effective Porosity ◽  
Overburden Pressure ◽  
Porosity Reduction ◽  
Porosity And Permeability ◽  
Eromanga Basin

When effective porosity and permeability are measured at simulated overburden pressure, and grain size variation is taken into account, two distinct relationships are evident for Eromanga Basin reservoirs. Reservoirs in the Hutton Sandstone and Namur Sandstone Member behave such that significant porosity reduction can be sustained with retention of high permeability, whereas permeability of reservoirs in the Birkhead Formation and Murta Member is critically dependent on slight porosity variations. Logging tool responses are compared with core-derived data to show in particular the effects of grain size and clay content on the gamma ray, sonic, and density tools, where clay content is assessed from cation exchange capacity measurements. Sonic and density crossplots, constructed to provide comparison with a water-saturated 'reference' reservoir, are advantageous in comparing measured effective porosity from core plugs at overburden pressure with porosity calculated from logs. Gamma ray and sonic log responses of the Murta Member in the Murteree Horst area are clearly distinct from those of all other reservoirs, perhaps partly due to differences in mineralogy and shallower depth of burial compared with other formations.

scholarly journals Elastic-Electrical Rock-Physics Template for the Characterization of Tight-Oil Reservoir Rocks

Lithosphere ◽  
2021 ◽  
Vol 2021 (Special 3) ◽  
Author(s):  
Mengqiang Pang ◽  
Jing Ba ◽  
José M. Carcione ◽  
Erik H. Saenger
Keyword(s):  
Complex Structure ◽  
Rock Physics ◽  
Clay Content ◽  
Rock Properties ◽  
Tight Oil ◽  
Log Data ◽  
Reservoir Rocks ◽  
Porosity And Permeability ◽  
Parallel Network ◽  
Log Interpretation

Abstract Tight-oil reservoirs have low porosity and permeability, with microcracks, high clay content, and a complex structure resulting in strong heterogeneities and poor connectivity. Thus, it is a challenge to characterize this type of reservoir with a single geophysical methodology. We propose a dual-porosity-clay parallel network to establish an electrical model and the Hashin-Shtrikman and differential effective medium equations to model the elastic properties. Using these two models, we compute the rock properties as a function of saturation, clay content, and total and microcrack porosities. Moreover, a 3D elastic-electrical template, based on resistivity, acoustic impedance, and Poisson’s ratio, is built. Well-log data is used to calibrate the template. We collect rock samples and log data (from two wells) from the Songliao Basin (China) and analyze their microstructures by scanning electron microscopy. Then, we study the effects of porosity and clay content on the elastic and electrical properties and obtain a good agreement between the predictions, log interpretation, and actual production reports.

Is water saturation a fracability indicator for organic‐rich, yet low‐clay content, tight carbonate source rock reservoirs?

2021 ◽  
Author(s):  
Hui‐Hai Liu ◽  
Jilin Zhang ◽  
Feng Liang ◽  
Mustafa Basri ◽  
Cenk Temizel ◽  
...  
Keyword(s):  
Source Rock ◽  
Water Saturation ◽  
Clay Content ◽  
Tight Carbonate ◽  
Carbonate Source Rock ◽  
Carbonate Source

scholarly journals Spatial variation in porosity and permeability of the Rupel Clay Member in the Netherlands

2016 ◽  
Vol 95 (3) ◽  
pp. 253-268 ◽  
Author(s):  
Hanneke Verweij ◽  
Geert-Jan Vis ◽  
Elke Imberechts
Keyword(s):  
The Netherlands ◽  
Spatial Variation ◽  
Clay Content ◽  
Burial Depth ◽  
Southeastern Part ◽  
Boom Clay ◽  
The North ◽  
Sealing Capacity ◽  
Porosity And Permeability ◽  
Permeability Data

AbstractThe spatial distribution of porosity and permeability of the Rupel Clay Member is of key importance to evaluate the spatial variation of its sealing capacity and groundwater flow condition. There are only a limited number of measured porosity and permeability data of the Rupel Clay Member in the onshore Netherlands and these data are restricted to shallow depths in the order of tens of metres below surface. Grain sizes measured by laser diffraction and SediGraph® in samples of the Rupel Clay Member taken from boreholes spread across the country were used to generate new porosity and permeability data for the Rupel Clay Member located at greater burial depth. Effective stress and clay content are important parameters in the applied grain-size based calculations of porosity and permeability.The calculation method was first tested on measured data of the Belgian Boom Clay. The test results showed good agreement between calculated permeability and measured hydraulic conductivity for depths exceeding 200m.The spatial variation in lithology, heterogeneity and also burial depth of the Rupel Clay Member in the Netherlands are apparent in the variation of the calculated permeability. The samples from the north of the country consist almost entirely of muds and as a consequence show little lithology-related variation in permeability. The vertical variation in permeability in the more heterogeneous Rupel Clay Member in the southern and east-southeastern part of the country can reach several orders of magnitude due to increased permeability of the coarser-grained layers.

Field Test Results in Malaysia Wells of a New-Generation Slim Pulsed Neutron Logging Tool

2017 ◽  
Author(s):  
P. Millot ◽  
F. K. Wong ◽  
D. A. Rose ◽  
T. Zhou ◽  
R. Grover ◽  
...  
Keyword(s):  
Field Test ◽  
Test Results ◽  
New Generation ◽  
Pulsed Neutron ◽  
Logging Tool

Tight Jurassic Carbonate Reservoir Characterization and Fluid Typing Identification by Integrating Magnetic Resonance, Elemental Spectroscopy and Micro-Resistivity Image Data in Umm Ross Field, West Kuwait, Case Study

2021 ◽  
Author(s):  
Said Beshry Mohamed ◽  
Sherif Ali ◽  
Mahmoud Fawzy Fahmy ◽  
Fawaz Al-Saqran
Keyword(s):  
Magnetic Resonance ◽  
Reservoir Characterization ◽  
Clay Content ◽  
Effective Porosity ◽  
Carbonate Reservoir ◽  
High Resistivity ◽  
Permeability Index ◽  
Rock Quality ◽  
Porosity And Permeability ◽  
Bulk Volume

Abstract The Middle Marrat reservoir of Jurassic age is a tight carbonate reservoir with vertical and horizontal heterogeneous properties. The variation in lithology, vertical and horizontal facies distribution lead to complicated reservoir characterization which lead to unexpected production behavior between wells in the same reservoir. Marrat reservoir characterization by conventional logging tools is a challenging task because of its low clay content and high-resistivity responses. The low clay content in Marrat reservoirs gives low gamma ray counts, which makes reservoir layer identification difficult. Additionally, high resistivity responses in the pay zones, coupled with the tight layering make production sweet spot identification challenging. To overcome these challenges, integration of data from advanced logging tools like Sidewall Magnetic Resonance (SMR), Geochemical Spectroscopy Tool (GST) and Electrical Borehole Image (EBI) supplied a definitive reservoir characterization and fluid typing of this Tight Jurassic Carbonate (Marrat formation). The Sidewall Magnetic resonance (SMR) tool multi wait time enabled T2 polarization to differentiate between moveable water and hydrocarbons. After acquisition, the standard deliverables were porosity, the effective porosity ratio, and the permeability index to evaluate the rock qualities. Porosity was divided into clay-bound water (CBW), bulk-volume irreducible (BVI) and bulk-volume moveable (BVM). Rock quality was interpreted and classified based on effective porosity and permeability index ratios. The ratio where a steeper gradient was interpreted as high flow zones, a gentle gradient as low flow zones, and a flat gradient was considered as tight baffle zones. SMR logging proved to be essential for the proper reservoir characterization and to support critical decisions on well completion design. Fundamental rock quality and permeability profile were supplied by SMR. Oil saturation was identified by applying 2D-NMR methods, T1/T2 vs. T2 and Diffusion vs. T2 maps in a challenging oil-based mud environment. The Electrical Borehole imaging (EBI) was used to identify fracture types and establish fracture density. Additionally, the impact of fractures to enhance porosity and permeability was possible. The Geochemical Spectroscopy Tool (GST) for the precise determination of formation chemistry, mineralogy, and lithology, as well as the identification of total organic carbon (TOC). The integration of the EBI, GST and SMR datasets provided sweet spots identification and perforation interval selection candidates, which the producer used to bring wells onto production.

The Pressure Dependence of the Archie Cementation Exponent for Samples from the Ordovician Goldwyer Shale Formation in Australia

SPE Journal ◽  
2021 ◽  
pp. 1-11
Author(s):  
Zhiqi Zhong ◽  
Lionel Esteban ◽  
Reza Rezaee ◽  
Matthew Josh ◽  
Runhua Feng
Keyword(s):  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Complex Impedance ◽  
Clay Content ◽  
Exchange Capacity ◽  
Shale Formations ◽  
Fluid Saturation ◽  
Confining Pressures ◽  
Log Interpretation

Summary Applying the realistic cementation exponent (m) in Archie’s equation is critical for reliable fluid-saturation calculation from well logs in shale formations. In this study, the cementation exponent was determined under different confining pressures using a high-salinity brine to suppress the surface conductivity related to the cation-exchange capacity of clay particles. A total of five Ordovician shale samples from the Canning Basin, Australia, were used for this study. The shale samples are all illite-rich with up to 60% clay content. Resistivity and porosity measurements were performed under a series of confining pressures (from 500 to 8,500 psi). Nuclear magnetic resonance (NMR) was used to obtain porosity and pore-size distribution and to detect the presence of residual oil. The complex impedance of samples was determined at 1 kHz to verify the change in pore-size distribution using the POLARIS model (Revil and Florsch 2010). The variation of shale resistivity and the Archie exponent m at different pressures is caused by the closure of microfractures at 500 psi, the narrowing of mesopores/macropores between 500 and 3,500 psi, and the pore-throat reduction beyond 3,500 psi. This study indicates that unlike typical reservoirs, the Archie exponent m for shale is sensitive to depth of burial because of the soft nature of the shale pore system. An equation is developed to predict m under different pressures after microfracture closure. Our study provides recommended experimental procedures for the calculation of the Archie exponent m for shales, leading to improved accuracy for well-log interpretation within shale formations when using Archie-basedequations.

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