Mineralogical modelling and petrophysical parameters in Permian gas shales from the Roseneath and Murteree formations, Cooper Basin, Australia

Jadoon, Quaid Khan; Roberts, Eric M.; Blenkinsop, Tom; Wüst, Raphael A.J.; Shah, Syed Anjum

doi:10.1016/s1876-3804(16)30031-3

Cited by 15 publications

(7 citation statements)

References 11 publications

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“…The petrophysical parameters constitute basic data for diverse investigations or practice works of economic interest [43][44]. The knowledge of these parameters allows for the optimal use of soils; for example: hydraulic conductivity (Fig.…”

Section: Analysis Of the Apparent Resistivity Valuesmentioning

confidence: 99%

Using Electrical Profiling to Determine Soil Petrophysical Parameters in an Agricultural Field

Contreras¹,

Ortiz²,

Peinado-Guevara³

et al. 2017

Pol. J. Environ. Stud.

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The soils of a 10-ha agricultural field in the municipality of Guasave, Sinaloa, were characterized through electrical measurements and determination of petrophysical parameters. An electrical profiling survey for AB/2 = 2 m and 3 m was carried out in the study area. The apparent resistivity, soil moisture, and pore water salinity values were recalculated into petrophysical values using the Petrowin software, resulting in fine (clay+silt) content, and porosity and hydraulic conductivity maps. Using the Bouyoucos method, soil texture was determined for 30 soil samples collected in points matched with EP measurement points. The results of textural analysis indicated that the study area is quite complex, with the presence of eight textures of soil, and soil moisture and groundwater salinity variable in twice and five times, respectively. The fines content values obtained from Petrowin software and the Bouyoucos method were compared showing a high correlation (R = 0.91), giving reliability to fast and inexpensive techniques such as electrical profiling for the determination of the soil petrophysical parameters in extensive agricultural land.

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Section: Analysis Of the Apparent Resistivity Valuesmentioning

confidence: 99%

Using Electrical Profiling to Determine Soil Petrophysical Parameters in an Agricultural Field

Contreras¹,

Ortiz²,

Peinado-Guevara³

et al. 2017

Pol. J. Environ. Stud.

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“…A detailed reservoir characterization and quality assessment can be achieved using integration between the petrographical (thin sections, SEM and XRD data) and the pertrophysical (core and well log data) studies have been widely used by many authors in different basins worldwide (Harrison and Dutton, 1991;Abd El-Gawad, 2007;Makhloufi et al, 2013;Chihi et al, 2013;Shalaby et al, 2013Shalaby et al, , 2014Ghanizadeh et al, 2015;Nabawy, 2016, 2018;Jadoon et al, 2016;Azeem et al, 2017;Benaafi et al, 2018;Nabawy et al, 2018aNabawy et al, , 2018bNabawy et al, , 2020aAbuamarah et al, 2019;Radwan et al, 2021aRadwan et al, , b, 2022aAbdelwahhab et al, 2022). Therefore, the main target of the present study is to characterize the Mangaa reservoir in Taranaki Basin using integration between petrographical, and petrophysical well logs and core data.…”

Section: Introductionmentioning

confidence: 99%

Hydrocarbon prospectivity of the miocene-pliocene clastic reservoirs, Northern Taranaki basin, New Zealand: integration of petrographic and geophysical studies

Radwan

Nabawy

2022

J Petrol Explor Prod Technol

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In this study, it is aimed to characterize the Early pliocene sandstone (EP-SD) and the Late Miocene-Early Pliocene Mangaa sandstone reservoirs and the efficiency of their sealing cap rocks using the petrographical and petrophysical data of these sandstone zones in northern Taranaki basin, New Zealand. The prospective potential reservoirs were studied using impregnated thin sections, XRD data analysis, and well log data (self-potential, gamma-ray, sonic, density, neutron, shallow\deep resistivity and PEF) to characterize the reservoir zones, in addition to Mercury intrusion capillary pressure data (MICP) to check the efficiency of some potential seals. The EP-SD and the Mangaa sandstone units are typically poorly consolidated very fine sandstone to siltstone, with porosities averaging 25%. The sands are composed of quartz (38.3–57.4%), with common feldspars (9.9–15.2% plagioclase, and 2.7–6.3% K-feldspars) and up to 31.8% mica. In Albacore-1 well to the north of the Taranaki Basin, the Mangaa formation includes three separate for each of the EP-SD zones (EP-SD1, EP-SD2, and EP-SD3), and the Mangaa sequence (Mangaa-0, Mangaa-1, and Mangaa-2). The thin section studies indicate that, the studied samples are grouped into greywackes, arenites and siltstone microfacies with much lithic fragments and feldspars, sometimes with glauconite pellets. From the XRD data, it is achieved that the mineral composition is dominated by quartz, mica/illite, feldspars, and chlorite. The petrophysical investigation revealed absence of pay zones in the EP-SD zones, and presence of thin pay zone with net thickness 5.79 m and hydrocarbon saturation of about 25.6%. The effective porosities vary between 23.6 and 27.7%, while the shale volume lies between 12.3 and 16.9%. Although the shale content is relatively low, the relatively high API (50–112 API of average 75 API) is contributed by the relatively high K-feldspar content and intercalations with thin siltstone and muddy siltstone beds. Sealing units include the intra-formational seals within the Mangaa sequence, mudstones and fine grained units overlying the Mangaa and further intra-formational mudstones, within the shallower EP-SD units. The efficiency of these seals indicates the capability to trap 16.4–40.6 m gas or 17.4–43.0 m oil which is relatively low in correlation with their efficiency in the central parts of the Taranaki Basin Overlying the primary seals, mudstones of the Giant Foresets Formation provide additional regional seal.

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“…Compared with the rock physics experiments, DRP, as a numerical simulation method, is cheap and effective to obtain the various rock properties (Yang et al, 2021;Golparvar et al, 2018). At present, 3D digital rocks have been used to evaluate the porosity and analyze the pore radius and connectivity (Anselmetti et al, 1998;Jadoon et al, 2016;Wu et al, 2019a), elastic moduli (Hossain et al, 2019;Miao et al, 2015), permeability (Wu et al, 2019b;Nie et al, 2016), and electrical properties. It is important to construct accurate digital rocks for the simulation of rock physical properties (Wei et al, 2018;Wu et al, 2020a).…”

Section: Introductionmentioning

confidence: 99%

Rock Physical Modeling of Tight Sandstones Based on Digital Rocks and Reservoir Porosity Prediction From Seismic Data

et al. 2022

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Digital rock physics (DRP) has become an important tool to analyze the characteristics of pore structures and minerals and reveal the relationships between microscopic structures and the physical properties of reservoirs. However, it is greatly difficult to upscale the rock physical parameters, such as P-wave velocity, S-wave velocity, and elastic moduli, from DRP to large-scale boreholes and reservoirs. On the other hand, theoretical rock physical modeling can establish the internal relationship between the elastic properties and physical parameters of tight sandstones, which provides a theoretical basis for seismic inversion and seismic forward modeling. Therefore, the combination of digital rock physics and rock physical modeling can guide the identification and evaluation of the gas reservoir’s “sweet spot.” In this study, the CT images are used to analyze the mineral and pore characteristics. After that, the V-R-H model is used to calculate the equivalent elastic moduli of rocks containing only the mineral matrix, and then, the differential equivalent medium (DEM) model is used to obtain the elastic moduli of dry rocks containing minerals and pores. Subsequently, the homogeneous saturation model is used to fill the fluids in the pores and the Gassmann equation is used to calculate the equivalent elastic moduli of the saturated rock of tight sandstones. Rock physical modeling is calibrated, and the reliability of the rock physical model is verified by comparing those with the logging data. Afterward, the empirical relationship of rock porosity established from CT images and rock elastic moduli is obtained, and then, the elastic parameters obtained by seismic data inversion are converted into porosity parameters by using this empirical relationship. Finally, the porosity prediction of large-scale reservoirs in the study area is realized to figure out the distribution of gas reservoirs with high porosity. The results show that the H3b and H3c sections of the study area exhibit higher porosity than H3a. For the H3b reservoir, the northeast and middle areas of the gas field are potential targets since their porosity is larger than that of others, from 10% to 20%. Because of the effects of the provenance from the east direction, the southeast region of the H3c reservoir exhibits higher porosity than others.

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Mineralogical modelling and petrophysical parameters in Permian gas shales from the Roseneath and Murteree formations, Cooper Basin, Australia

Cited by 15 publications

References 11 publications

Using Electrical Profiling to Determine Soil Petrophysical Parameters in an Agricultural Field

Using Electrical Profiling to Determine Soil Petrophysical Parameters in an Agricultural Field

Hydrocarbon prospectivity of the miocene-pliocene clastic reservoirs, Northern Taranaki basin, New Zealand: integration of petrographic and geophysical studies

Rock Physical Modeling of Tight Sandstones Based on Digital Rocks and Reservoir Porosity Prediction From Seismic Data

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