Empirical relations between strength and static and dynamic elastic properties of Asmari and Sarvak limestones, two main oil reservoirs in Iran

Najibi, Ali Reza; Ghafoori, Mohammad; Lashkaripour, Gholam Reza; Asef, Mohammad Reza

doi:10.1016/j.petrol.2014.12.010

Cited by 181 publications

(64 citation statements)

References 13 publications

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“…Comparison between the developed correlation and Najibi et al (Najibi et al 2015) correlation on Laboratory data measurements …”

Section: Fig 18mentioning

confidence: 93%

“…Najibi's correlation underestimated the static Young's modulus for all core data. Figure 17b shows that the root-mean-square error was 15.6 for Najibi et al (Najibi et al 2015) correlation. The new developed correlation yielded very good match for the static Young's modulus compared to the measured laboratory core data.…”

Section: Case #3mentioning

confidence: 94%

“…Canady's correlation (Canady 2010) again overestimated the static Young's modulus values and also did not match the core data. Figure 17b shows that the new correlation gave the lowest root-mean-square error of 9.4, while the root-meansquare error for Wang's correlation (Wang 2000), Canady's method (Canady 2010), and Najibi et al (Najibi et al 2015) was 25.2, 11.8, and 15.6, respectively. Based on the results obtained from the previous four cases, it can be concluded that the new correlation provided the best match for the core data with the lowest root-mean-square error as compared with the previous methods.…”

Section: Case #3mentioning

confidence: 98%

“…Relying on one or two parameters in the prediction process might lead to wrong estimation of the static Young's modulus. Najibi et al (Najibi et al 2015) introduced an empirical correlation to determine the static Young's modulus from compressional velocity (Eq. 11).…”

Section: Case #3mentioning

confidence: 99%

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Development of a new correlation to determine the static Young’s modulus

Elkatatny

Mahmoud

Mohamed³

et al. 2017

J Petrol Explor Prod Technol

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The estimation of the in situ stresses is very crucial in oil and gas industry applications. Prior knowledge of the in situ stresses is essential in the design of hydraulic fracturing operations in conventional and unconventional reservoirs. The fracture propagation and fracture mapping are strong functions of the values and directions of the in situ stresses. Other applications such as drilling require the knowledge of the in situ stresses to avoid the wellbore instability problems. The estimation of the in situ stresses requires the knowledge of the Static Young's modulus of the rock. Young's modulus can be determined using expensive techniques by measuring the Young's modulus on actual cores in the laboratory. The laboratory values are then used to correlate the dynamic values derived from the logs. Several correlations were introduced in the literature, but those correlations were very specific and when applied to different cases they gave very high errors and were limited to relating the dynamic Young' modulus with the log data. The objective of this paper is to develop an accurate and robust correlation for static Young's modulus to be estimated directly from log data without the need for core measurements. Multiple regression analysis was performed on actual core and log data using 600 data points to develop the new correlations. The static Young's modulus was found to be a strong function on three log parameters, namely compressional transit time, shear transit time, and bulk density. The new correlation was tested for different cases with different lithology such as calcite, dolomite, and sandstone. It gave good match to the measured data in the laboratory which indicates the accuracy and robustness of this correlation. In addition, it outperformed all correlations from the literature in predicting the static Young's modulus. It will also help in saving time as well as cost because only the available log data are used in the prediction.

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“…Comparison between the developed correlation and Najibi et al (Najibi et al 2015) correlation on Laboratory data measurements …”

Section: Fig 18mentioning

confidence: 93%

Section: Case #3mentioning

confidence: 94%

Section: Case #3mentioning

confidence: 98%

Section: Case #3mentioning

confidence: 99%

See 2 more Smart Citations

Development of a new correlation to determine the static Young’s modulus

Elkatatny

Mahmoud

Mohamed³

et al. 2017

J Petrol Explor Prod Technol

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“…For example Najibi et al (2015), presented a study on limestone data from Iran and give an additional overview of published equations. Chang et al (2006) also give an overview of ____________________ gocene-Miocene) also from the Molasse basin and additionally Triassic Buntsandstein samples are taken from a quarry.…”

Section: Introductionmentioning

confidence: 99%

Petrographic coded model concept for the correlation between geomechanical and elastic properties and its application on log data for Alpine rocks

Gegenhuber¹,

Schifko²,

Pittino³

2017

AJES

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The derivation of geomechanical properties, like the rock strength, from elastic properties is an important topic not only in the oil industry, but also for geothermal projects, tunnelling or mining. It is one of the crucial parameters for the stability of the borehole, the drilling rate or stability of an underground mine. The idea of applying the petrographic model concept which involves an additional mineralogical influence was developed for the correlation between compressional wave velocity and uniaxial compression strength for sandstone, limestone, anhydrite and gypsum. The first step of this model is to define or assume the solid matrix properties of the dense host material, which covers therefore the influence of the rock type/lithology. The second step implements fractures/cracks with an inclusion model. Samples are selected from the surface and from borehole. A newly measurement set-up was developed to measure velocities during the uniaxial compression test. Additionally, the application of the derived equations on log data is tested. The presented correlations using the petrographic coded model concept shows good first results. Correlation between uniaxial compression strength and compressional wave velocity can be derived using the petrographic coded model concept (inclusion and defect model). The derived equations can easily be applied on log data and also deliver good results for the uniaxial compression strength in the borehole.Die Ableitung geomechanischer Parameter, wie die Gesteinsfestigkeit, von elastischen Eigenschaften, ist nicht nur in der Ölindus-trie, sondern auch für Geothermie Projekte, im Tunnelbau oder im Bergbau, von großem Interesse. Es ist eine der Kerngrößen für die Stabilität des Bohrlochs, der Bohrgeschwindigkeit oder der Stabilität eines Untertagebergbaues. Die Idee der Anwendung des petrographisch kodierten Modelkonzeptes, welches zusätzlich den Mineraleinfluss beinhaltet, wurde für die Korrelation zwischen Kompressionswellengeschwindigkeit und einaxialer Druckfestigkeit für Sandstein, Kalkstein, Anhydrit und Gips entwickelt. Der erste Schritt dieses Models ist es, die Matrixwerte von der dichten Materialmasse, welche den Einfluss des Gesteinstyps/der Lithologie beinhaltet, zu bestimmen oder anzunehmen. Der zweite Schritt implementiert Risse/Brüche mit einem Inklusionsmodel. Es wurden Oberflächenproben und Bohrkerne ausgewählt. Ein neuer Messaufbau wurde entwickelt um Geschwindigkeiten währen eines einaxialen Druckversuches zu messen. Zusätzlich wurde die Anwendung der abgeleiteten Gleichungen an Bohrlochdaten getestet. Die hier präsentierten Korrelationen unter Verwendung des petrographisch kodierten Models zeigen erste gute Ergebnisse. Korrelationen zwischen einaxialer Druckfestigkeit und Kompressionswellengeschwindigkeit können mit dem Model (Inklusionen und Defekt Model) abgeleitet werden. Die Gleichungen können weiter leicht an Bohrlochdaten angewendet werden und erste Ergebnisse liefern gute Werte für die einaxiale Druckfestigkeit im Bohrloch.

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Rock Strength Estimation from Petrophysical Logs Through Core Data Calibration in Low Porosity and Low Permeability Carbonate Rocks

Saha¹,

Vishal²

2022

Handbook of Petroleum Geoscience

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Empirical relations between strength and static and dynamic elastic properties of Asmari and Sarvak limestones, two main oil reservoirs in Iran

Cited by 181 publications

References 13 publications

Development of a new correlation to determine the static Young’s modulus

Development of a new correlation to determine the static Young’s modulus

Petrographic coded model concept for the correlation between geomechanical and elastic properties and its application on log data for Alpine rocks

Rock Strength Estimation from Petrophysical Logs Through Core Data Calibration in Low Porosity and Low Permeability Carbonate Rocks

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