Predicting rock mechanical properties of carbonates from wireline logs (A case study: Arab-D reservoir, Ghawar field, Saudi Arabia)

Ameen, Mohammed; Smart, Brian George Davidson; Somerville, James McLean

doi:10.1016/j.marpetgeo.2009.01.017

Cited by 214 publications

(82 citation statements)

References 23 publications

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“…The static Poisson's ratio, (y s ) was derived from the ratio of radial to axial strain. Static bulk modulus (K s ) and shear modulus (G s ) for these samples were calculated using modulus of elasticity and Poisson's ratio as follows (Ameen et al, 2009).…”

Section: Rock Elastic Parametersmentioning

confidence: 99%

A modified fuzzy inference system for estimation of the static rock elastic properties: A case study from the Kangan and Dalan gas reservoirs, South Pars gas field, the Persian Gulf

Ranjbar-Karami

Kadkhodaie

Shiri

2014

Journal of Natural Gas Science and Engineering

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Section: Rock Elastic Parametersmentioning

confidence: 99%

A modified fuzzy inference system for estimation of the static rock elastic properties: A case study from the Kangan and Dalan gas reservoirs, South Pars gas field, the Persian Gulf

Ranjbar-Karami

Kadkhodaie

Shiri

2014

Journal of Natural Gas Science and Engineering

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“…The most common of these methods is conducting the geomechanical tests on cores (Qiu et al, 2013) or estimating the mechanical properties of the rock from the petrophysical logs (Ameen et al, 2009;Fjaer et al, 2008;Yasar & Erdogan, 2004). Regarding limitations of this study, petrophysical logs were used to calculate the mechanical properties of the rock.…”

Section: The Introduction Of the Field And Estimation Of The Geomechamentioning

confidence: 99%

Effect of Rock Properties on ROP Modeling Using Statistical and Intelligent Methods: A Case Study of an Oil Well in Southwest of Iran

Bezminabadi

Ramezanzadeh

Jalali

et al. 2017

Archives of Mining Sciences

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Rate of penetration (ROP) is one of the key indicators of drilling operation performance. The estimation of ROP in drilling engineering is very important in terms of more accurate assessment of drilling time which affects operation costs. Hence, estimation of a ROP model using operational and environmental parameters is crucial. For this purpose, firstly physical and mechanical properties of rock were derived from well logs. Correlation between the pair data were determined to find influential parameters on ROP. A new ROP model has been developed in one of the Azadegan oil field wells in southwest of Iran. The model has been simulated using Multiple Nonlinear Regression (MNR) and Artificial Neural Network (ANN). By adding the rock properties, the estimation of the models were precisely improved. The results of simulation using MNR and ANN methods showed correlation coefficients of 0.62 and 0.87, respectively. It was concluded that the performance of ANN model in ROP prediction is fairly better than MNR method. Keywords: ROP, rock properties, MNR, ANNPrędkość wiercenia jest jednym z podstawowych parametrów charakteryzujących tempo prac wiertniczych. Oszacowanie prędkości wiercenia jest zagadnieniem kluczowym dla inżynierów wiertnictwa, gdyż pozwala na dokładne określenie czasu trwania prac, a co za tym idzie także kosztów operacyjnych. Szacowanie prędkości wiercenia odbywa się na podstawie modelu uwzględniającego parametry pracy oraz parametry środowiskowe. Pierwszy krok obejmuje pozyskanie danych o fizycznych i mechanicznych właściwościach skał na podstawie profilowania geofizycznego otworu. Zastosowano korelację odpowiednich par danych dla pokreślenie wpływu głównych czynników warunkujących prędkość wiercenia. Nowy model obliczania prędkości wiercenia opracowany został w okręgu naftowym Azadegan w południo -wo-zachodniej części Iranu. Symulacje prowadzono w oparciu o metodę wielokrotnej regresji nieliniowej a także przy wykorzystaniu sztucznych sieci neuronowych. Poprzez dodanie danych o właściwościach

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“…Tools that predict rock properties are critical because there are usually limited or no borehole-based rock property data (Gunsallus and Kulhawy, 1984;Edlmann et al, 1998;Ameen and Smart, 2009). Relationships between strength and porosity, density or mineralogy for a specific rock formation have been widely developed based on laboratory tests on rock core from a given field or lithology (Chang et al, 2006;Tamrakar et al, 2007;Rigopoulos et al, 2010;Singh et al, 2012;Karakul and Ulusay, 2013).…”

Section: Introductionmentioning

confidence: 99%

“…Research has shown that several rock properties (mineral hardness, secondary minerals, microstructural damage that includes the presence of microfractures and pores) can influence the predicted rock strength of material (Tuğrul and Zarif, 1999;Ameen and Smart, 2009;Rigopoulos et al, 2010;Coggan et al, 2013;Heap et al, 2014a). Several petrographic and weathering indices related to chemical, petrological and mechanical properties, have been suggested to identify the impact of alteration on rock properties in different lithologies (Ulusay et al, 1994;Tamrakar et al, 2007;Ceryan et al, 2008;Yildiz et al, 2010;Pola et al, 2012Pola et al, , 2014.…”

Section: Introductionmentioning

confidence: 99%

The development and application of the alteration strength index equation

Wyering

Villeneuve

Wallis

et al. 2015

Engineering Geology

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We have developed an alteration strength index (ASI) equation to address the effect of hydrothermal alteration on mechanical rock properties. This equation can be used to estimate a range of rock strengths, comparable to uniaxial compressive strength (UCS), based on rapid analysis of mineralogy and microstructure. We used rock samples from three geothermal fields in the Taupo Volcanic Zone (TVZ) to represent a range of alteration types. These are sedimentary, intrusive and extrusive rocks, typical of geothermal systems, from shallow and deep boreholes (72 measured Depth (mD) to 3280 mD). The parameters used in ASI were selected based on literature relating these aspects of mineralogy and microstructure to rock strength. The parameters in ASI define the geological characteristics of the rock, such as proportions of primary and secondary mineralogy, individual mineral hardness, porosity and fracture number. We calibrated the ASI against measured UCS for our samples from the TVZ to produce a strong correlation (R 2 of 0.86), and from this correlation we were able to derive an equation to convert ASI to UCS. Because the ASI-UCS relationship is based on an empirical fit, the UCS value that is obtained from conversion of the ASI includes an error of 7 MPa for the 50th percentile and 25 MPa for the 90th percentile with a mean error of 11 MPa. A sensitivity analysis showed that the mineralogy parameter is the dominant characteristic in this equation, and the ASI equation using only mineralogy can be used to provide an estimated UCS range, although the error (or uncertainty) becomes greater. This provides the ability to estimate strength even when either fracture or porosity information are not available, for example in the case of logging drill cuttings. This research has also allowed us to provide ranges of rock strengths based solely on the alteration zones, mineralogy, and depth of lithol-ogies found in a typical geothermal field that can be used to update conceptual models of geothermal fields.

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Predicting rock mechanical properties of carbonates from wireline logs (A case study: Arab-D reservoir, Ghawar field, Saudi Arabia)

Cited by 214 publications

References 23 publications

A modified fuzzy inference system for estimation of the static rock elastic properties: A case study from the Kangan and Dalan gas reservoirs, South Pars gas field, the Persian Gulf

A modified fuzzy inference system for estimation of the static rock elastic properties: A case study from the Kangan and Dalan gas reservoirs, South Pars gas field, the Persian Gulf

Effect of Rock Properties on ROP Modeling Using Statistical and Intelligent Methods: A Case Study of an Oil Well in Southwest of Iran

The development and application of the alteration strength index equation

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