Depositional Facies, Pore Types and Elastic Properties of Deep‐water Gravity Flow Carbonates

Abstract

Quantitative petrographic analyses of deep-water resedimented carbonates from the Gargano Peninsula (SE Italy) were integrated with petrophysical laboratory measurements (porosity, Pand S-wave velocities) to assess the impact of sedimentary fabrics and pore space architecture on velocity-porosity transforms. Samples of Upper Cretaceous carbonate came from the Monte Sant'Angelo, Nevarra and Caramanica Formations and can be classified into four depositional facies associations: F1, lithoclastic breccias; F2, bioclastic packtones to grainstones; F3, interbedded grainstones-packstones and wackestones; and F4, (hemi-) pelagic mudstones. Five pore type classes were distinguished: I and II, dominant intercrystalline microporosity; IIIa, dominant intergranular macroporosity; IIIb, dominant mouldic macroporosity; and IIIc, mixed intergranular and mouldic macroporosity. Pore type was found to strongly control velocity-porosity transforms, unlike depositional facies associations.The equivalent pore aspect ratio (EPAR), derived from differential effective medium models, is proposed to identify pore types from elastic properties. The EPAR originates from the bulk modulus or shear modulus of the samples (K-and μ-EPAR, respectively). Regardless of porosity values and depositional facies, microporous samples (type I) and samples with dominant intergranular porosity (type IIIa) are characterized by low values of K-and μ-EPAR (<0.22) and by K-EPAR > μ-EPAR. By contrast, samples with dominant mouldic porosity (type IIIb) display high values of K-and μ-EPAR (>0.25 and 0.4 respectively) and K-EPAR < μ-EPAR.High permeability limestones with dominant intergranular porosity cannot be discriminated from low permeability microporous carbonates. The petrophysical classes derived from elastic properties are shown to be distinct from reservoir property-driven rock types. In the present case, a seismic-based poro-elastic model does not match the reservoir property model. Hence, a sedimentary facies model for the studied carbonates cannot accurately represent the petrophysical properties, which are determined by pores types and pore network architecture.

The equivalent pore aspect ratio as a tool for pore type prediction in carbonate reservoirs

“…The pre-salt database includes cores, side-wall plugs, thin-sections and well-logs from two wells located offshore Brazil. Results from the Provence and Gargano case studies are based on the laboratory acoustic database published respectively by Fournier et al (2014) and Hairabian et al (2014) and on a revision of the pore typing for the corresponding samples.…”

“…The measurement protocol for these samples is detailed in Fournier et al (2014) for the Urgonian limestones from Provence and in Hairabian et al (2014) for the Gargano samples. The ultrasonic velocities integrated in the present paper were measured at 40 MPa.…”

“…The stratigraphic framework of the Upper Cretaceous limestones from the Gargano Peninsula is summarized in Figure 2C. The studied carbonate samples exclusively consist of deep-water (several hundred meters of water depth) base-of-slope carbonates, including debris flow, grain flow, turbidite and hemipelagic sediments, deposited at the foot of fault-related escarpments bounding the Apulian Platform (Masse & Borgomano, 1987;Hairabian et al, 2014).…”

Classifying chalk microtextures: Sedimentary versus diagenetic origin (Cenomanian–Santonian, Paris Basin, France)

“…Although chalk varies considerably in texture and biological composition (mudstone to packstone; Quine, ; Quine & Bosence, ; Lasseur et al ., ), it always contains a micritic matrix. This matrix is the main location of porosity in chalk, attributable to the presence of micropores (Moshier, ; Richard et al ., ; Hairabian et al ., ). Currently exploited for hydrocarbon in the North Sea, chalk is a carbonate reservoir, where primary interparticle microporosity is preserved (Arribas et al ., ), but reservoir properties are highly variable (for example, porosity ranges from 5 to 55%; Alam et al ., ).…”

Alternative Ways to Formalize Knowledge for Petrophysical Inversion

“…В открытой печати рядом авторов опубликованы таблицы данных, включающие измерения скорости упругих волн, пористости и проницаемости карбонатных горных пород. Нами использовалась 4 ре разные коллекции образцов, в частности, приведенные в [1][2][3][4][5]. Представление о характере этих взаимосвязей дает рис.…”