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Rock Physics Modelling for Shale Gas IdentificationMuhammad Saladin Islami

1and Ignatius Sonny Winardhi

1

1

Department of Geophysics, Bandung Institute of Technology.

Abstract

Unconventional resources such as shale gas have been animportant exploration and production target for severalyears. However, seismic characterization of shale gasreservoirs remains challenging due to limited understandingof seismic responses to shale-gas reservoir properties, suchas relationship between total organic content (TOC) andseismic anisotropy. Studying the effective elastic propertiesusing the rock physics model will help improving ourunderstanding of shale gas reservoirs.An integrated rock physics modelling is carried out by

incorporating organic matter as part of the voids space inthe rock. The solid background is estimated using effectivemedium theory by mixing different minerals involved. Asvoid is divided into solid-filled volumes - with kerogeninside - and fluid filled pores, both substitution involvingsolid and fluid are performed. The change in effectivemodulus and velocity anisotropy due to TOC, mineralogyand water saturation are exercised. Results show thatincrease in organic content, generally reduces Vp/Vs ratioand P-impedance, while increases anisotropy parameter.

This experiment, which has been validated to field datameasurement, helps us to better understand and identifyshale gas reservoirs.

Introduction

Organic-rich shales are intrinsically heterogeneous andcomplex, comprising an inorganic framework in whichorganic matter may be dispersed in different amounts. Theinorganic is clay, silt, quartz, carbonate, pyrite, etc. Theorganic (kerogen) appears as nano-particles (macerals) andhydrocarbons. In some cases organics appear as inclusionsin the inorganic background and the inverse in other case.Elastic properties of organic-rich shales, such as velocityand anisotropy parameter, depends on many factor and hardto predict. Mineral composition, porosity, TOC, maturity,

compaction, and brittleness affect the elastic properties ofshales rocks.Studying the effective elastic properties using the rockphysics model will help improving our understanding ofshale gas reservoirs. Several study on the elastic propertiesof shale gas have been developed. Vanorio et al (2008)presents a relationship between maturity and Thomsensanisotropy parameter. Lucier et al (2011) evaluate effect of

gas saturation on acoustic log data from shale gas plays.Zhu et al (2012) developed an improved rock physicsworkflow to incorporate TOC effects. Guo et al (2012)

investigate how to measure the variation of rock brittlenessindex, mineralogy, and porosity in shales using rock

physics templates with proper elastic properties. Sayers(2013) evaluate effect of kerogen on the elastic anisotropyof organic rich shale.In this paper, we construct an integrated shale rock physicsmodel, considering complexity and heterogeneity in shale.The model then applied to log data from Gumai formation.Results show that increase in organic content, generallyreduces Vp/Vs ratio, P-impedance, and brittleness index,while increases velocity anisotropy. This experiment,which has been validated to field data measurement, helps

us to better understand and identify shale gas reservoirs.

Methodology

Our shale-gas rock physics modelling (Figure 1) based onthe workflow that developed by Zhu et al (2012). Thisframework is similar to and can be found in Xu and Payne(2009) for modelling carbonate rocks. First, we calculatethe mixture of different minerals such as clay, carbonates,and quartz for solid background using Voigt-Reuss-Hill

averaging. A dry rock frame is then formed by introducinginclusions into the solid background using Kuster-Toksoz.The inclusions such as pores and cracks characterized byaspect ratio (short axis to long axis ratio) in shales.Experiences from various shale gas formations suggest that

modelled results using organic matter treated as inclusion-filling material generally match well with measuredvelocity and resistivity log data [Zhu et al, 2009]. In thisstudy, organic matter is considered as part of the inclusionspace (void). As void is divided into solid-filled volumes,with kerogen inside, and fluid filled pores, both substitutioninvolving solid and fluid are performed.

Figure 1: Schematic view of shale rock physics model

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index 1 and brittleness index 2. TOC increases from 0 to12% with an interval 1%. Figure 4 shows that increasingTOC, reduces Young modulus, Poisson ratio, , ,brittleness index 1 and brittleness index 2.

AnisotropyWe evaluate effect of TOC and aspect ratio on theanisotropy parameter. TOC increases from 0 to 20% withan increase 1%. Aspect ratio varies from 0.1, 0.3, and 0.5.Figure 5 shows that increasing TOC, increase anisotropyparameter meanwhile increasing aspect ratio, reduces

anisotropy parameter. This results confirm previous resultby Sayers (2013).

Field Data

We use Gumai formation log data with depth interval from6400 -7200 ft. The data include both sonic log (P- and S-wave velocity), resistivity log, water saturation, volume ofshale, and porosity data. Beside of log data, the formationhas geochemical measurement properties such as TOC andvitrinite reflectance. We use grid search inversion to getTOC value from rock physics modelling.

Passey et al (1990) extract TOC from log data usingdifferences between resistivity and porosity log such asdensity, neutron, or sonic log . Sonic log is the mostcommon log that used in Passey method. In this paper, we

use rock physics modelling to extract TOC. The schematicview of our inversion process is illustrated in figure 6. Weuse grid search to find the best aspect ratio and TOC for themodel. We first minimize dry bulk modulus Kdry error tofind aspect ratio and then minimize saturated bulk modulusKsat error to find TOC. In case, data is limited ,there is noS-wave sonic log data, we can use P-wave modulus Minstead of bulk modulus K. For this field data, we initiatethe population of aspect ratio in range 0.05-0.3 and TOC in

range 0-0.06 (0-6 %).

The inversion result is shown in figure 7. Aspect ratio andTOC can be extracted using this rock physics inversion.Each modulus is well reconstructed similar as the observeddata with error distribution close to zero. The observedTOC is plotted with the inversion TOC. Error betweenobserved and calculated TOC using this inversion is 0.11%.On the other hand, error from Passey method is 2.9%.Figure 8 show several elastic properties, aspect ratio, TOC,

and anisotropy parameter plot. From forward modelling,we know that shale gas reservoir have low elasticproperties caused by the presence of organic matter. Based

on that phenomenon, our interest zone is between 6940 7020 ft (yellow highlighted area). This results is confirmedby the presence of gas show from logging data. Anisotropyparameter, epsilon and gamma, significantly caused by theshape of pore geometries than by organic matter because

same TOC value can have different anisotropy value.Aspect ratio have reverse relation with anisotropy value.

Figure 4: Increasing TOC, reduces elastic properties. Left

side (from top to base): Young Modulus, Poisson Ratio,

and Brittlenes Index 1. Right side (from top to base): ,, and Brittlenes Index 2.

Figure 5: Crossplot TOC vs anisotropy parameter withrespect to aspect ratio (ar). Red (ar=0.5), Green (ar=0.3),and Blue (ar=0.1). Increasing TOC, increase anisotropy

parameter.

Figure 6: Rock physics inversion workflow.

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Conclusions

Shale gas reservoir can be identified from the elastic

properties. Generally, it has low velocity, low AI, and lowbrittleness index. TOC and aspect ratio can be extracted byusing rock physics modelling inversion. Anisotropy factor

significantly caused by pore geometries than by thepresence of organic matters. high aspect ratio will have lowanisotropy parameter. Further research is needed to knowthe effective brittleness index for hydraulic fracturing.

References

Guo et al, SEG Las Vegas 2012 Annual Meeting, 2012

Lucier et al, The Leading Edge, 2011

Passey et al, AAPG Buletin, v.74, no.12, P.1777-1794,1990

Sayers, C.M, Geophysics, v.78, no.2, P.D65-D74, 2013

Vanorio et al, SEG Las Vegas 2008 Annual Meeting, 2008

Xu, S. and Payne, M.A., The Leading Edge, 2009

Zhu et al, SEG Las Vegas 2012 Annual Meeting, 2012

(a) (b)

Figure 7: Inversion result (a) from left to right : aspect ratio, dry bulk modulus, and error distribution. (b) from left to right :

saturated bulk modulus, saturated shear modulus, and TOC

Figure 8: from left to right : velocity, acoustic impedance, brittleness index 1, brittlenes index 2, TOC inversion, aspect ratio,

epsilon, and gamma. Our interest zone between 6940-7020 ft