Rock Physics Modeling for Organic Matter Effect in Corridor Block, South Sumatera Basin Setyo Wahyu Nurdian and Ign. Sonny Winardhi¹ Institut Teknologi Bandung¹ Abstract Corridor block is located in South Sumatera Basin, this area has Multiple reservoir like basement fracture and sand reservoir, the source rock in this play predicted lies in Lemat formation and younger formation. Geochemical analysis shows that shale in this area has a lot organic matter with high level of maturity. For that reason, rock physics modeling is used to determine the effect of organic matter in geomechanical and elastic properties in Corridor Block. Since TOC data are limited, organic matter estimation technique like Passey, Optimal Superposition and Carbolog are used to determine TOC value along wellbore based on sonic and resistivity logs. Rock physics modeling is carried out from Sun and Liu (2014) schematic by adding the pore shape of inclusion and kerogen subtitution as Solid material, the pore shape of inclusion is estimated from pore space stiffness using Single and Multiple Aspect Ratio schematic based on Zimmerman equation and calculate it into inclusion concept from Kuster Toksoz. Organic matter estimation shows that Carbolog estimation technique has high correlation and hasn’t overestimated value, while the pore space stiffness modeling results show that the Multiple Aspect Ratio scheme provides better correlation than the Single aspect ratio. In other way rock physics modeling result shows every increase volume of organic matter 0.01 will reduce value of density 1.76%, Vp 0.39%, Vs 1,17% and young’s modulus 3,09%, while poisson’s ratio value increase 1,59%, and from geomechanical properties shows that increasing organic matter the value of brittleness index will decrease. In volume TOC modeling, its show that conventional data sensitive for TOC analysis when the average volume TOC is 0.09 (± 18 Wt%). Keywords : Corridor Block, Organic Matter, Rock Physics
Introduction
that has variety pore shape, Xu and Payne (2009) modeled carbonate rock with pore shape of inclusion using aspect ratio value and filled by fluid. After that Sun and Liu (2014) used Xu and Payne schematic research for shale and adding pore shape of inclusion with kerogen as solid material. From this advanced understanding, rock physic modeling for shale with kerogen content as inclusion is needed because kerogen content can affect the elastic properties.
Shale gas commercially used in United State since 2000 even the production rate of shale gas increasing very significant, it’s the reason why people triggered to research about shale especially rock physics modeling on shale with organic matter (kerogen). Bayuk (2008) started rock physic modeling on shale with kerogen, in this method kerogen is assumed to form a network surrounding the clay mineral. But, Bayuk research is less precise because kerogen has its own place in the solid rock that separated from clay mineral. In other ways, rock physic modeling continues to develope especially modeling in carbonate rock
Methodology Rock physiscs modeling in this research is performed using Sun and Liu (2014) schematic research for shale, but this
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Figure 1. Schematic reasearch
modeling requires TOC data along wellbore so the TOC estimation needs to be done before doing rock physics modeling (figure 1). TOC estimations are performed using three techniques estimation and compared, the three techniques are Passey ∆LogR, Optimal Superposition Coefficients ∆LogR and Carbolog. Passey technique using ∆LogR by determining the baseline of sonic and resistivity logs when they are overlaying each other, Passey used the following empirical equation to calculate TOC in shale from ∆logR :
đ?‘‡đ?‘‚đ??ś = đ?‘Žâˆ†đ?‘Ą + đ?‘?đ?‘… −1/2 + đ?‘?
(3)
The estimation result of these methods are compared to obtain the best estimate to be used for rock physics modeling.
đ?‘‡đ?‘‚đ??ś = (∆đ??żđ?‘œđ?‘”đ?‘…)10(2.297−0.1688đ??żđ?‘‚đ?‘€) (1) And then, Liuchao (2011) proposed improved ∆logR technique called optimal superposition coefficient ∆LogR technique, which does not need to determine baseline and calculates TOC directly, the empirical equation is đ?‘‡đ?‘‚đ??ś = đ?‘Žđ?‘™đ?‘œđ?‘”đ?‘… + đ?‘?∆đ?‘Ą + đ?‘?
Figure 2. TOC estimation result
Based on TOC estimation result, optimal superposition coefficient ∆LogR technique has the best correlation value with R2 =0.93, but the result has an over estimated value so this technique less reliable for this area. Whereas Carbolog
(2)
While, Liu (2008) modified Carbolog techniques from France Petroleum Institute (1998), the improved algebraic expression is:
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technique has correlation with R2 =0.9 but there is no an over estimate value so Carbolog technique is considered to be the best TOC estimation technique for this area.
Zimmerman equation, with following this algebraic expression: đ??žđ?‘‘đ?‘&#x;đ?‘Ś 1 = đ??žđ?‘šđ?‘–đ?‘› 1 + đ?œƒ đ?‘˜ đ??žđ?œƒ đ?‘˜= đ??žđ?‘šđ?‘–đ?‘›
(4)
(5)
As a result of the effect of TOC in porosity has been eliminated when interpolating the mineral fraction, then the porosity no longer represents kerogen porosity, whereas in elastic properties there is a kerogen fraction that can’t be separated. Then the volume of kerogen needs to be added to the porosity so the effect of kerogen on elastic properties can be known.
Figure 3. Ternary plot XRD data
The rock physics modeling began with interpolation of mineral fraction from XRD data, but before that, XRD treatment data needs to be done to avoid extreme values estimation, in this area XRD data is taken from shallow marine environment so its contain diverse mineral domination. Filters are applied to data that has more than 10% carbonate minerals and more than 55% clay. After that, mineral fraction interpolation was applied using Krief (1990) and Nur (1992) model, based on modeling result of both methods gives K and Îź reference values for each depth, but comparison from these methods the Krief model provides a better correlation than Nur model in this case. Mineral fraction interpolation gives the value of K, Âľ, đ?œŒđ?‘šđ?‘Žđ?‘Ąđ?‘&#x;đ?‘–đ?‘Ľ , and đ?œƒđ?‘’đ?‘“đ?‘’đ?‘˜đ?‘Ąđ?‘–đ?‘Łđ?‘’ along wellbore, so solid rock modeling can be estimated.
Figure 4. Pore space stiffness
Solid rock modeling estimated using Voigt-Reus-Hill method that can determine the ratio of average strain and average stress in the rock, so the rock stiffness properties can be known. After that solid rock is modeled to be dry rock form using Gassmann equation. In dry rock rock model, the physical properties and pore shape can be known by making a plot between porosity with Kdry/Kmin and inserting a constant k curve using
Figure 5. Dominant data pore space stiffness
The plot between porosity + volume of TOC and Kdry/Kmin is applied at depth of 1000 m to 1300 m (figure 4) where the formation is dominated by shale lithology, then the constant k curves are
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inserted from the value k 0.01 to k 0.1. The value of constant k can indicate the aspect ratio of the pores, smaller constant k value indicates the pore shape is flat (soft), while higher constant k value indicates the pore shape is round (stiff). To see dominance of the data then multiplication between porosity histogram and Kdry/Kmin histogram, from the result of porosity histogram times Kdry/Kmin histogram will show the dominance of the data (Figure 5). Dominance of the data lies in areas with red lines restricted, this is applied to reduce the effect of extreme values of constant k. The value of constant k from the dominant data is used to modeling aspect ratio of the pore.
shceme is obtained from the median histogram of the constant k value for entire data. The median value of k = 0.038 is considered to represent the aspect ratio for entire data (reference aspect ratio). Whereas, multiple aspect ratio scheme is obtained from the value of the constant k that intersects with each value in the plot between porosity + volume of TOC and Kdry/Kmin From the 2 aspect ratio schemes, the values are used to modeling dry rock with pore shape inclusion using Kuster Toksoz theory with P and Q value modified by Wu (1966). After dry rock has a pore inclusion shape the dry rock will be added with fluid using Gassmaan equation and added with kerogen as solid material using Brown-Korringa equation. From the fluid and solid substitution, the value of Ksat and Îźsat will be recalculated in the form of Vp and Vs, and then Vp and Vs results from the calculation will be compared with Vp and Vs values from the measurement, if the error value is minimal then the data rock physics modeling can be analyzed.
Figure 7. shows the correlation between Vp calculations with Vp measurement the single aspect ratio scheme is 0.7 and the multiple aspect ratio scheme is 0.82 and correlation between Vs calculations
Figure 6. Histogram k constant
The aspect ratio selection based on single aspect ratio and multiple aspect ratio scheme, the single aspect ratio
Figure 7. Vp, Vs calculation and measurement
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with Vs measurement the single aspect ratio scheme is 0.91 and the multiple aspect ratio scheme is 0.96. From this comparison the multiple aspect ratio scheme produces a better correlation because this aspect ratio value can represent the elastic properties of the rock and doesn’t impose values on 1 value aspect ratio. Discussion and Result Figure 10. Volume TOC versus Vs
The result from rock physics modeling can be analyzed the effect of organic matter on elastic properties with plots between volume of TOC with density, Vp and Vs (Figure 8, 9, and 10.). Every increasing volume of TOC 0.01 it will reduces the value of density 1,76%, Vp 0.39%, and Vs 1,17%. Vs show the sharper decline than Vp, that indicating organic matter is more sensitive for rigidity parameters (Îź) than compressibility (K).
After the organic matter effect on elastic properties is known, the importance of analyzing the effect of organic matter needs to be known to avoid misleading interpretation. In Figures 11, 12, and 13, there are plot between porosity and volume of TOC with color key Âľ, K and density. From the plot it can be seen that porosity and volume of TOC is not correlated, then the elastic properties value is affected by porosity and volume of TOC.
Figure 8. Volume TOC versus density
Figure 11. Porosity versus volume TOC with density color key
Figure 9. Volume TOC versus Vp
Figure 12. Porosity versus volume TOC with Vp color key
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Gambar 15. AVO intercept Figure 13. Porosity versus volume TOC with Vs color key
If the organic matter analysis is not carried out on modeling it will make elastic properties value is greater and the porosity value will be greater (indirect relationship with density). It’s because the TOC content will reduce the value of ¾, K and density, so if the interpretation without organic matter knowledge it will cause a misleading interpretation. Gambar 16. AVO gradient
For data sensitivity analysis in resolving TOC volumes, there is scheme for separating the plays based on the TOC volume, if the TOC volume value is higher than 0.0165 then it is considered as non-conventional play and if the TOC volume is lower than 0.0165 considered as conventional play (Figure 17.). In nonconventional data modeling, the TOC volume will be multiplied by 2.3.4 and 5 times greater, while in conventional play the TOC volume remains.
Gambar 14. The effect of organic matter on AVO
In the plot between porosity and volume of TOC with AI color key (Figure 14.), there are 2 points that represent the data, red color for data that has a high value of TOC volume and blue has a low TOC volume. From these two points AVO is analyzed with Zoeppritz equation with the lithological contrast above it in the form of sandstone with Vp 2.85 km/s, Vs, 1.6 km/s and density of 2.6 g/cc. From both points in the negative intercept and the positive gradient, but the high value of TOC volume has a lower intercept value and higher gradient (Figure 15. and Figure 16.).
Figure 17. AI with real TOC volume
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In this modeling scheme, all elastic properties value in the non-conventional play are still mixed with conventional play, so the analysis is difficult because the sensitivity of the data doesn’t dissolve the TOC volume properly, for that reason modeling of non-conventional schemes needs to be done to separate from conventional play for knowing the sensitivity of the data to be able to resolve the TOC volume.
an average TOC volume of 0.09, the data can be resolved properly (Figure 18.).
Figure 20. Brittleness Index from elastic properties
Figure 18. AI with TOC Volume x5
Figure 21. Brittleness Index from mineral fraction
đ??ľđ??ź =
đ?‘ đ?‘œđ?‘› đ??śđ?‘™đ?‘Žđ?‘Ś (6) đ?‘ đ?‘œđ?‘› đ??śđ?‘™đ?‘Žđ?‘Ś + đ??śđ?‘™đ?‘Žđ?‘Ś + đ?‘‡đ?‘‚đ??ś
After that the effect of organic matter on geomechanical properties (Figure 20.) shows that every increase TOC volume 0.01 then the value of young's modulus decreased 3.09%, and poisson's ratio increased 1.59%. Young's modulus and poisson's ratio values can show the brittleness index, where the increase of organic matter, the brittleness index will decrease. It was also validated by measurement brittleness index from the mineral fraction (figure 21.)
Gambar 19. AI with TOC Volume real, x2, x3, x4, x5
In the modeling scheme 2x, 3x, 4x and 5x (Figure 19.) 1 point is taken to represent the data so the movement can be seen when the TOC volume increases, when the TOC volume increases, AI in nonconventional play has been separated from AI conventional play, when the average value of TOC volume is 0.09, all elastic properties are separate from conventional play, this proves that with
Conclusions 1. The result of organic matter (TOC) estimation using the carbolog technique is better than Passey ΔLogR
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Mavko, G., and Mukerji, T., 1997. Comparison of the Krief and Critical Porosity Models for Prediction of Porosity and Vp/Vs. Stanford University. Dept. of Geophysics, Rock Physics Laboratory. Stanford, CA 94305-2215, in Society of Exploration Geophysicist.
technique and optimum superposition coefficient ΔLogR technique because the estimation result has a high correlation and there is no an over estimated result. 2. The results of pore modeling in dry rock using multiple aspect ratio scheme is better than single aspect ratio scheme because multiple aspect ratio scheme can represent elastic properties values. 3. The increase TOC volume, reduces value of all elastic properties, while in geomechanichal properties poisson's ratio will increase and young's modulus will decrease. 4. The most sensitive elastic properties to TOC volume is µ (rigidity) when compared to K (compressibility), It’s shows that the organic matter is solid with low elastic properties 5. The effect of modeling data without TOC volume correction will cause porosity values and elastic properties value increase,it cause misleading interpretation. 6. Based on TOC Volume modeling, the effect of kerogen will be resolved properly with Conventional play data when the average TOC volume is 0.09 (± 18 Wt%).
Passey. Q. R., 1990. A Practical Model For Organic Richness from Porosity and Resistivity Logs. AAPG Bulletin V.74, No.12. Sun, S.Z. ,et al. 2014. Method of Calculating Total Organic Carbon from Well Logs and its Application on Rock’s Properties Analysis. AAPG Convention 2014 Zhu, Y. 2012. Improved Rock Physic Models for Shale Gas Resevoir. SEG Annual Meeting. Zhu, Y., et al. 2012. Prediciting Anisotropic Source Rock Properties from Well Log, International Publication. Xu, Shiyu. Dan M. A. Payne. 2009. Modelling Elastic Properties in Carbonate Rocks. The Leading Edge
Acknowledments This research was supported and sponsored by Pusat Data dan Teknologi Informasi Energi dan Sumber Daya Mineral (Pusdatin ESDM), PT. Patra Nusa Data, and Seismic Laboratory Institut Teknologi Bandung. References Bishop, Michele. G., 2001. South Sumatra Basin Province, Indonesia: The Lahat/ Talang Akar-Cenozoic Total Petroleum System. USGS 9905-S. USA.
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