3D PETROPHYSICAL ROCKTYPE AND FZI MODELING OF BERAI CARBONATE RESERVOIR AT BARITO
BASIN, EASTERN KALIMANTAN PROVINCE
J Prasetiya¹, I M Fakhri Putera1, S S Surjono1, J Setyowiyoto1
1 Geological Engineering Department of Gadjah Mada University - Yogyakarta
[email protected] / [email protected]
Abstract : The distribution of carbonate rock type of Berai Formation in the Barito basin has been analyzed using the combination of well log data, core analysis and seismic inversion. 3D object modeling of facies distribution shows the three major units sub-carbonate that forming in the different chronostratigraphy inside the overall Berai carbonate geometry named as Platform A, Platform B and Platform C. Meanwhile, the rocktype classification has been performed by using the different range of petrophysical value (RHOB, NPHI and GR) from six different rock type object according to Dunham (1962) classification. The result of the rock physics analysis shows the petrophysical cut off range of each rock type object consist in Berai carbonate has similar with the actual rock type conditions obtained from core data descriptions.
There are only four rock type object returning from this experimental result., Berai carbonate composed by dominant Packstone and Grainstone, then followed by minor Mudstone and Wackstone. In other words, the petrophysical approach can be used to categorize the variations of rock type in a carbonate field.
The presence of prospect gas inside Berai carbonate reservoir generated from this study are reflected from the largest FZI value, low MSFL resistivity and smallest water saturation value (Sw). The 3D co-kriging estimation model shows the gas distribution based on three parameters above has similar with DST test profile. Best gas play and the largest gases distribution are trapped in Platform A carbonate reservoirs as packstone product (PRT class 3) where deposited at transition of reff flat and lagoon environment. Other gases are located near with Adang Fault association and the accumulation of hydrocarbons in this prospect area may also be derived from the upward migration in the Tanjung Formation to Platform A carbonate layer.
Keywords: Berai Carbonate, Rocktype Classification, Gas Resevoir, 3D Object Modelling, Seismic Inversion, Petrophysics, FZI Model.
1. INTRODUCTION
The research location is one of oil and gas field in Indonesia located in East Kalimantan Province which has been explored by PT. Saka Energy. This research location has been proven produces hydrocarbon in economically valuable. At present, the research area is still very interesting to be developed because its still has the potential resource to produce some additional oil and natural gas.
The nearby area located in the Pekawai field become the main gas bearing target trapped in the Berai Formation carbonate.
Characterization of carbonate reservoir in this research area will be focused on multi-dimension reservoir characterization using raw subsurface data, ie core data, cutting, well log, routine core analysis and seismic data and DST (drill steam test) data to perform the carbonate facies distribution,
precipitation relationship, and petrophysical analysis. The research area consists of 5 exploration wells data availability, namely AD-1 well, BK-1, BL-1, SG-1, LB-1. The AD-1 wellbore is a abandoned well status and it has been closed due to limit hydrocarbon flow at perforation hole (dry well).
However, based on the production test history, the BL-1 wellbore perform the gas flowing with gas rate ranging about 0.152 (MMSCFD). This evidence shows that Berai formation still dominant to produce gas phase. Meanwhile BK-1 and SG -1 are the keys-wellbore, because it consist fairly complete composite log data supported with complete RCAL (routine core analysis) data and DST (drill steam analysis) production test, but showing no hydrocarbon flow taken from Berai formation.
LB-1 wellbore is used as guidance because it has overlaying by seismic line that passes this well but lacks of composite log data.
Basic of rock type classification should clearly divided by using petrography analysis of carbonate rock samples (Amiarsa DP et al., 2012 ; Lucia, 1995), petrography microscopic application for rock type classification purpose in the depth of reservoir taken from integrated coring sampling activity requires a fairly expensive cost. Therefore, the main output of this research is given an alternative method to perform the distribution of rock type by using petrophysical approach based on available secondary data. Thus, major advantage will be impact to reduce the cost of exploration.
According Koesumadinata (2004), the classification of stratigraphic units of the Berai Formation in the research area can be divided into 3 groups of carbonate platforms, namely Lower Berai which is composed of intercalation of limestone, claystone and Marl, then Middle Berai is composed of massive limestone, reefal and sketel limestone, and Top Berai which is also composed of intercalation limestone, claystone and Marl formed in the lagoon and shallow sea deposition environments (refer to Figure 1).
2. CARBONATE ROCK TYPING
Rock typing is situated right at the interface between the disciplines of geology, petrophysics and engineering but generally is published mostly in petrophysical journals and out of sight of the petroleum geologist. Therefore, prior to introducing the new petrophysical rock type (PRT) workflow,
Figure 1. Regional stratigraphy of research area located at Barito basin ( modified after koesoemadinata at al., 2004)
it is critical to review the existing status quo in carbonate rock typing practices and highlight those issues that the new workflow attempts to mitigate. The present status of carbonate rock typing can be summarized in five different schemes. Table 1 summarizes the different schemes in terms of data needs, scale, and suggested advantages and disadvantages that are core for the rationale of developing the new rock typing ‘road map’ according to Skalinsi & Kenter (2014).
Depositional rock types are defined as lithofacies and associated attributes, what is commonly referred to as depositional rock types (DRTs). This includes pure textural classes (Dunham 1962;
Embry & Klovan 1971), generic pore types (Choquette & Pray 1970) or combinations of Dunham/Embry– Klovan classes with grain/pore size, which is represented by the popular Lucia classification (Lucia 1995). Such classifications relate to geologists’ classification of geological attributes, and are relatively simple as they can be linked to depositional environments and associated spatial distributions, but are often not relevant to fluid flow as a result of diagenetic modification.
Rock fabric and inferred pore types (Lucia 1983, 1995, 2007) can provide a link between petrophysics Table 1.
Summary and comparison of current rock typing schemes (adapted from Skalinsi & Kenter, 2014).
and spatial geological trends only if pore systems confirm the fabric element definitions and are relatively uniform. Lønøy (2006) and Ahr (2008) defined rock types by estimation of conventional pore size and texture from porosity distributions by using thin section analysis, but a significant mismatch is observed when comparing resulting rock types with porosity-based classification on the same dataset.
Rock types based on combination of petrophysical partitioning of core and log data known also as electrofacies (Serra & Abbott 1980; Wolff & Pellissier-Combescure 1982) or core porosity–
permeability partitioning on flow units (Amaefule et al. 1993). These methods are linked to petrophysical properties but are lacking the critical link to geology and spatial rules. Serra & Abbott (1980) coined the term ‘electrofacies’, which basically captures the set of log responses that uniquely characterizes a rock unit. In reality, it is ‘log typing’ and was proven successful in several siliciclastic reservoir studies where ‘natural ordering’ in lithofacies successions was correlated with petrophysical ordering observed in the log domain. Using reservoir quality index (RQI) and flow zone indicators (FZI), rock types are classified through artificial binning of the porosity– permeability space (Amaefule et al. 1993). A similar approach, also based on the Kozeny– Carman model (Carman 1939), was proposed by Wibowo & Permadi (2013). Corbett & Potter (2004) and Corbett (2010) to expand the FZI concept, by introducing generalized hydraulic elements (GHEs) that improve the link to geological facies. Although such approaches are more practical and efficient in carbonates with complex pore systems and strong diagenetic modification, the link between log response and geology is generally rather weak and leads to inaccurate spatial prediction and proportions of resulting rock types. Skalinski & Kenter (2015) finally describes a new workflow that intends to address – and mitigate – the gaps specified above and define petrophysical rock types (PRTs) which account for both depositional and diagenetic processes (DRT & DM), are predictable from logs in uncored wells, and can be distributed in a geologically realistic way in 3D Earth models.
This study has also adapted similar PRT concept suggested by Skalinski & Kenter (2014).
However, little modification has been applied in DRT classification. DRT class are divided into facies association terminology obtained from Sarg (1988) with using some adapted carbonate model analogies at Kepulauan Seribu Park refer to the Crevello (2005). These terminology are simplified into three group named back reef-lagoon, reef flat dan reef slope. Seismic inversion volume has been applied for enhance the prediction of facies model distribution. Final goal of this study perform unmapped gas bearing associated with PRT and DRT class in Berai carbonate reservoir.
3. RESEARCH METHODOLOGY
Several steps in this research are summarized below :
1) Initial stages of this research began with literature study. Literature study has been done by review some previous literature related to the similar research objective, flow and supporting theories at similar or different basin location especially in the carbonate rock type modeling subject.
2) The second stage is the data collecting process. The data collection were starting from raw data of previous cores experiment, composite logs and seismic survey. The best data QC will support the achievement of goals in this study.
3) The third stage is data processing and analysis steps. This stage aim to support the achievement of rock type modeling objectives. The first step of processing flow was begin from 2D seismic data input and create the wellbore database by entering raw composite log data and facies log (lithology) obtained both from Gamma Ray log identification and/or cutting information, then the velocity model can be obtained by using the availability of wellbore check shot survey and well tie works based on data density log and sonic logs correction. Limitation of Berai carbonate reservoir boundary (top and bottom boundary) can be mapped by pick several depth marker based on internal well report data.
The further step was continued by picking horizon of the target parasequence. The amplitude curve refer to 2D raw seismic data in the time domain cannot be used as a reservoir modeling properties. These 2D seismic amplitude domain must be resampled into the RHOB petrophysical property value. Conversion process of RHOB log values into seismic attribute known as deterministic seismic inversion work. The results of seismic attributes called as pseudo seismic RHOB. Not overall of seismic attributes such as pseudo seismic NPHI and pseudo seismic Gamma Ray are required in this preliminary step. Because initial trend analysis perform that those three log attributes has an identical trend curve. The distribution of 3D grid model of NPHI and Gamma Ray properties can be later distributed using upscale well log process and co-krigging in pillar grid using RHOB property trends.
4) The final stage is the modeling works. This modeling work will be performed in three dimensional display using Petrel modeling software. Three-dimensional modeling of the facies probability should be located inside the parasquence target. Therefore, top and bottom horizon of Berai Formation is very important, because the result of the facies probability will be used for a trend object to control the spreading of other reservoir property. Furthermore, in this modeling stage, the carbonate reservoir property will be modeled into RHOB volume, NPHI volume and Gamma Ray volume. The distribution of the each grid property will be displayed either vertically or laterally following the identical range of petrophysics value at Berai carbonate depth. Table 2 shows the reading of RHOB value at Berai carbonate depth ranging from 2.1 - 2.8 gr / cc, reading of NPHI value at Berai carbonate depth ranging from 0 - 0.75 fraction, and reading of GR value at Berai carbonate depth ranging from 1 - 40 API. Rock type classification according to Dunham (1962) will be classified within rockphysics crossplot using Rockdoc software and the Flow Zone Indicator (FZI) calculation will be applied in this research. The relation between rocktype and FZI will explained using neural network correlation.
The simplified 3D of PRT modeling flow incorporation with several question related with the absence and percentage of rock class, depositional forming boundary and the presence of potential gas inside rock class are captures in Figure 2.
4. EXPERIMENTAL RESULT 4.1. Marker zone of Berai Formation
Characteristics of composite log can be used to determine the reflection of rocks at several wells that have similarities based on patterns and petrophysical characteristics in the seqeunstratigraphy units.
Table 2. Rock type class input according to the combination of actual data and assumption
No Lithofacies Grid Interval GR log
Grid Interval RHOB log
Grid Interval NPHI log
1 Mudstone* 25 - 40 API 2.1 - 2,39 gr/cc 0 – 0.008 fraction 2 Wackstone* 11 - 16 API 2,39 - 2.41 gr/cc 0.008 - 0.03 fraction 3 Packstone* 16 - 25 API 2.43 - 2.67 gr/cc 0.03 - 0.11 fraction 4 Grainstone* 8 - 11 API 2.41 - 2.43 gr/cc 0.11 - 0.45 fraction 5 Boundstone* 5 - 8 API 2.67 - 2.74 gr/cc 0.45 - 0.6 fraction 6 Crystaline* 1 - 5 API 2.74 - 2.8 gr/cc 0.6 - 0.75 fraction
Note (*) = petrophysics values generated from core depth data (*)= assumption petrophysics values
The marker boundaries of some formations in the study area are obtained through the integration of well log correlation in accordance with Gamma ray log pattern and information obtained based on drilling cutting data. The well correlation is done by mapping the boundary between Tanjung Formation, Berai Formation and Warukin Formation on 4 different wells
The results of the electrofacies section of BK-1 well as shown in Figure 3 show that the stratigraphic markers between Top Berai and Lower Berai have corresponded to the cores position depth validation and also corresponds to the position of the regional stratigraphic columns indicating that the Berai formation has composed of 3 units of different carbonates platform (Lower Berai, Middle Berai and Upper Berai). The presence of others carbonate unit which are located at above of Berai Formation and formed at Warukin Formation age is also known as Bebulu Carbonate (Mobil Adang Kutei inc, 1987).
4.2. Depth structure of Berai Formation
Initial stages in this works included with data input and preliminary geophysical analysis. well tie seismic process has been corrected following the basic 2D seismic line which passed the wellbore location point. The seismic picking process was performed to obtain the horizon constraint from the block sequence of Berai Formation, whereas each of horizon depth marker from the top of Berai and bottom of Berai has assessed from the secondary information at previous drilling report. The results of initial horizon interpretation based on complete picking seismic works can be seen in Figure 4
Figure 2.
Simplified flowchart of 3D PRT modeling conducted in this study
(1)
(2)
(3a) (3b)
(5)
(4)
4.3. Deterministic Seismic Inversion
The pseudo log volume resulted from seismic inversion process includes pseudo seismic RHOB, pseudo seismic Gamma Ray and pseudo seismic NPHI. Representation of log data for acoustic impedance (AI) crossplot were taken from two wellbore points considered with most sensitive to the original seismic line path passed the BL-1 wellbore and SG-1 wellbore. The result of acoustic impedance versus RHOB log, Gammay ray log and NPHI log and its regression function can be seen completely in Figure 5. The function of acoustic impedance (AI) crossplot will be used for a trend volume to spread seismic inversion attribute.
The detail application of trend volume can be used to enhance the difference of lithological unit (facies), as expressed below :
1. Clay facies (shale) is reflected from high scores of gamma ray logs, then the shale object will be estimated by using the trend of pseudo gamma ray volume.
2. Sandstone facies is reflected from low scores of gamma ray reading values, then the sandstone object will be estimated by using the reverse of the trend of pseudo gamma ray volume (trend = 1 / Gamma Ray).
3. Siltstone facies is reflected from high scores of RHOB log reading values, then the siltstone object will be estimated by using the trend of RNOB pseudo volume.
4. Carbonate facies is reflected from low scores of NPHI log reading values, then the limestone object will be estimated by using the reverse of the trend of pseudo NPHI volume (trend = 1 / NPHI).
Figure 3. Electrofacies Analysis and core description validation at BK-1 wellbore
4.4. 3D Facies Model and Platform Geometry
The result of geometric shape analysis inside carbonate facies body (refer to Figure 6) was completed by mapping the dominant pattern of the carbonate facies. The carbonate model perform three major different platform groups and different forming period named as Platform A, Platform B and Platform C. Each platforms body has clearly separated by shale, silt and sand interruption. The distribution of shale facies which are quite dominant with thick carbonate bodies were indicating as the part of platform B and its identified as the onlap product. In some places the presence of sandstone facies indicating as the part of downlap sediment geometry (regression). Figure 7 shows the lateral facial section of the facies model on the SR-1X seismic line located at eastern block of the study area, these slicing shows the position of platform A and C were downlap towards the NE direction. Also, some additional facies slicing shows the absence of platform B in the NW research block. An alternative approach to perform the development patterns of the reef build up will be assessed based on the thickness maps configuration on each platform (Figure 8).
Interpretation of reef buildup geometry body by using direct horison picking in single seismic interpretation works cannot provide a definite picture due to changes in build up controlling factors such as tectonic evolution after burial, carbonate evaporation processes due to meteoric water interruption and disturbing of reef growing process by debris flow material interruption when regression occurs. There some an alternative methods conducted in this study to perform the geometry
Figure 4 (A) Top deep structure Berai, (B) Bottom deep structure Berai
Figure 5. (A) Crossplot log AI versus RHOB log, and RHOB volume trend (B) Crossplot log AI versus Gamma Ray log, and Gamma Ray volume trend
(C) Crossplot log AI versus NPHI log, and NPHI volume trend
layer of carbonate platform. Clean carbonate facies has been filtered into 3 main platform group, each thickness platform obtained from interval calculation between top platform horison and bottom platform horison. This approach is carried out because the seismic resolution in the study area is very low, it cause some difficulty to interpret the appearance of the reef build up. The results of this approach can describe a reef build up pattern configuration and develops pattern within the body of the carbonate parasequence itself. The growth of massive reef buildup in the geometry platform can be identified based on the dominant thickness.
The result of the thickness profile of each platform shows in Figure 8. The development of reef build up on platform A and Platform C is more dominant in the NW of research area, whereas the development of reef build up on platform B is more dominant in the SE of research area. This may provide an overview of the sea level rise fluctuations in the paleogeographic environment of the research block in line with the interpretation of the onlap geometry and downlap position of the platform body following by previous interpretation (Figure 8).
Conceptual geological models of carbonates deposition (refer to Figure 9) also inline with the interpretation result of build-up growing direction and sediment associations. Figure 9 also explains the presence of sand facies from regression products that overlain the platforms A and C. While the presence of shale facies as sediment association of platform B which is part from the transition phase.
According Figure 9, the body of platform B will tend become a lithofacies of reef flat product. This has been proven in the facies association modeling result that showing the platform B in middle of carbonate body contain dominant of reef flat product (refer to Appendix D).
Figure 6.
(A) 3D estimation result of facies distribution (B) Validation 3D facies model with facies log at wellbore crosssection
Figure 7.
(A) Slicing of Facies distribution model at seismic line SR-1X (B) Platform geometry interpretation at seismic line SR-1X
Figure 8. (A) Chronostatigraphy reef buildup at Platform A (B) Chronostatigraphy reef buildup at Platform B (C) Chronostatigraphy reef buildup at Platform C
Figure 9. Tentative geological model of carbonate platform forming (modified after ABGP, 2015)
4.5. Rock Physics Analysis and Rock Type Classification
Several researchers has classified carbonate rock type using the combination of petrophysical and petrography approach (Skalinski M et al., 2014). However, there are no previous literature subject in similar rock typing purpose in the Berai carbonate reservoir. Complete petrophysisc range input following six rock type according to Dunham (1962) has been assessed using the combination of petrophysics value located at core depth data and several assumption input (refer to Table 2). The results of the rockphysics crossplot analysis show that from overall six lithofacies class, there are only 4 carbonate rock type classes in the research field, consist of grainstone, wackstone, packstone and mudstone (Table 3). The crossplot analysis also shows the packstone class is very dominance, then followed by grainstone, mudstone and minor wackstone.
Figure 10 describe that crossplot analysis results using the Rockdoc software showing the more or less similar information obtained from secondary data based on petrographic observations. 3D rocktype estimation model in this study area and rocktype configuration along NW-SE seismic direction can be seen in Appendix A.
Table 3. Final cut off result of rock type class in the research area
No Lithofacies RHOB
Cut off
NPHI Cut off
GR Cut off
1 Mudstone 2.315 – 2.389 gr/cc 0.00 - 0.012 fraction 27.5 – 38 API 2 Wackstone 2.389 – 2.406 gr/cc 0.012 - 0.025 fraction 13 - 15.7 API 3 Packstone 2.45 - 27 gr/cc 0.025 - 0.115 fraction 15.7 - 27.7 API 4 Grainstone 2.406 - 2.45 gr/cc 0.115 - 0.25 fraction 7.0 - 13 API
Figure 10.
(A) Sample case of rock physics Crossplot based on Gamma ray versus RHOB (B) Rockphysics analysis result showing only 4 rocktype class in the research filed
4.6. Porosity Model
Porosity modeling were processed by create AI versus porosity value crossplot. The porosity value taken for this step was collected from core porosity value derived only from availability core samples data at the depth of the SG-1 and BK-1 wellbores. These core porosity value then extracted into log porosity. The limitation of core sampling interval taken at Berai Formation has less representative to describe porosity variation at overall carbonate reservoir depth. Consequently, there will be an high anomaly in the AI versus core porosity reading (Appendix B). The results of the 3D distribution of the porosity model can be seen in Appendix C.
4.7. FZI Model
FZI is the common parameters used to classify flow units in reservoirs which can be seen on equation (1) and (2). Many previous researcher apply this parameter in sandstone reservoirs study and some also apply it to carbonate reservoirs purpose (Amaefule et al, 1993).
Whereas RQI is the reservoir quality index, FZI is the flow zone indicator, is the Permeability and ф is the porosity.
The FZI volume model were generated by using simple volume calculator function, the necessary data input such as porosity and permeability model already has been estimated in the previous step.
Results from the 3D model FZI can be seen in (Appendix B).
4.8. Distribution of Prospect Gas Bearing Zone
The accumulation of gases bearing reservoir in the research area depends on the distribution of the FZI model after it being corrected with initial water saturation, other consideration of gas accumulation in the Berai carbonate reservoir also performed by using MSFL resistivity respons and the simple calculation of the gas saturation value (1-Sw). Recommendations of prospect area was estimated according the highest FZI values, low MSFL resistivity values and small water saturation values (sw).
The prospect zonation of gas accumulation has been validated by using important information of hydrocarbon flow corresponding to the depth of the DST test profile (Appendix C). Best reservoir gas play in study area based on combination of PRT, DRT and DST validation located at rocktype class 3 (packstone) where deposited at transition of reff flat and lagoon environment (Figure 11).
……..Eq (1)
……..Eq (2)
Figure 11.
Recapitulation of potential PRT and DRT for gas reservoir play
5. THE RELATIONSHIP BETWEEN DRT, PRT AND FZI
Variations in rock type produced in this study must be able to explain diagenesis in carbonate rocks which can be represented by FZI parameters. The correlation results using the neural network approach between volume rock type and FZI volume in the study area shows that Grainstone has the largest simultaneous FZI value, followed by packstone and mudstone, while wackestone has the lowest FZI simultaneous value (Figure 12).
Based on facies association model (refer to Appendix D), the back reef-lagoon facies association is dominated by skeletal wackestone and mudstone. Several product forming in this facies association are resulted from low energy environment that create an higher mud content which can be seen from its precipitation texture, commonly characterized as mudstone and wackestone (Sarg 1988). These forming factor at lagoon environment causes the mudstone and wackestone rock type contain a low simultaneous FZI value (refer to Figure 12).
The reef flat facies association is dominated by Foraminifera packestone-grainstone. Several product forming in this in this facies association are resulted due to higher energy environment, its impact in the mud content percentage should be smaller inside rock texture. These forming factor especially at slope environment causes the grainstone rock type have an high simultaneous FZI value (refer to Figure 12).
6. CONCLUSION AND SUGGESTION
Based on the objectives to be achieved from this research, the researchers put forward some conclusions as below:
1. Rock type class of carbonate reservoir obtained from petrophysical approach in this research area from the largest volume to the lowest consecutive respectively are Packstone, Grainstone, Mudstone and Wackstone. The results of this rock type class shows the similar results with actual rocktype conditions obtained based on petrography observations of previous observation.
2. The distribution of carbonate facies model in the study area was formed in rimmed shelf units that composed into three different platform groups, whereas the type of reef that formed in the platforms A and platforms C has dominantly composed form at barrier reef and less patch reef, while platform B has dominantly form at barrier reef.
3. The zonation of high gas bearing in the study area of Berai Formation was found in the platform A carbonate. It evidently due to highest FZI (flow zone indicator) reflection and smallest reading of MSFL resistivity and water saturation.
4. Best gas play in research area after validated with DST information was found on packstone (PRT class 3) at transition of reff flat and lagoon environment. Although the grainstone product (PRT class 4) has the largest FZI value, it contain less gas amount due to high energy forming at slope.
Figure 12.
The result of simultaneous correlation using Neural Network function (A) Correlation result between FZI and rock type volume (B) Correlation result between FZI and lithofacies volume
The suggestions to be conveyed by the author among others:
1. It is necessary to add the SCAL data that should be taken at platform A carbonate depth.
2. Available and existing DST run only covered at platform B and C. Therefore, for further well tests, it is advisable to perform the DST test at the platform A depth interval.
ACKNOWLEDGEMENTS
Authors wishing to acknowledge assistance or encouragement from colleagues, and lot of support from all professional expertise inside joint study member at Saka Pangkah Energy – UGM Sedimentology laboratory.
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APPENDIX A
APPENDIX A.
(A ) 3D model display of rocktype class distribution in the research field (B ) Slicing of rock type model at NW-SE direction
APPENDIX B
APPENDIX B.
(A ) Crossplot function of AI versus Resistivity (B ) Crossplot function of AI versus core porosity (C ) Crossplot function of core permeability versus core porosity
(A)
(B)
(C)
APPENDIX C
APPENDIX C.
(A ) Distribution of Reservoir Properties Model (Porosity, Permeability, FZI, Sw) (B ) Properties validation with DST test profile at BL-1 wellbore
APPENDIX D
APPENDIX D.
(A) 3D neural network estimation of facies association after filtering (B) Slicing of facies association at seismic line xa-12
APPENDIX E
(B) (A)
DISTRIBUTION FZI VOLUME DISTRIBUTION RESISTIVITY VOLUME
FZI PLATFORM A
FZI PLATFORM B
MSFL PLATFORM A
MSFL PLATFORM B
FZI PLATFORM C MSFL PLATFORM C
Low Resistivity High FZI
High FZI High FZI
High FZI
Low Resistivity Low Resistivity
Low Resistivity
APPENDIX E
(A) FZI properties at surface carbonate platform (B) Resistivity properties at surface carbonate platform
