IPA05-G-034
PROCEEDINGS, INDONESIAN PETROLEUM ASSOCIATION Thirtieth Annual Convention & Exhibition, August 2005
SEISMIC RESERVOIR CHARACTERIZATION OF THE ABADI GAS FIELD, MASELA PSC BLOCK, WEST ARAFURA SEA, EASTERN INDONESIA.
Shinji Matsuura*
Shin Saito*
Yoshiro Ishii*
Hiromi Honda*
Ayato Kato**
Takao Yagi***
ABSTRACT
This paper presents the results of seismic inversion work of the Abadi gas field in Masela PSC Block, West Arafura Sea, Eastern Indonesia. The field was discovered in 2000 and has undergone a series of reservoir characterization programs, which include seismic inversion, depth conversion and reservoir geology. The basic data sets for this study consist of 2,060km2 of 3D seismic, and well data of Abadi-1, Abadi-2 and Abadi-3.
A discrepancy exists between velocity measurements in wireline logs and those in laboratory core sample measurements. It is essential to reconcile this discrepancy for calibration of seismic-inverted properties. A method of rock physics modeling was applied, first in order to correct the log data and secondly to optimize the seismic inversion.
Quantitative relationships were identified between the seismic-inverted and petrophysical properties by the modeling. Intensive ultra-sonic measurements on core samples were conducted to properly calibrate the seismic-inverted properties.
Seismic inversion was conducted for the 3D seismic data and the modeled sonic and density logs of the three wells. Three Elastic Impedance (EI) volumes
were constructed from three reflection-amplitude-angle stacks. Based on the relationships between seismic-inverted and petrophysical properties, the EI volumes were converted into a total porosity volume and a lithology
* Inpex Masela, Ltd.
** Japan Oil, Gas and Metals National Corporation
*** Inpex Browse, Ltd
indicator volume which discriminates sand and shale.
The volumes indicate a 3D distribution of the sandy reservoir which is used as an input for dynamic simulation of the Abadi Field’s gas production. The seismic inversion provides high resolution lithological contrasts that correspond to stratigraphic boundaries.
Reserve estimates of the Field and their uncertainty have been repeatedly updated based on the successive programs of the reservoir characterization study.
Seismic data reprocessing and wavelet analysis are being conducted in order to improve the quality of both the seismic inversion and depth structure mapping for better reservoir characterization in the future.
INTRODUCTION
This paper presents a case history of rock physics modeling and seismic inversion for the Abadi Gas Field, Masela PSC, West Arafra Sea, Eastern Indonesia.
The Abadi gas field is located within the Masela PSC block in the west Arafra Sea, Eastern Indonesia, along the Indonesia-Australia international boundary (Figure 1, Nagura, et al., 2003). Figure 2 shows the general structure of the field. Figure 3 shows the seismic cross section intercepting the existing three wells. The field was discovered in late 2000 by the Abadi-1 well. A 3D seismic survey of 2,060 km2 was conducted in 2001. Two appraisal wells, Abadi-2 and Abadi-3 followed the 3D seismic in 2002, both of which confirmed the gas column. The 3D seismic was reprocessed in 2002.
© IPA, 2011 - 30th Annual Convention Proceedings (Volume 1), 2005
The lithostratigraphy of the Abadi field area is shown in Figure 4. The inserted column in the figure is the acoustic impedance (P-wave velocity multiplied by density) of Abadi-1, which is calculated from sonic and density logs. Three seismic markers are mapped for the study; MH4000: Base Albian Jamieson Formation; MH5000: Top Callovian Plover Formation (Figures 3 and 4); and MH6000:
Intra-Plover marker, which defines the boundary of the Upper and Lower Plover. The Plover Formation is a deltaic to shallow marine sandstone dominant unit and the primary reservoir of the Abadi field. The pay zone is an interval between the MH5000 through to the MH6000.
The field has been under a series of reservoir characterization programs, which include seismic inversion, depth conversion, geological modeling and reservoir simulation. The work is based on 3D seismic data, and on wireline logs and their petrophysical analysis, of the Abadi-1, Abadi-2 and Abadi-3 wells. Reprocessed, finite difference post-stack migrated 3D seismic data were used for this study.
A significant discrepancy was observed between wireline logging data of sonic velocity and laboratory measurements of ultrasonic wave velocity of core samples (Figure 5). These are both basic calibration data for the seismic inversion. Rock physics modeling was used to resolve the discrepancy, and to give a robust basis for the seismic inversion.
DATA AND METHODS Core Measurements
Velocity measurements were performed for core samples of the Upper Plover Formation from the three wells. The measurements were made to give a calibration for quantitative seismic reservoir characterization.
The core plugs were first trimmed at both ends using a diamond slab saw, to cylinder-shaped samples with the diameter of 3.8 cm and length ranging from 3 to 5 cm. The core plugs were soaked in ethanol overnight, and then soaked in heated toluene for 2 weeks, in order to perfectly clean any remaining oil within the pores. After this solvent cleaning the cores were dried in a chamber at 60°C for more than 4 hours.
P-wave velocity (Vp) and S-wave velocity (Vs) both of the dry and of brine saturated samples were measured under the in-situ condition of reservoir rock, at a pressure of 6,500 psi (44.8GPa) and a temperature of 120° C (393° K). Additionally, porosity and weight of the samples were measured;
bulk density and grain density were calculated from the measurements.
Rock Physics Modeling
The rock physics modeling was conducted to produce synthetic logs from the result of petrophysical log analysis of the Upper Plover Formation in the three wells. The methodology applied for this study was to construct modeled sonic and density logs from the result of the petrophysical analysis.
The method chosen for the rock physics modeling was that proposed by Xu and White (1995). First, the mineral components (typically, quartz framework-grains and clay matrix in petrographical sense) are mixed together, and the velocity of the matrix is obtained using the time averaging equation from the velocities of each mineral component weighted proportionally to the volume fraction of the components. Next, ellipsoidal empty pore spaces are included into the matrix, and we obtain this velocity by the formulae proposed by Kuster and Toksoz (1974). Finally, fluids are introduced into the pores using Gassmann’s Equations (e.g., Mavco, et al., 2002). The density of the rock is calculated simply by using the arithmetic mean of the density of mineral components and pore fluids weighted proportionally to their volume fractions. The measured Vp, Vs and density logs are not used directly for the model construction.
The volume fractions of mineral components (quartz, clay, pyrite, etc.) and pore fluids (gas, formation water, bound water, and mud filtrate) were calculated from the petrophysical analysis based on the dual water method for shaly formations.
In performing the modeling, we chose parameters to match modeled log-values with the Vp, Vs and density measurements of the core samples, and with the recognized trends of measured sonic logs on cross plots, which are Vp-PHIT, Vs-PHIT, Vp-Vs, etc. (see Figures 6 – 8). Vp, Vs, density, and porosity of the core, all of which were correlated at in-situ conditions were used in comparison with the modeling. We
assumed the core porosity to be effective porosity, and corrected to total porosity by adding a volume of bound water using the results of petrophysical analysis at the corresponding depth. Our correlation between the core data and wireline logging data is based on geometrical patterns of values versus depths.
This correlation method is considered to give a reliable result.
By the comparison of modeled logs with measured ones, unreasonable sonic and density log data were detected. The synthetic data were then used to supply data where a modeled value is necessary due to the omission of unreasonable actual data, and where no directly measured data are available.
Seismic Inversion
Seismic inversion processing was carried out by a model-based constrained method using Hampson-Russel’s STRATA software. The model-based constrained method uses seismic traces, a representative wavelet and an initial presumed EI model (initial model) for input data. The initial model is modified by an iterated inversion process so as to acquire the best match between seismic trace and the synthetic seismogram based on the modified model. The final inversion output is the modified model with the best match to the seismic data. The deviation of the modified model from the initial model is constrained by the processing parameters in the method. We used the constraint that the impedance change from the initial model is less than 40%.
Modeled EI logs were used for the wavelet extraction and the initial model construction. They were calculated from the modeled Vp, Vs and density logs by the equation proposed by Connoly (1999). Three EI volumes, which are EI (6-degrees), EI (16-degrees) and EI (22-degrees), were constructed from three angle-offset-sub-stacked data sets (near-offset, middle-offset and far-offset).
For each angle sub-stacked data set we extracted a representative wavelet using seismic traces and modeled logs of the three wells. The Roy White method mounted in the software was chosen for wavelet extraction; the method uses the seismic traces and well logs, and maximizes the correlation between the synthetic seismic traces from well data and their
corresponding actual seismic traces in frequency domain (White, 1997).
An initial model is necessary for the model based constrained method. The initial model was created for each angle offset from modeled EI logs. A 20Hz high cut filter was applied for the initial model so as to avoid a false high frequency trend that would remain in the inversion results; such false high frequency components could be caused not from seismic records themselves but by spatial interpolation of modeled logs. The spatial interpolation of the initial model was conducted by a kriging method.
Lithofacies Prediction
Construction of the petrophysical property volumes from inverted EI volumes was carried out by multi-attribute analysis using Hampson-Russel’s EMERGE software. The relationships between the seismic and petrophysical properties were identified from the rock physics modeling. We obtained the equations to convert the seismic properties to petrophysical ones from the modeled logs and the petrophysical analysis. Using the equations, two rock property volumes were calculated from the inverted EI volumes; one is a total porosity (PHIT) volume, and the other is a lithology indicator volume.
From the two petrophysical-property volumes we discriminated three lithofacies: shale, tight sand and reservoir sand.
RESULTS AND DISCUSSION
Comparison of Velocities from Sonic log, Core Samples and Modeled Log data
The sonic log velocity is from the measurement of a dipole-share sonic imager tool (DSI) in the interval from the lower part the Wangarlu Formation to the Top Plover Formation in the Abadi-1, Abadi-2 and Abadi-3 wells. Modeled Vp, Vs and density logs were constructed for the interval, and compared with the measured sonic log. The modeled Vp and Vs logs at Abadi-2 are shown in Figure 5, and a cross plot modeled Vp vs. PHIT is shown in Figure 6.
Figures 5 and 6 also show a comparison of the modeled log with measured sonic log without any editing. The modeled velocity matches with the core
velocity more sharply than the measured one.
The difference between the modeled log and measured one was the largest in the reservoir sand of Abadi-2 among the three wells.
The depth of investigation of the sonic log is very shallow (less than a few inches) so the measurement can be greatly affected by the borehole condition, degree of mud-filtrate invasion, drilling induced fractures, etc. and thus is prone to error. It is also possible that the core plug velocity has some errors because it is not measured completely under in-situ conditions. In addition there is the possibility of micro-fractures resulting during core acquisition, sampling and trimming. The velocities of the core samples of the three wells show a clear negative linear relationship with a high correlation coefficient on the cross plot of PHIT and Vp as shown in Figure 6. The core velocities are also consistent with volumetric fractions of clay (VCL). The sonic log velocities of the three wells are on the same trend as in the core data on the cross plot of PHIT and Vp but in the Abadi-2, the velocities of the reservoir sands drift to lower velocities. Consequently, we considered that the confidence of the core velocities was higher than the sonic log velocities, and that there were some errors on the sonic log for the reservoir intervals in Abadi-2. Errors in core-to-log depth correlation and scales of rock mass measured in the two types of data are ignored based on the parallelism of changes versus depth observed in Abadi-2 (Figure 5).
The borehole condition of the reservoir sands of Abadi-2 was generally good, however the time-depth curve from the integration of the sonic log does not match the check shot velocity. The sonic log shows slower velocities than the check shot and modeled velocities. An azimuthal anisotropy in dipole-share sonic log measurement (∆t fast - ∆t slow) indicates the presence of fractures. It is therefore interpreted that the “anomalously low” sonic log velocity in the Abadi-2 was possibly caused by drilling induced fractures.
Relationships between Lithofacies and Modeled Log
Figure 7 shows the comparison of modeled velocities with sonic log velocities on the cross plot of Vp and PHIT, color coded by clay volume (VCL). Figure 8 shows the cross plot of Vp-Vs color coded by PHIT.
Both the sonic log and modeled velocities are
virtually on the same trend on the two cross plots but the modeled velocity shows a better relationship with PHIT and VCL than the sonic log velocity. Plotted sonic log velocities are scattered and the separation between reservoir sand, tight sand and shale is not clearly observed on the cross plots. The sharp relationship between the modeled seismic properties (Vp, Vs and density) and petrophysical properties (PHIT, VCL) on the above cross plots gives a robust basis for converting seismic inverted volumes to petrophysical volumes. Using the Vp-Vs cross plot of modeled logs we can discriminate three lithofacies, shale, tight sand and reservoir sand.
Sensitivity of EI to the change of fluids is smaller than to the change of PHIT or VCL. We recognized, therefore, that it is difficult to separate gas saturated sand from water saturated sand. Considering the error range of inverted EI, we judged that we could not discriminate gas sand from water sand from the seismic data.
Deviation of Seismic Inverted Volumes from Modeled Log
Comparisons of inverted EI volumes with modeled EI logs in the three wells are shown in Figure 9. There remain some disagreements between inverted and modeled EI. The deviation of inverted EI from modeled EI is significant in near-offset, EI (6-degree) and this is considered to be caused by significant coherent noise problems on the seismic traces giving a poor synthetic to seismic correlation and not as a direct result of the inversion process. As a result, some coherent patterns observed on the inverted data were considered to be unreasonable and artificial, based on the geological interpretation of the area.
Artificial coherent patterns were dominant on the cross sections of the inverted EI (6-degree) and therefore the inverted EI (6-degree) was considered unsuitable for reservoir characterization. The EI (6-degree) was therefore eliminated from the prediction of the petrophysical property volumes.
To improve the seismic inversion result in the future we are planning to perform re-processing of the 3D seismic data with advanced processing methods.
Lithofacies Prediction from Seismic Inverted Volumes
The result of the multi-attribute analysis accurately predicted VCL and PHIT logs from modeled EI log in
the intervals of Plover Formation for the three wells.
The errors of the predicted PHIT from actual PHIT were less than 1 percent of porosity, as shown in Figure 10.
The VCL and PHIT volumes from inverted EI volumes, has larger errors than the volumes from modeled EI logs, as expected due to coherent noise on the seismic traces, and by the limit of vertical resolution of the seismic data. The comparison of predicted PHIT from inverted EI with actual PHIT is shown in Figure 11. The noise causes a poor correlation between the synthetic seismogram and seismic trace, and it causes some errors in wavelet extraction and inversion processing. We expect that wavelet extraction and seismic inversion can be conducted with more accuracy, and that prediction error will be reduced if the noise were reduced by re-processing. To detect the distribution and the source of the noise will be crucial in establishing an effective method and workflow to remove the noise.
We could not predict the value of VCL satisfactorily therefore we produced a lithology indicator volume, which qualitatively discriminates sand from shale, instead of a quantitative VCL volume.
Using the volumes of lithology indicator and porosity (PHIT), we successfully discriminated the three lithofacies; reservoir sand, tight sand and shale, using two steps. First, sand and shale were discriminated by the lithology indicator volume, then secondly sand was discriminated into tight sand and reservoir sand by the PHIT volume. The examples of the PHIT volume are shown in Figures 12 and 13.
Lithological layers show subtle facies changes in inverted seismic attributes between the two seismic markers MH4000 and MH6000. Figure 13 shows a horizon slice of the PHIT volume along the seismic marker MH5000 + 20 msec. The structural control of the distribution of the lithofacies is clearly observed in comparison with the structural map of Top Plover (Figure 2). This 3D distribution of the reservoir sand is used as an input for the reservoir model.
CONCLUSIONS AND FUTURE PLANS Conclusions
1. It is considered that there were some errors on the sonic log for the reservoir intervals in Abadi-2,
where the sonic log shows anomalously low velocity, possibly caused by drilling induced fractures. The confidence of the core velocities is considered greater than the sonic log velocities, and the rock physics modeling was performed to be consistent with core measurements.
2. The rock physics modeling was effective in identifying quantitative relationships between the seismic and rock properties, and effective for 3D rock property volume prediction. Based on the relationships between seismic and petrophysical properties, EI volumes were converted into a total porosity volume and a lithology indicator volume to discriminate reservoir sand from the other lithofacies.
3. There remain some errors in inverted EI at the existing well locations. The inverted EI (6-degrees) volume was removed from the lithofacies prediction because of significant errors at the well locations and artificial coherent patterns are dominant on cross sections. The errors are interpreted to be caused by coherent noise on seismic traces.
Future Plans
Reserve estimates and their uncertainty for the Abadi gas field have been repeatedly updated through the successive programs of the reservoir characterization study.
A new appraisal well program has been programmed in 2006, which is expected to provide additional data to update the model for the reservoir characterization with more confidence.
A pilot test for reprocessing the 3D seismic data is now in progress which aims to detect the cause and spatial distribution of the noise and to establish an effective method and workflow to eliminate this noise.
ACKNOWLEDGEMENTS
The authors would like to express their deep thanks to BPMIGAS, Inpex Masela, Ltd., and JOGMEC (ex-JNOC) for their help in course of this study and their permission for publication of this paper.
Courtesy for Hampson-Russel Geophysical Services should be expressed with deep appreciation to their service and help in course of this study.
REFERENCES CITED
Nagura, H., Suzuki, I., Teramoto, T., Hayashi, Y., Yoshida, T., MP Bandjarnahor, H., Kihara, K., Swiecicki, T., Bird, R., 2003. The Abadi gas field.
Proceeding of the Indonesian Petroleum Association, 29th Annual Convention, v. 1, p. 451-466.
Xu, S., and White, R.E., 1995. A new velocity model for clay-sand mixtures. Geophysical Prospecting, 43, p. 91-118.
Kuster, G.T., and Toksoz, M.N., 1974. Velocity and attenuation of seismic waves in two-phase media, Geophysics., 39, p. 587-618.
Mavco, G., Mukerji, T., and Dvorkin, J., 2002. The Rock Physics Handbook Tools for Seismic Analysis in Porous Media, Cambridge University Press, 329 pp.
Connoly, P., 1999. Elastic Impedance. The Leading Edge, 4, p. 438-452
Roy White. 1997. The accuracy of well ties: practical procedures and examples: Presented at the 1997 SEG Annual International Meeting, Expanded Abstracts, v. 2, RC1.5, 2126.
511 Figure 1 - Location map of the Abadi gas field. Contour lines indicate water depths in meters. The arrow indicates Abadi gas field in Masela PSC
block. The other gas fields in the area are also shown; such as Evans Shoal, Sunrise-Troubadour, Bayu-Undan.
Figure 2 - Depth structure map of Top Plover seismic marker (MH5000) (contour interval: 25 m). The polygon enclosed with blue lines is the outline of the 3D seismic survey area (2,060km2). Black bold lines outside the polygon indicates faults based on 2D seismics. Color depth codes show depths from the mean sea surface. Bold red line indicates the level of the estimated gas water contact (however. not necessarily, directly indicating the presence of gas anywhere above the level). Abadi-1, Abadi-2 and Abadi-3 are marked with gas-discovery-well symbols at their locations.
Figure 3 - Cross section of reprocessed, finite difference post-stack migrated 3D seismic data intercepting Abadi-1, Abadi-2 and Abadi-3. Seismic polarity is SEG normal with red as peak and black as trough. The vertical axis is two way time (sec.) from mean sea surface. The seismic markers are MH4000 representing Base Jamieson Formation; MH5000 representing Top Plover Formation; and MH6000 representing an intra-formation key of the Plover. GR curves of the three wells are shown as reference.
3600m
4600m 3900m
Abadi-1
Abadi-3
Abadi-2
:3D Seismic Survey Area ( 2,060 km2 )
:3D Seismic Survey Area ( 2,060 km2 )
0 10km 0 10km
Figure 4 - Generalized stratigraphy of the Abadi field area. The seismic markers are MH4000 representing Base Jamieson Formation; MH5000 representing Top Plover Formation;
and MH6000 representing an intra-formation key of the Plover. The pay zone of the field is between MH5000 and MH6000. The inserted log shows acoustic impedance produced from sonic and density logs of Abadi-1, edited by rock physics modeling.
(acoustic impedance: density x P-wave velocity; unit = g/cc x m/sec.)
Figure 5 - Comparison of the velocities in sonic-log, core and in the model, in the reservoir zone of Abadi-2. The horizontal axis is velocity (m/s) and the vertical axis is vertical depth from sea surface (m). The blue line is P-wave velocity (Vp) and the red line is S-wave velocity (Vs) from the sonic-log. The green line is Vp and the pink line is Vs from the modeled log. The circles filled with sky-blue are Vp of water saturated core samples; the circles with orange are Vp of dry core samples. The squares with sky-blue are Vs of water saturated core samples; the square with orange are Vs of dry core samples.
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Paleocene Eocene Oligocene
Miocene
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Plover Fm
Echuca Shoals Fm Jamieson Fm
ALBIAN APTIAN BARREMIAN HAUTERVIAN VALANGINIAN BERRIASIAN TITHONIAN KIMMERIDGIAN
OXFORDIAN CALLOVIAN BATHONIAN BAJOCIAN AALENIAN TOARCIAN PLIENSBACHIAN
SINEMURIAN HETTANGIAN MAASTRICHTIAN
CAMPANIAN SANTONIAN CONIACIAN TURONIAN CENOMANIAN DANIAN THANETIAN YPRESIAN LUTETIAN BARTONIAN PRIABONIAN RUPELIAN CHATTIAN AQUITANIAN BURDIGARIANLANGHIAN SERRAVALLIAN RORTONIANMESSINIAN
EARLYMIDDLELATEEARLYLATEELEMLELEML Pleistocene PIACENZIAN
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LITHOLOGY &
FORMATION NAME
MESOZOICCENOZOIC PALEOGENENEOGENEQ
Wangarlu Fm Oliver Fm
Prion Fm Hibernia Fm Johnson Fm Barracouta Fm STANDARD
CHRONOSTATIGRAPHY AGE
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LEGEND
Sandstone dominant Limestone dominant Marl dominant Shale dominant LEGEND
Sandstone dominant Limestone dominant Marl dominant Shale dominant
Abadi-1
MH4000 MH5000
MH6000 Echuca Shoals
Upper Plover Jamieson
Lower Plover Wangarlu
TVDss(m)
Figure 6 - Comparison of the velocities in sonic-log, core and in model on the cross plot of total porosity (PHIT(%); horizontal axis) vs. P-wave velocity (Vp (m/s); vertical axis), in the reservoir sand of Abadi-1, Abadi-2 and Abadi-3. The reservoir sand is defined by petrophysical analysis of the log data following the diagnostic criteria as follows;
volume of clay (VCL) should be less than 50 %, PHIT be more than 6 %, water saturation (Sw) be less than 60 %. (a) The cross plot of sonic-log. (b) The cross plot of modeled log. The small dots are the cross plot of PHIT from log analysis vs.
Vp from the sonic/modeled log. The large circles, triangles and squares filled with sky-blue are the cross plot of PHIT vs. Vp from water saturated core samples; those with orange are from dry core samples.
Figure 7 - Comparison of the velocities in sonic-log and modeled ones on the cross plot of total porosity (PHIT (%); horizontal axis) and P-wave velocity (Vp m/s); vertical axis) with color coding by volume of clay (VCL), in the reservoir intervals of the Abadi-1, Abadi-2 and Abadi-3. (a) The cross plot of sonic-log. (b) The cross plot of modeled log. The plotted dots are the cross plot of PHIT from log analysis vs. Vp from the sonic/modeled log.
VCL
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PHIT (fraction) PHIT (fraction)
Vp (m/s)
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(a)Sonic-log (b)Modeled log
Figure 8 - Comparison of the velocities in sonic-log and modeled ones on the cross plot of P-wave velocity (Vp (m/s); horizontal axis) and S-wave velocity (Vs (m/s); vertical axis) with color codes by total porosity (PHIT), in the reservoir intervals of the Abadi-1, Abadi-2 and Abadi-3. (a) The cross plot of sonic-log. (b) The cross plot of modeled log. The plotted dots are the cross plot of Vp vs. Vs from the sonic/modeled log.
Figure 9 - Comparison of the inverted EI with modeled ones, in the reservoir zones and their overlying seal zones of the Abadi-1, Abadi-2 and Abadi-3. The horizontal axis is EI (g/cc x m/s) and the vertical axis is two way time (TWT) from sea surface (msec.).
The blue lines are modeled EI; the red lines are inverted EI of the three incident angles at the three well locations.
15% 0%
PHIT
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PHIT
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MH4000
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Modeled Well EI log Inverted EI
Modeled Well EI log Inverted EI
Abadi-3 Abadi-1 Abadi-2
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Figure 10 - Comparison of the predicted total porosity (PHIT in fractions; horizontal axis) with actual ones, in the reservoir intervals of the Abadi-1, Abadi-2 and Abadi-3. The vertical axis is vertical depth from mean sea surface (m). The red lines are predicted PHIT from modeled EI logs using multi-attribute analysis; the black lines are actual PHIT from the petrophysical analysis.
Figure 11 - Comparison of the predicted total porosity (PHIT in fractions) with actual ones, in the reservoir intervals of the Abadi-1, Abadi-2 and Abadi-3. The vertical axis is vertical depth from mean sea surface (m). The red lines are predicted PHIT from inverted EI; the black lines are actual PHIT from the petrophysical analysis.
Abadi-1 Abadi-2 Abadi-3
Cross-Correlation = 0.969 RMS ERROR = 0.00853 MH5000
MH6000
Actual PHIT Predicted PHIT Actual PHIT Predicted PHIT Actual PHIT Predicted PHIT
TVDss (m)
MH5000
MH6000
0 PHIT 0.2 (frac.) 0 PHIT 0.2
(frac.) 0 PHIT 0.2
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Abadi-3 Abadi-2
Actual PHIT log (ELAN)
Predicted PHIT from inverted EI Actual PHIT log (ELAN)
Predicted PHIT from inverted EI
TVDss (m) TVDss (m) TVDss (m)
Abadi-1
Figure 12 - NNW-SSE dip-cross-section of the inverted volume of PHIT, passing Abadi-1. The vertical axis is two way time (msec.) from mean sea surface. PHIT log by petrophysical analysis of the well is shown as reference.
Figure 13 - Horizon slice of the PHIT volume along the seismic marker MH5000+20 msec. The surface locations of Abadi-1, Abadi-2 and Abadi-3 are shown as reference.
PHIT Volume, Horizon Slice of MH5000+20msec Predicted PHIT = (-1.88656e-4×EI(16) + 0.978748) 2
10km 10km
PHIT(V/V)
Abadi-1
Abadi-3
Abadi-2