Arab J Geosci DOI 10.1007/s12517-014-1734-y
ORIGINAL PAPER
3-D predrill overpressure prediction using prestack depth-migrated seismic velocity in a field of southwestern Malay Basin Iftikhar Ahmed Satti & Deva Ghosh & Wan Ismail Wan Yusoff
Received: 29 September 2014 / Accepted: 19 November 2014 # Saudi Society for Geosciences 2014
Abstract Quantitative predrill pore pressure prediction is very important for reducing the drilling hazards. In many Tertiary basins, generation of overpressure is mainly by compaction disequilibrium due to high deposition rate and low permeability in shale. In the Malay Basin, high geothermal gradient (i.e., 40–60 °C/km) and high heat flow also play an important role in generating overpressure at shallow depth. This study describes the utilization of 3-D prestack depthmigrated seismic interval velocity for predrill pore pressure prediction in a field of southwestern Malay Basin. The quality of 3-D prestack depth-migrated seismic interval velocity was enhanced by calibration with check-shot data. Modified Gardner’s equation was used to generate the 3-D density cube from the interval velocity. The Eaton and Bowers methods were used to compute and predict pore pressure values from the seismic velocity. The Eaton method with standard exponent for seismic velocity gave good prediction in the shallower zone where overpressure is caused by undercompaction mechanism, whereas underpredicted the high pore pressure at the greater depth where fluid expansion is the cause of overpressure. However, the overpressures were predicted quite well by applying correction on the Eaton method for fluid expansion mechanism.
Keywords Overpressure . Interval velocity . Anisotropy . Depth migration . Prediction
I. A. Satti (*) : D. Ghosh : W. I. Wan Yusoff Department of Geosciences, Universiti Teknologi PETRONAS, Perak, Malaysia e-mail:
[email protected]
Introduction Pore fluid pressure higher than the normal/hydrostatic pressure is called overpressure and has significant importance in geohazards analysis and prediction (Bowers 2002). In the high overpressured regions, accurate predrill pore pressure prediction is very important for casing point selection and well planning. Pore pressure prediction has implication in migration modeling for prospect evaluation and seal prediction. Undercompaction and fluid expansion are two most common mechanisms of overpressure generation. Different pore pressure prediction schemes are required for both mechanisms, as they have different effect on rock properties (Tingay et al., 2009). The undercompaction mechanism occurs when low permeability prevents the rapid escape of pore fluids due to fast deposition. Fluid expansion (unloading) mechanism such as aqua thermal expansion, kerogen to gas maturation, and clay digenesis can increase pore fluid volume within the confined pore space (Bowers 2002). In case of fluid expansion mechanism, pore pressure increases more rapidly than the lithostatic pressure, so fluid expansion can decrease the effective stress (Bowers 2002). Undercompaction is the main mechanism of overpressure generation in most of the Tertiary sedimentary basins where sedimentation rate is high. Other mechanisms such as chemical compaction and fluid expansion can generate overpressure in the deeper part of the sedimentary basins (Deming et al. 2002). For pore pressure prediction, it is important to understand the different mechanisms of overpressure generation and their effect on rock properties (Swarbrick 2001). Seismic velocity is most reliable tool for predrill pore pressure prediction in the frontier area. Pennebaker (Pennebaker 1968) first demonstrated the concept of using seismic interval velocity for pore pressure prediction. Seismic pressure prediction relies on detecting changes in interval velocity with depth. These changes
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reflect the properties of the overpressured rocks, which, in most cases, are undercompacted (Dutta 2002). Mechanical compaction due to sedimentation increases the effective stress and reduces the porosity with depth. Seismic Pwave velocity has direct relation with effective stress and increase with depth due to increase in effective stress. However, if the rate of sedimentation is very high and fluid cannot escape from the pore space due to low permeability in rock, pore fluid will support a part of the overburden and become overpressured. As overpressure reduces the amount of compaction that could occur, seismic velocity can be used for pore pressure prediction (Sayers et al. 2006). According to Chopra and Huffman 2006, accuracy of the seismic velocity is one of the key factors required for reliable pore pressure prediction. The quality of the seismic velocity could be improved using advanced seismic processing techniques like high-density high-resolution velocity analysis, prestack seismic simultaneous inversion, and anisotropic prestack depth migration etc. In this study, we used anisotropic 3-D prestack depth-migrated (PSDM) seismic velocity for predrill overpressure prediction.
Geological history of the Malay Basin The Malay Basin is situated to the east of Peninsular Malaysia, in the South China Sea (Fig. 1). Malay Basin is a NW-SE Fig. 1 Location map of the Malay Basin and study area, modified from (Tjia and Liew 1996)
trending rift basin geometrically asymmetric in form with the depositional axis occurring closer to the southwestern flank of the basin (Ebdale and Redfern 1997). The sedimentary history of the Malay Basin began in the Late Eocene-Early Oligocene (45 Ma). Malay Basin has two depositional episodes: (i) syn-rift with continuous subsidence including thermal and (ii) post-rift with basin inversion in Middle-Late Miocene (Ghosh et al. 2010). Sedimentary sequence in the Malay Basin is subdivided into different stratigraphic groups starting from the A (Recent) to the M (Oligocene) (Ghosh et al. 2010). The study area is located in the southwestern part of the Malay Basin, and it comprises an elongated NW-SE trending anticline. This anticlinal structure is dissected by a series of broad north-south trending normal faults. Normal faults have en echelon arrangement, which suggest the presence of shear component in the western flank of the study area (Ebdale and Redfern 1997). Based on structural interpretation, the study area can be divided into four clearly defined fault blocks (Fig. 2). The main fault block A is not significantly affected by normal faulting and is tested by seven wells, while fault blocks B and C (shear zone ) are structurally more complex due to the presence of many extensional faults and are tested by two well. The fault block D is on the western side of the study area and is tested by one well.
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Fig. 2 Example of an interpreted horizon showing the presence of extensive normal faulting in the study area. The locations of fault blocks and wells used for pressure prediction validation are also shown
Overpressure in the Malay Basin The Malay Basin is one of the most prolific hydrocarbonproducing basins in Southeast Asia. Overpressure developed in the deeper parts of the basin due to the deposition of 12-km fine-grained Tertiary sediments during the last 35 Ma (Hoesni et al. 2003). The depth to the start of overpressure varies across the Malay Basin. The top of overpressure is shallower in the basin center (i.e., 1.9–2.0 km) and gradually deepens toward the basin flanks (i.e., 3.0 km) as shown in Fig. 3 (Hoesni 2004). The top of overpressure does not conform to any particular stratigraphic group. There exists no relationship between the top of overpressure and the temperature distribution in the Malay Basin. A previous study (Koch et al. 1994) has shown that overpressure is not generated by undercompaction alone.
Fluid expansion or unloading mechanisms may have also contributed to the high overpressure in the Malay Basin. Temperature may also play an important role in the development of overpressure. Geothermal gradient is high (i.e., 50 °C/km) in the central Malay Basin resulting in thin and shallow dewatering zone. This may probably be the reason for the occurrence of stratigraphically younger units at shallow depth. While toward the basin flanks, geothermal gradient is less and overpressure occurs deeper in older stratigraphic units (Shariff and Leslie 1995). Malay Basin is characterized by high heat flow with geothermal gradient of between 40 and 60 °C/km that has influence on hydrocarbon generation and migration (Yusoff 2011). There is a strong correlation between the rate of sedimentation and the overpressure development by disequilibrium compaction. However, when wells are drilled deeper into the basin,
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Fig. 3 The onset of overpressure in Malay Basin (Hoesni 2004). The depth to the start of overpressure is shallowest in the basin center and increases gradually toward the basin flanks. The top of overpressure crosscuts the stratigraphy of the basin
thermal processes in shale will result in secondary overpressure generation (O’Connor et al. 2012). It suggests that additional overpressure mechanisms such as clay mineral diagenesis and active hydrocarbon generation exist in the Malay Basin.
The initial velocity model was constructed by using the second pass anisotropic migration velocity picked at dense grid of 100 m×100 m. The constructed root-mean-square (RMS) velocity was converted to interval velocity (vint) and further converted to depth domain.
Overpressure in the study area Study area is situated in the southwestern Malay Basin where overpressure behavior is variable, with different fault blocks showing different overpressure trend. Overpressure may start deeper and ramp up rapidly or start at shallow and ramp up gradually with depth. Overpressure is observed in almost all of the wells drilled in the study area. Pore pressure data obtained from well test tools such as repeat formation tests (RFTs), drill stem tests (DSTs), and modular formation dynamics tests (MDTs) was used to analyze the overpressure distribution. In the study area, heat flow is up to 75 mW/m2 causing source rock to mature at earlier and hydrocarbon generation start at shallow depth. This can also contribute to overpressure generation (Satti et al. 2013). According to the recent study in the area (Satti et al. 2014), overpressure in the shallower zone is caused by undercompaction, whereas in the deeper zone, fluid expansion is the mechanism of overpressure generation.
3-D seismic velocity processing 3-D PSDM seismic interval velocity data used for pore pressure prediction. The processing workflow of the seismic velocity is shown in Fig. 4.
Fig. 4 Processing workflow
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Fig. 5 Final migration model before well calibration
Fig. 6 Final migration model after well calibration
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The initial interval velocity model was updated in six iterations. The updated model obtained from iteration 1 was used in beam prestack depth migration to produce a set of 25 m×25 m grid of common image point (CIP) gathers. Iteration 5 involved mechanically slowing down the velocities around the affected faults and bull’s-eye. Velocity scanning using various percentages of velocities was carried out before arriving at an optimal percentage to use in slowing down the velocity. This test was repeated for every area that required adjustment. High-resolution tomography update was then performed. In iteration 6, anisotropy update was carried out by using the delta model to depth adjust the iteration 5 velocity model to obtain a vertical velocity model. After validating the tomography update, this velocity model was used for the final anisotropic Kirchhoff depth migration. The final velocity model obtained after applying anisotropic Kirchhoff depth migration was calibrated with the well tops and two interpreted depth surfaces. The model was iteratively updated by reducing the depth difference between the interpreted horizons and well tops. The brief description of the workflow used for well calibration is discussed below. The supplied depth surfaces were being scaled to vertical time through the final imaging interval velocity volume. The interval velocity volume, used to produce the final depth image, was converted to its corresponding average velocity time. Analysis of the well ties with the supplied interpretation was used to calculate a multiplication factor or scalar at each well location, which were spatially mapped. The scalar maps were used to create the scalar volume which was utilized to calibrate the average velocity volume. The calibrated average velocity volume was then converted back to its corresponding interval velocity. The time interpretation from average velocity was scaled back to depth through the calibrated interval velocity volume. Analysis of the well ties with the calibrated depth surfaces was carried out to determine if the differences were sufficiently reduced. Example of velocity before and after calibration with wells is shown in Figs. 5 and 6, respectively.
3-D seismic velocity modeling The quality of interval velocities derived from seismic processing has direct impact on the accuracy of pore pressure prediction. Interval velocity extracted from 3-D cube for different well locations was compared with the check-shot/sonic velocity to check the accuracy of the seismic velocity. In most of the wells, seismic velocity was slightly lower than the check-shot/sonic velocity (Fig. 7a). 3-D grid-based velocity modeling technique of Petrel software was used to refine the PSDM interval velocity cube by calibrating with the check-shot interval velocity from the
Fig. 7 a Comparison of sonic/check-shot velocity with seismic velocity before and after calibration with check-shot velocity at blind well location. Seismic velocity density is showing better match with the sonic velocity after check-shot calibration. b Final PSDM interval velocity cube obtained by calibration with check-shot data from the offset wells. This is the calibrated velocity used for density calculation and pore pressure prediction
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Fig. 7 (continued)
offset wells. The summary of the 3-D grid-based velocity modeling procedure is discussed here. In this grid-based velocity modeling, check-shot data from eight wells and 15 interpreted horizons were used. All the 15 interpreted horizons were used to create the 3-D grid to incorporate the structural information contained in the horizons. The PSDM interval velocity was sampled into the 3-D grid. Check-shot data from the offset wells is upscaled and sampled in the same 3-D grid. In the final step, the PSDM interval velocity was calibrated with upscaled check-shot by applying anisotropy factor derived from check-shot interval velocity and seismic interval velocity. It is observed that check-shot-calibrated velocity is giving better match with the sonic velocity at all the well locations, which were used for calibration. The calibrated velocity cube is validated at blind well location, and a good match is observed with the sonic/check-shot velocity (see Fig. 7a). The calibrated 3-D PSDM velocity is shown in Fig. 7b.
3-D density calculation and calibration 3-D check-shot-calibrated velocity cube is converted to density cube using Gardner’s equation (Fig. 8a). The Gardner et al. (1974) equation (Eq. 1) is given as ρ ¼ aV b
ð1Þ
where ρ is the density, V is the P-wave velocity, and a and b are the empirical parameters. The Gardner parameters for the study area are obtained from the cross plot of sonic and density log. Density calculated using Gardner’s equation was extracted at well location and compared with the density log. The density obtained from the seismic velocity was slightly lower than the well log density. To minimize the discrepancy between the observed log density and calculated density, 3-D density cube is calibrated with the well log density by applying the same technique used for
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Fig. 8
a 3-D density calculated from calibrated PSDM interval velocity. b Comparison of log density (RHOB) with seismic velocity-generated density before and after calibration with log density at blind well location. Seismic generated density is showing good match with the log density after calibration. Scale bar for density cube is in gram per cubic centimeter
velocity calibration. The calibrated density cube is validated by exporting the density data at blind well locations. A good match is found between the calibrated density and the log density (Fig. 8b). Overburden pressure is calculated using the calibrated density cube. In the marine environments, overburden pressure σv at depth z is given as by Eq. 2. z
σv ðzÞ ¼ ρw gzw þ zw ρðzÞgdz
ð2Þ
Pore pressure prediction The Eaton (1972) and Bowers (1995) methods were used for pore pressure prediction using 3-D PSDM interval velocity in the study area. The Bowers method is based on the effective stress approach and can be used to predict overpressure generated by either undercompaction or fluid expansion mechanism (Bowers 1995). The Eaton method is based on the detection of changes in porosity with depth and is derived from Terzaghi (1948) equation (Eq. 3) based on soil mechanics (Eaton 1972). S v ¼ σv þ P f
ð3Þ
where ρw is the seawater density, zw is the water depth, ρ(z) is the density at depth z below the surface, and g is the acceleration due to gravity.
Here, Sv is the total vertical stress, Pf is the pore fluid pressure, and σv is the vertical effective stress.
Fig. 9 a 3-D pore pressure prediction from calibrated PSDM interval velocity using Eaton exponent 3. b 3-D pore pressure prediction from calibrated PSDM interval velocity using Eaton exponent 6. Pore pressure predicted using Eaton exponent 3 matches with repeated formation test (RFT) pressure data at shallow zone, whereas pore pressure prediction using exponent 6 matches with the RFT pressure data in the deeper zone.
Hence, Eaton exponent 3 and exponent 6 can be used for pore pressure prediction at shallower and deeper zone, respectively. Red lines show the locations of the wells for which RFT pressure data is available. The wells taken as blind well for pore pressure validation are highlighted blue (well A) and black (well B). The scale bar for both cubes (a, b) is in pounds per gallon
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Fig. 9 (continued)
Pore pressure prediction results obtained from both methods were compared with the RFT pressure data. The Eaton method is giving better pore pressure prediction, whereas the Bowers method did not give satisfactory results and underpredicted the pore pressure. Therefore, pore pressure prediction using the Eaton method is discussed in this paper. The Eaton method estimates pore pressure from the ratio of acoustic travel time in normally compacted sediment to the observed acoustic travel time (Hoesni 2004). The Eaton method is given as in Eq. 4. Pp ¼ S v −ðS v −Ph ÞðV obs =V norm ÞðX Þ
ð4Þ
Here, Pp is the pore pressure, Ph is hydrostatic pore pressure, Sv is the total vertical stress, Vnorm is the normal velocity, Vobs is the observed velocity, and X is the Eaton Exponent (resistivity=1.2, velocity=3). The Eaton method is an empirical method to estimate pore pressures from seismic velocity, sonic, and resistivity logs. The Eaton method uses a regionally defined exponent (Eaton exponent) that can be varied to calibrate the trend and predict
the pore pressure generated by different mechanisms. In the areas where mechanism of overpressure generation is undercompaction, an Eaton exponent of 3.0 is typically used for pore pressure prediction. Pore pressure cubes using Eaton exponent 3 and exponent 6 are shown in Fig. 9a, b. This predicted pressure using the Eaton exponents is extracted at two blind well locations (well A, well B, and well C) as shown in Fig. 10a, b. The observed discrepancy between the RFT pressure and the predicted pore pressure from the Eaton method has been used as an indication of additional pressuring mechanisms besides disequilibrium compaction. Pore pressure was successfully predicted using an Eaton exponent of 3.0 in the shallower zone where the overpressure is believed to be generated by disequilibrium compaction. In the deeper part of the wells where overpressure is generated by fluid expansion, Eaton exponent 3 underpredicted the pore pressure. However, a reasonable pore pressure prediction was obtained by applying correction on the Eaton method (i.e., using the higher Eaton exponent of 6), determined from calibration with RFT pressure data.
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Fig. 10 Example of predicted pore pressure using Eaton exponent 3 (red line) and exponent 6 (green line) at the blind wells, well A (a) and well B (b). The predicted pressure is compared with the RFT pressure data (black dots). In both the wells, onset of overpressure is well predicted using
Eaton exponent 3. However, in the deeper zone, overpressure is underpredicted. After applying the correction (i.e., using higher Eaton exponent), the Eaton method gives good prediction in the deeper zone
Conclusions
can be used for predrill overpressure prediction using the Eaton method in the study area. A refined interval velocity model can be obtained by calibration of PSDM interval velocity with check-shot velocity data and thus can be used for
Based on the results obtained from pore pressure prediction, it is concluded that well-conditioned PSDM interval velocity
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overpressure prediction in the area of interest. Calibration of density obtained from seismic interval velocity with the well log density can also contribute to enhance the accuracy of pore pressure prediction. The mismatch observed between the formation pressure and predicted pressure is attributed to the presence of different mechanisms of overpressure. Anisotropic Kirchhoff PSDM interval velocity has increased the accuracy and reliability of the pore pressure prediction in the study area. Acknowledgments We wish to thank the Universiti Teknologi PETRONAS (UTP), PETRONAS Carigali, and Petrofac for providing the data and permitting to publish these findings. We are thankful to Faizal Rahim and M. J. Hoesni from PETRONAS Carigali and Muhammad Sajid from UTP for their valuable suggestions and discussions. Thanks are due to Geovani Christopher from the Halliburton Services, Kuala Lumpur, for providing software support. Special thanks are given to the Center of Seismic Imaging (CSI), Universiti Teknologi PETRONAS, for providing facilities to carry out this research work.
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