PROCEEDINGS, INDONESIAN PETROLEUM ASSOCIATION Thirty-Eighth Annual Convention & Exhibition, May 2014

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1 IPA14-G-227 PROCEEDINGS, INDONESIAN PETROLEUM ASSOCIATION Thirty-Eighth Annual Convention & Exhibition, May 2014 A COMPARISON OF DEPTH CONVERSION METHODS IN BUNTAL GAS FIELD, BLOCK B, NATUNA SEA, INDONESIA Irfan Yuliandri Syukri* John Hughes* Medi Medianesterian* ABSTRACT We present a case study analysis of gross rock volume (GRV) sensitivity to depth conversion for undeveloped zones at Buntal Gas Field, Block B, Natuna Sea. Methods incorporated included: (1) a linear average velocity-time function; (2) a 2nd order polynomial time-depth function; (3) an instantaneous velocity-depth V 0k method; and (4) calibrated seismic stacking velocities. We discuss and analyze best practices for each method, review a statistical depth error analysis, and highlight the benefits and limitations of each method. The linear average velocity function and polynomial function are simple and easy to apply. By using Dix corrected raw hand-picked seismic stacking velocities as input a gas slow down effect was detectable corresponding to the productive gas zones at Buntal. This provided confidence that seismic stacking velocities were viable for predicting velocity distribution away from well control and suggests the method is well suited for this project owing to the limited spatial control from wells. The V 0K method requires good quality calibrated velocity log, however, in this study area the data were not ideal with spikes due to washout/borehole rugosity and gas slow down effects. Despite these limitations, where clear seismic amplitude fluid responses are evident, the gas-water contact showed good consistency with the amplitude conformance to structure, a positive qualitative indicator that the method accurately predicts away from well control. Comparison of GRV computations using each method showed a range of numbers, on the main outlier being the stacking velocity method, emphasizing the importance of depth conversion in prospect characterization. * ConocoPhillips Indonesia Inc. Ltd. The use of a variety of depth conversion techniques further enabled a means of determining a range of GRV uncertainty for volumetric analysis. For these reasons it is a recommended best practice to run multiple depth conversion scenarios to evaluate uncertainty ranges for GRV and well depth prognosis. INTRODUCTION The biggest impact seismic interpreters have on the value of a producing reservoir or exploration prospect is the estimation of the container size or depth structure model. This is a function primarily of the seismic pick quality, depth conversion accuracy, and surface gridding. Here we focus on a case study analyzing the sensitivity of gross rock volume to depth conversion methodology. Various depth conversions with differing degrees of complexity are used in the oil and gas business. Often a deterministic depth structure model is used for reservoir characterization, or geostatistical methods may be used to estimate uncertainty of that method. In this study we employed four different depth conversion approaches commonly used by geophysicists, and analyzed the different outcomes and impact on gross rock volume uncertainty. The Buntal gas field study area is located approximately 136 kilometers north of the Matak Island base in the Natuna Sea and 30 kilometers to the east of the producing Belida oil and gas field (Figure 1). Seismic data quality is excellent with a strong amplitude response associated with gas in the main producing reservoirs in the Arang Formation at depths between -3,000 feet and -4,000 feet below sea level. The Buntal structure (Figure 2) is an elongated NE- SW trending asymmetric anticline bounded to the southeast by a reverse fault, approximately six kilometers long and two kilometers wide. The trap

2 consists of a four-way dip closure related to Middle Miocene structural inversion of an Eocene half graben. The fluvial-deltaic Arang formation is unconformably overlain by transgressive mudstones and claystones of the post inversion Muda Formation. The Buntal Field was discovered in 1990 by the Buntal-1 well. An appraisal well, Buntal-2, was drilled in 1998 approximately 3 km to the east of the discovery well. Three primary reservoirs from the fluvial-deltaic Middle Miocene Arang Formation contain the majority of the gas reserves: the Beta-1, Beta-2 and Zone-1C reservoir units (Figure 3). These were put on production in 2003 through the Buntal-3 and Buntal-4 development wells. Pressure data confirms connectivity of Beta sands and defines a GWC for the Beta-1, Beta-2 and Zone-1C sands. Although good stratigraphic correlations between Buntal-1 and Buntal-2 are observed, the Beta-1 and Zone-3 sands are shaled out in Buntal-2 and in general, reservoir quality is poorer at Buntal-2 relative to Buntal-1 (Figure 4). SEISMIC INTERPRETATION A 130 km 2 subset area was taken from a more extensive 3D survey acquired in 1997 (bin size 12.5m x 12.5m). The interpretation utilized full stack time migrated data. Seismic to well ties were established using synthetic seismograms. The synthetics seismograms were created using edited sonic and density curves, check shot data and extracted wavelets for three wells, Buntal-1, Buntal- 2, and Buntal-3. The reflection coefficient series are convolved with seismic wavelet to produce a synthetic seismic trace. In this case, the seismic wavelet is obtained using a trapezoidal wavelet to cover frequency bandwidth of seismic data from Upper Beta Marker Lower Arang interval. The synthetic seismogram is then compared with actual seismic trace at the well location to get the best well to seismic tie. The calibrated velocity log computed in the well tie process ensured the well velocity data derived time-depth values tied the seismic data. Then, after getting a good well to seismic tie, the seismic horizons were picked based on where well formation tops sits on the seismic data (Figure 5). The seismic data polarity is SEG normal (an increase in impedance represented by a peak). The horizon interpretation extended from the Buntal structure to the adjacent Alu-Alu well providing additional depth calibration. Eight horizons corresponding to zones of unproduced stacked gas bearing reservoirs (Beta-0, Zone-1A, Zone-1B, Zone-1D, Zone-1E, Zone-2B, Zone-3 and Zone-3A) were interpreted. Most of these zones exhibit strong seismic amplitude fluid responses to the gas bearing reservoirs, with seismic bright spots and flat spots corresponding to pay zones demonstrated on well logs at Buntal, and untested zones evident at the adjacent Alu-Alu, SW Buntal, and NE Buntal structures (Figure 6). DEPTH CONVERSION METHODOLOGY We utilized four different depth conversion methodologies: a linear average velocity-time function, a second order polynomial time-depth function, a V 0+KZ approach, and Dix corrected stacking velocities. These were analyzed and compared. Linear Average Velocity-Time Analytical Function This method used depth time pairs at the tops of the reservoir zones to compute average velocity using the total vertical subsea (TVDSS) well zone top and the seismic one-way time from the calibrated well tie. The one-way time and average velocity were cross-plotted independently for each zone, and a linear regression calculated to derive an analytical function of average velocity as a function of oneway time. This function was then applied to the seismic time map for depth conversion of each layer (Figure 7). To obtain sufficient statistical control for meaningful regression the zone tops were tied to nine (9) neighboring wells around Buntal Field. Subsequent to depth conversion the well residuals were flexed to tie with the depth markers using 2 km radius. If a reasonable regression is obtained this method has the advantage of being a simple and easy method to depth convert using multiple wells and is convenient for quick look and exploration work. The limitation in this case is that the availability of just nine wells as control points led to a scatter and a non-optimum regression line with R 2 as low as 0.59 (Beta-0) and 0.62 (Zone-1D), to a reasonable 0.87 at Zone-3. Regression gradients varied from a reasonably consistent 2.13 (Beta-0) and 2.25 (Zone- 3) to 2.81 (Zone-1D). Varying average velocity gradients are not easy to justify in terms of geologic compaction trends and are probably more a consequence of statistical instability in the method than geology. Furthermore, depth converting

3 horizons with independent functions is not a best practice as unrealistic interval thickness will result. 2 nd Order Polynomial Time-Depth Analytical Function This method used time-depth data from calibrated velocity logs from the three Buntal wells (Buntal-1, 2, and 3). The Alu Alu-1 well was excluded as it exhibits an independent time-depth relationship to the Buntal wells. This variation is attributed to velocity slow-down at Buntal below the Arang gas bearing zones (Alu Alu-1 has limited gas pay). A polynomial fit to these data provided an equation for depth conversion (Figure 8). After depth conversion the well residuals were flexed to tie with the depth markers using 2 km radius. The advantages of this method, it is a simple automated procedure, and unlike method 1, directly honors well velocity trends and is consistent between layers. However, the method requires care in deriving the calibrated velocity logs and good velocity data is a necessity. Furthermore, as demonstrated by the Alu Alu-1 well falling of the time depth function trend, the method is onedimensional and cannot predict lateral velocity variations away from the well control points. V 0k+Z Analytical Function Marsden (1992) describes the V 0k+Z (abbreviated to V 0k) method as applicable when a linear relationship between instantaneous velocity and depth is established (assumes a depth constant compaction factor (k)). The increase of velocity with depth due to compaction is a measurable and an important predictive tool for depth conversion. The method requires isolation of velocity layers associated with lithologies exhibiting well defined instantaneous velocity-depth trends. Method 1 (linear average velocity-time function) and method 2 (2 nd order polynomial time-depth) account for this effect, but not directly in depth and velocity changes which is theoretically a more correct measure of the compaction trend. As the solution of the equation in time results in an exponential term, the V 0k approach is computationally more complex than the polynomial time-depth method although it uses the same velocity data. For the zones of interest at Buntal, a single linear function from surface through the Arang formation was considered sufficient as it was not possible to separate any additional distinct layers. Although a general instantaneous velocity compaction gradient (k) is observed in the Buntal data, it has a low R 2 of 0.54 as the velocity logs are noisy with spikes due to washout/borehole rugosity, and gas bearing intervals cause local velocity slow down effects (Figure 9). For these reasons, the Buntal data is not ideally suited to the V 0k method. The V 0k workflow employed was as follows: 1) Apply median filter to the calibrated velocity logs and limited to the Upper Lower Arang Interval. 2) Determine V 0 and k values by linear regression of instantaneous velocity and TVDSS. For Buntal a single linear instantaneous velocity/depth function from surface ~ 0 datum level until the depth around ft tvdss ~ Lower Arang was used. 3) Constant K gradient term assumed to represent compaction of shales. 4) Back calculate V 0 for each zone top at each well location by rearranging equation: 5) Grid V 0 to create a V 0 map for each top zones (Figure 10). 6) Calculate a depth grid using equation the V 0 grid, k constant, and one way time grid. 7) The depth grid now ties the wells directly (implicit in the calculation of the V 0 map). However, residual miss-ties are possible due to the effects of gridding. The correction grid is derived using back-calculated V 0 values that depend on one-dimensional well data and are difficult to train away from well control. The method shares similar advantageous and disadvantages with the time-depth approach in benefiting from the densely sampled well velocity data, but having limited predictive capability away from well control. Dix Corrected Stacking Velocities A velocity cube was constructed using the raw hand-picked seismic stacking velocity data. The hand-picked values are preferred as they are not smoothed and therefore retain detail lost in smoothing processes used to derive migration velocities. The hand-picked data are noisy, but it is preferred to retain the detail and maintain control of

4 velocity smoothing processes. The velocity cube was gridded using a 500x500 m grid cell size. Dix corrections were then applied and average velocity surfaces extracted for each layer. Well-tie corrections factor were calculated by dividing pseudo average velocity (well depth/seismic time) with the average stacking velocity at the wells and the values gridded to provide a correction grid. This was then multiplied by the average stacking velocity in each surface grid to calculate a corrected average velocity surface grid for each layer. This corrected velocity was then used to compute depth grids. The main advantage of this method is seismic velocity data provides densely sampled velocity control and hence a means to predict velocity away from the well bores. The method is valid for high quality seismic data for depths up to the streamer length. Importantly, it is observed that this method captures the effect of gas presence as velocity slowdown is observed corresponding to the gas pay zones (Figure 11). If spatial velocity sampling away from the well control is important the use of seismic velocities for the depth conversion is highly desirable. However, this method requires good quality seismic data and careful velocity picking using a geologically constrained velocity model followed by statistical smoothing of redundant seismic velocity data to produce reliable seismic velocity data. It is noted that the Buntal data meets all of these criteria, and lateral velocity variation is desirable due to gas slow down effects. RESULTS We analyzed the results for the Beta-0 interval (see Figures 12-15). Beta-0 has an excellent fluid response enabling the amplitude shut-off to be compared with the calculated depth of the gas water contact established by well pressure analysis. A simple arbitrary line from Alu Alu SW Buntal Buntal NE Buntal was created to analyze the results for each of the depth conversion methods (Figure 16). Qualitative Methods: The amplitude shut-off at Beta-0 enabled a qualitative assessment of the depth conversion method predictive strength: i. The depth maps formed by V 0k analysis gives the best correspondence between the amplitude shut-off and gas water contact derived from well pressure data. The Beta-0 gas water contact also shows are good correspondence to amplitude shut-off for the linear average velocity-time and polynomial time-depth analytical function methods. ii. Using the Dix corrected stacking velocity; the gas water contact polygon separates into two areas as it has a significant higher velocity when going to the flanks. The gas-water contact is high relative to the amplitude shutoff implying the velocities are too fast. Quantitative Methods: A quantitative comparison of the depth errors calculated at the well control points is tabulated in Table 1. The errors were calculated as flows: i. For methods 1 and 2 (average velocity time and polynomial time-depth, the predicted error is the difference between calculated and actual values. ii. iii. The V 0k method implicitly ties the wells as a V 0k grid was used, hence the error was calculated by computing an average V 0 and the error based on the difference between the depth calculated using an average V 0 and actual depth. The Dix corrected stacking velocities also implicitly tied the wells as a calibration grid was used. Hence errors were calculated by computing an average calibration value and the error based on the difference between the depths calculated using an averaged calibration and actual depth. The statistical analysis shows residual RMS depth errors for each method are similar. For Beta-0, using Dix corrected stacking velocity gives the smallest error (11 ft) and polynomial time-depth approach gives the largest (23 ft) as seen on the Table 1; Therefore, based on the analysis of the RMS error values, Dix corrected stacking velocities method clearly superior for both Beta-0 and Zone 3. This was largely due to the Alu Alu-1 well being used to calibrate the stacking velocity method, but

5 excluded from the analytical functions as it falls on an independent velocity trend. Impact of Depth conversion Method on Gross Rock Volume Using the depth structure maps calculated using the four depth conversion methods, and the measured gas-water contacts, gross rock volumes were calculated for Beta-0, Zone-1D and Zone-3. These are graphically compared in Figure 17. While analytical function methods give similar GRV values, the Dix corrected stacking velocities consistently yields the lowest GRV values. CONCLUSIONS By using different depth conversion methods, it is possible to calibrate depth structure uncertainty for volumetric analysis. This is particularly useful where there is insufficient well control to reliably apply well drop-out geostatistical analysis. The four depth conversion methods used in the Buntal case study were predictive with accuracy within ± 50 ft of actual well tops. Three of the four methods were based on analytical function techniques (linear average velocity-time, polynomial time-depth, and V 0k functions). These three methods yielded similar results in terms of predictive accuracy and calculated gross rock volumes. Of the three analytical function methods, V 0k was statistically slightly superior. A Dix corrected stacking velocities approach contrasted in providing the statistical predictability at well locations, and lower gross rock volumes. Although the Dix corrected stacking velocities provided the best depth predictions based on statistical analysis, the method did not show as good qualitative predictive correspondence for the gas-water contact to amplitude shut-off at the Buntal Beta-0 horizon. The advantages and disadvantages of each method are summarized as follows: Linear Average Velocity Analytical Function This method is quick and easy to use, especially for exploration work. By using seismic times and well depths it is the easiest way to quickly generate depth structure maps. However, it is considered a quick look approach and not a best practice as: (a) independent functions are derived for each layer, leading to artificial calculated thickness variations between layers; and (b) limited control points and poor data scatter for regression analysis (for Buntal, 9 time depth pairs were used). The method was marginally the worst for Buntal predictive analysis. 2 nd Order Polynomial T-D Analytical Function This method honors densely sampled well data from calibrated velocity logs and hence predicts compaction trends better than the time depth pairs used in method 1. It requires careful calibration of velocity log data, and has the primary limitation of being unable to predict lateral velocity variations away from well control points. V 0k Analytical Function The V 0k method is theoretically the most correct of the analytical function approaches as it honors a true instantaneous velocity-depth trend which is the best approximation to a compaction trend. In all other senses it is similar to the polynomial timedepth function in that it requires careful calibration of velocity log data and is unable to predict lateral variations velocity away from well control. At Buntal, it was marginally the statistically superior method, although the velocity data was not ideal. Dix Corrected Stacking Velocities This approach is valid where high quality stacking velocity data are available as was the case for Buntal. It has the advantage over the analytical function method of providing a measure of lateral velocity variation away from well control points, although the velocity data itself is lower resolution and noisy compared with well velocity data. At Buntal it had the best overall statistical predictability and appeared to be able to predict effects such as velocity slow-down through gas charged zones. Despite these apparent advantages it was poorest in the qualitative predictive measure of correspondence of amplitude shut-off with the fluid contact at the Buntal Beta-0 reservoir. It provided the most significantly different outcome and lowest GRV estimates. In conclusion the qualitative observations of correspondence of the gas-water contact to the amplitude shut-off for the Beta-0 zone is compelling and favors analytical functions and V 0k in particular for Buntal GRV best technical case assessments. This conclusion is drawn owing to the uniquely high confidence in the amplitude shut-off correspondence with the gas-water contact.

6 However, the uncertainty ranges provided by other methods are vital for volumetric uncertainty analysis. Although the Dix corrected stacking velocities detect lateral velocity variations, such as those due to gas charged zones, the qualitative analysis of fluid contacts invoke too steep a velocity gradient. It is suggested this is due to stacking velocity profiles lacking adequate vertical and spatial definition causing artificial velocity slow-down above the shallowest pay zones such as Beta-0. Future Work Choice of correction techniques such as gridding residuals (extrapolating) or tapering with a radius from the well control point can have considerable bearing on the final result. This was constrained by a 2 km radius for this project (methods 1 and 2) or by error gridding (methods 3 and 4). Varying these parameters with consistency between methods is critical to further refine this analysis. ACKNOWLEDGEMENTS The authors would like to thank ConocoPhillips, Chevron South Natuna B Inc., Inpex Natuna Ltd. and MIGAS for their generous permission to present and share the materials used in this paper. REFERENCES Marsden, D., 1992, V o-k method of depth conversion, Geophysics: The Leading Edge of Exploration, p

7 TABLE 1 SUMMARY OF RESIDUAL DEPTH ERRORS (WELL DEPTH GRIDDED DEPTH) AT BETA-0 AND ZONE-3 FROM EACH DEPTH CONVERSION METHODS. THE SENSE IS THAT A NEGATIVE VALUE MEANS THAT THE DEPTH GRID HAS TO BE SHIFTED UP TO TIE THE WELL Beta 0 Zone 3 Error (feet) TD Method Buntal 1 Buntal 2 Buntal 3 Alu Alu 1 RMS Error VA Function TD Polynomial V0K Stacking Velocity VA Function TD Polynomial V0K Stacking Velocity

8 Figure 1 - Location Map of Buntal Gas Field, Block B, South China Sea, Indonesia Figure 2 - Beta-0 depth structure map (V 0K method), with maximum trough amplitude overlay using -4/6 ms window. Note amplitude conformance to gas-water contact and channelized features

9 Figure 3 - West Natuna Stratigraphic Chart & Buntal Typical Well Log

10 Figure 4 - Structural Well Correlation across Buntal Field

11 Figure 5 Arbitrary line showing synthetic seismograms for each Buntal wells

12 Figure 6 - SW-NE Regional Seismic Section across Buntal Gas Field (refer to Figure 2). Log curves are volume of shale (left-green/yellow) and resistivity (right-red). Bright spots and flat spots as Direct Hydrocarbon Indicators (DHI) are observed Figure 7 - One Way Time vs Average Velocity in Beta-0 (red), Zone-1D (blue) and Zone-3 (green)

13 Figure 8-2 nd Order Polynomial T-D function using 3 Buntal wells from ms (red line). Buntal wells are affected by Arang gas and show a slower velocity compared with Alu Alu-1 T-D pairs Figure 9 - Calibrated instantaneous velocity combined for Buntal from Buntal-1, Buntal-2 and Buntal-3 wells with median filter applied

14 Figure 10 - Buntal Beta-0 V 0 Map Figure 11 - Stacking velocity profile (Inline 8420) showing slow velocity associated with gas bearing reservoirs at the Buntal Structure. C-D marks line of section on Figure 2

15 Figure 12 - Buntal Beta-0 Depth Structure Map (via a linear average velocity-time function) overlay with Maximum Trough Amplitude Figure 13 - Buntal Beta-0 Depth Structure Map (via 2 nd Order Polynomial T-D Function) overlay with Maximum Trough Amplitude

16 Figure 14 - Buntal Beta-0 Depth Structure Map (via single layer V 0k Function, using average V 0 from each wells) overlay with Maximum Trough Amplitude Figure 15 - Buntal Beta-0 Depth Structure Map (via Dix corrected stacking velocity) overlay with Maximum Trough Amplitude

17 Figure 16 - Depth cross-section showing different depth conversion outcomes. Note stacking velocities are faster and hence deeper away from well control Figure 17 - Buntal GRV s distribution for each method (assume constant thickness and same gas-water contact for each zone)