PROCEEDINGS, INDONESIAN PETROLEUM ASSOCIATION Thirty-seventh Annual Convention & Exhibition, May 2013

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1 IPA13-SG-057 PROCEEDINGS, INDONESIAN PETROLEUM ASSOCIATION Thirty-seventh Annual Convention & Exhibition, May 2013 SEISMIC TIME TO DEPTH CONVERSION AND UNCERTAINTY ANALYSIS FOR HORIZON PREDICTION IN A PROPOSED WELL-SITE OF SUNGAI GELAM FIELD, JAMBI SUBBASIN Muhamad Wildan P* Imam Muda Gunawan** Andry Pujiriyanto** Sudarmaji* ABSTRACT Seismic time to depth conversion is an important part of seismic interpretation work flow. This research was conducted to determine the best method of depth conversion as a tool for horizon prediction in a proposed well-site KYT well. In this research, the seismic time to depth conversion process was divided into nine different methods clustered into two groups of methods. The difference in those methods lies on the variation of velocity model building. The velocity model building was built from two velocity sources: checkshot velocity and seismic stacking velocity. Those two velocity sources were processed using a geostatistical approach to obtain the velocity model. As the process results in several numbers of timeconverted data, it needs to be uncertainty analyzed to obtain the best method and the best depth values. The uncertainty analysis in this method was applied to four horizon Sand reservoirs (Top ARC, Top ASH, Base ASH, and Top TALC) by using the percentile classification approach, where the nine timeconverted data were classified into three percentiles: 10th percentile (P10) or Low Case, the 50th percentile (P50) or Base Case, and the 90th percentile (P90) or High Case. The results of the process show that the time-depth conversion method using the velocity model of the seismic stacking velocity with moving average interpolation shows the best result. This is justified by the minimum value of the horizon depth error to the same horizon in the well marker data. This uncertainty analysis is an effective and relatively inexpensive tool to guide * Gadjah Mada University ** Energi Mega Persada and to predict horizon depth in a drilling process to minimize the error. INTRODUCTION The recording method of seismic reflection data is in the domain of time (mostly in two-way time) (Sismanto a, 1996), whereas the interpretation of seismic data generally demands results using the depth. Therefore, the seismic time to depth conversion is one of the most important parts in the flow of the overall seismic data interpretation. In determining the value of depth, slope, thickness of a reflective plane (reflector) understanding seismic wave velocity is essential (Sheriff, R.E., and L.P. Geldart, 1995). One key aspect in the conversion of seismic data into depth is velocity modeling, which determines the relationship between the depth and the seismic time (TWT) (Etris, E.L. et al., 2001). A velocity model can be developed using many different methods depending on the purpose and the availability of existing data sources. In seismic data, processing in the form of migration also involves velocity modeling, but the migration process is not a process of conversion into the depths. One migration process aims to eliminate diffraction effects that can lead to misinterpretation. By undertaking this process, the best structural seismic section is obtained. Modeling the velocity of the migration process usually only involves the stacking velocity derived from the NMO (normal moveout), whereas the velocity data is not obtained from measurements (hard data), but from the NMO correction of trial and error (Sismanto b, 1996). Thus, to obtain the results of the seismic section in the depths, a process of its own, namely the conversion to depth using a velocity

2 model that involves stacking velocity, checkshot velocity (well velocity measurement), and a combination of both is used (Nam, N. and Lee, H.S., 2008). Converting depth can also be used to eliminate the ambiguity inherent in the structure of time and to test the validity of the structural pattern appearance. In the process, velocity modeling for depth conversion can be derived using a variety of input data, such as checkshot velocity, stacking velocity, and a combination of both, and can be based on certain geostatistics principles, linear velocity estimation, and interpolation (Abrahamsen, P., 1996). No single method is the main option in the depth conversion process. Each method has its own advantages and disadvantages as seen from several aspects, such as data availability, time constraints, cost, and so on. With that, ambiguity can arise in the process of converting the depth because the depth obtained by different methods often shows varying results. Therefore, we need to undertake analysis to minimize the errors that can be caused by the differences in the results obtained from the various methods. One method that can be used to solve such problems is the method of uncertainty analysis to obtain the classification percentile of the results obtained from the method used in the depth conversion process. For their analysis, the depth map can be specified for a particular target horizon that is considered the most likely value. METHODS a. Velocity Model The target of this research is in the Gelam field and focused on a two-way time (TWT) of ms. Reservoir rocks are found in the Air Benakat formation, which consists of shaly sands. The fault traps are present in this target zone, with two major faults. The horizon targets are divided into four sections: Top ARC, Top ASH, ASH Base, and Top TALC (Figure 1). In the case of velocity modeling, two main sources of velocity data were used: checkshot velocity (well velocity) and seismic velocity (stacking velocity, with the sampling time 250 ms). A velocity model was made of each source and the combination of both. Figure 2 shows the velocity modeling flow chart. The block of this research has six productive wells: X-A, X-B, X-C, X- D, X-E, and X-F (see Figure 3). The checkshot velocity was measured originally only in well X-B, while the checkshot velocity in other wells was generated by a well-seismic tie process. Generally, two groups of velocity models are divided into nine types of models (namely method A, B, C, D, E, F, G, H, and I). Velocity model group 1 (checkshot velocity) was derived from mathematical equations generated by the relationship of the T-D curve (Time- Depth curve) and the T-V curve (Time-Velocity curve) of the six wells. The mathematical equations for each curve are the main source for converting the seismic time data derived by using the linear regression. The form of equations derived by T-D curve is: Z(t) = Xt + C (1) where t is the TWT of the seismic horizons, and Z is the horizons depth (Figure 4). There are two ways to convert a seismic time map into a depth map by the T-D curve derived equation: the first one is the direct conversion, where the equation from the T-D curve is used directly to obtain the depth horizon by substituting Equation (1) to obtain the depth map (Z). Two velocity models are obtained by this method (namely, method A and B) are dissimilar in the limit of the plotted data. The second way to convert a seismic time map into a depth map using the T-D curve is to build the three-dimensional (3D) velocity model from Equation (1). The velocity is obtained by the following relation: Vavg (t) = 2000 Z t (2) where the Vavg(t) is the average velocity, Z is the horizon depth, and t is the seismic TWT (Figure 5). Two velocity models are obtained by this method (method C and D). In the case of the T-V curve, the mathematical equations are determined by plotting the Time versus Velocity (average velocity) data of the six wells, and the equation is derived similar to the T-D curve by using the linear regression method. The general form is shown in the following equation: Vavg (t) = Xt + C (3) where the Vavg(t) is the average velocity, and t is the seismic two way time (TWT). Figure 5 shows the velocity model built in the 3D cube by extracting Equation (3). This method also results in two velocity models: methods E and F. The difference in those methods lies on the limit of the plotted data, where in the E method, well data (average velocity versus

3 TWT) plotted from the datum (zero) to the lowest horizon, Top TALC, whereas, in the F method, the data are plotted from the highest horizon Top ARC to the Top TALC horizon. In the other case, the method of the velocity model in group 2 (seismic stacking velocity) uses a geostatistical approach to make the velocity model cube. In this group, the velocity cube was built by using three principles of geostatistics: simple kriging (method G), moving average (method H), and collocated cokriging (method I). The checkshot data are also needed in this group, as they are used as guidance for the vertical velocity interpolation when building the model. Therefore, this second group is a combination of checkshot velocity data and seismic velocity data in the process of velocity model building. b. Uncertainty Analysis From the results of nine types of velocity models, nine different types of depth map for each horizon were obtained. Therefore, uncertainty analysis was performed to classify the results of the nine depth maps for each horizon to obtain the most reliable value. The percentile classification method has been used to classify the overall results of the depth maps of each seismic horizon. Percentile classification was divided into three groups: 1. P10 or Low Case, to the deepest depths of the classification results, 2. P50 or the Base Case, the classification results for the depth which is considered the most likely, and 3. P90 or High Case, the classification results for the shallowest depths. c. Proposed Well The main purpose of this research is to predict the certain horizon depth in the proposed well location. This well, named the KYT well, is located in the upper part of the field. This proposed well is a distance of about 250 m to the east from the nearest well, the X-D well (see Figure 2). The uncertainty analysis was performed for the depth map results within this well location. This uncertainty analysis is a method to determine the most reliable result derived from the above criteria (percentile classification). A justification of the results was also performed by carrying out a blind test to calculate the deviation between the depth map result obtained by the depth conversion process in the existing wells and the marker data of such wells. RESULTS AND ANALYSIS The depth structure maps derived from the same group methods show similar results and characteristics (Figures 6 8). The results of the depth map in the same group of methods, group-1 and group-2, will show a similar structure (closure), whereas, the closure outcomes between different groups do not have such a striking resemblance. This is closely related to the velocity modeling process. Each group has its own step that is different for group 1 and group 2, so only the depth maps are included in the same group that will show identical results. The area of this study is approximately 25 km 2 and consists of six wells (X-A, X-B, X-C, X-D, X-E, and X-F). But the wells tend to cluster near each other. In conjunction with the velocity modeling, this is a disadvantage because the estimated value of the velocity model that uses input data, such as the well velocity, the point, or area whose position is far from the location of the well, tends to have a low accuracy. This is evidenced by the results obtained from the several methods. Figure 6 is an example of the depth structure map on horizon Top ASH. Methods A and F (Figure 6) are the methods by which the velocity model is based on checkshot velocity, while method G uses the velocity data calibration between wells (checkshot) and the seismic velocity data (Vstacking). The red circle on the index is the area far away from the location of wells. Method G returned depth values ranging m (yellow closure). Methods A and F in the same area produced a map of the structure with a depth range between m. This is obviously due to the different velocity values in each velocity model (methods A, F, and G). Unlike the case with the area near the well site, the results of the three methods suggest identical closures (green yellow), which indicate the velocity input data are in the same range. The depth structure maps of the method results of the same group show identical closure. Figure 7 for method A and method B for the Top TALC horizon shows the results of the depth map of the same group with a closure resemblance. Figure 7 (a) is the depth structure map obtained from method A of the horizon Top TALC, and (b) is the depth structure map derived by method B, the same

4 mathematical function but with a different data limit (starting point). Methods A and B have identical closure, but show different depths. For method A, the western and eastern zones, show values of approximately m, meanwhile for method B in the same area, depths range from m. The different results from are caused by the velocity model obtained from the wells, and in zones far from the well, the velocity value has a lower accuracy. Figure 8 shows the results of the methods G and H (group 2) displaying identical closure, but also similar values in the depth in almost all zones. That is understandable because the velocity model is based on seismic velocity, and the data points are scattered throughout the whole horizon area. Thus, estimating the velocity value in an unknown zone can be done by interpolating the velocity with a higher resolution of sampled data than the methods for group 1. The appearance of similarities of the closure patterns, differences, and similarities of depth values always appears consistent in almost horizons. Such consistency is derived from characteristics of the methods used in this study. Methods A and B, with the percentile results that tend to fall into percentiles P10 and P50, show the results of the horizon depth map within the KYT well has a high degree of risk. Method F, based on percentile classification, tends to obtain the P50 or the base case, which can be interpreted as the result with the highest degree of probability of truth. In addition, the results of the blind tests, method H (moving average) have relatively small deviations and are consistent across all horizons. Table 3 shows the deviation values of each method generated from the blind test for horizon Top ASH within the X-A and X-D wells. The X-A and X-D wells are located from the KYT wells as far as 1.6 km and 260 m, respectively. Table 4 shows the deviation value of method H (moving average) and the method F is relatively small in the Top TALC horizon. However, in the case of the depth map result, method H has a relatively more accurate depth distribution than the result obtained by method F (checkshot velocity only) in an area away from the well. This is because the velocity estimation of model H has more tightly sampled data. Percentile Classification The previous section describes that the standard deviation value is used as the control to determine the relationship between the results obtained for the depth of each horizon. The results indicate that the value of the depth of the nine methods has a good relationship, and this can be seen from the small standard deviation on each horizon. Table 1 shows the overall results for the nine methods along with standard deviations for each horizon. The results in Table 1 show that the percentile distribution is not specific for certain methods, but that this classification can be used to view the characteristics of the overall results for each group of methods. Table 2 shows the overall percentile classification results for all four horizons. From these results, it appears that methods A and B in group 1 are likely grouped into percentiles P10 and P90, which show the depth results obtained are the highest and lowest limits. The depth map results of such class are assumed high-risk results. Methods F and H tend to produce the P50 percentile, which means having the most likely depth value. CONCLUSIONS From the results, several conclusions can be derived. In the case of this study, where there are only a few well data and the wells are located close one to another, group method 2, namely the method of seismic velocity model with moving average interpolation (method H) is the best method to use. This is because it produces a small deviation and consistent data at all horizons. It is also important to be note that the uncertainty analysis of several different velocity models derived from various methods in terms of data sources (checkshot velocity data, seismic velocity data, and combination of both) and in terms of the steps taken (using mathematical equations and geostatistical approach) is a good method to classify the horizon depth maps by performing percentile classification. This uncertainty analysis method can be used as a tool to predict the depth of the horizon and as the controller in the drilling process of new wells to minimize the errors. ACKNOWLEDGEMENTS The authors are very grateful to Mr. Aris Setiawan as the General Manager of TAC Pertamina EP EMP Gelam who gave us permission to use the data for accomplishing this paper and the facility for the authors to complete the research.

5 The authors would also like to thank to Mr. Imam Muda Gunawan as their supervisor and Mr. Andri Pujiriyanto (EMP Malacca Strait) for giving many corrections and advices to the authors. We also thank the entire management and employees of P.T EMP Gelam who give their support to the authors to finish the research. REFERENCES Abrahamsen, P., 1996, Geostatistic for Time to Depth Conversion, Norwegian Computing Center, Box 114 Blindren, N-0314, Oslo, Norway. Etris, E.L., et al., 2001, True Depth Conversion: More Than A Pretty Picture, Core Laboratories Company, CSEG Recorder. Nam, N. and Lee, H.S., 2008, Rapid Multiple- Scenario Depth Structure Risk Analysis Case Study In Cuulong Basin, Vietnam, Proceedings, Indonesian Petroleum Association, Thirtieth Annual Convention & Exhibition. Sheriff, R.E., and L.P. Geldart, 1995, Exploration Seismology, 2 nd edition, Cambridge University Press. Sismanto a, 1996, Akuisisi Data Seismik, Modul Kuliah, Geophysics Laboratory, Faculty of Mathematics and Natural Sciences, Gadjah Mada University, Yogyakarta. Sismanto b, 1996, Pengolahan Data Seismik, Modul Kuliah, Geophysics Laboratory, Faculty of Mathematics and Natural Sciences, Gadjah Mada University, Yogyakarta.

6 Table 1: Overall results for each horizon Top Top Base Top No Method ARC (m) ASH (m) ASH (m) TALC (m) 1 Method A [Direct Conversion] Method B [Direct Conversion] Method C [Function Model Z(t)] Method D [Function Model Z(t)] Method E [Function Model Vavg(t)] Method F [Function Model Vavg(t)] Method G [Simple Kriging] Method H [Moving Average] Method I [Colocated Cokriging] Difference between the lowest and the highest value Standard Deviation Group Method 1 Group Method 2 No Table 2: Percentile classification of the total results Top Top Method ARC ASH Base ASH Top TALC 1 Method A [Direct Conversion] P10 P10 P90 2 Method B [Direct Conversion] P90 P10 3 Method C [Function Model Z(t)] 4 Method D [Function Model Z(t)] 5 Method E [Function Model Vavg(t)] P10 6 Method F [Function Model Vavg(t)] P50 P50 P50 7 Method G [Simple Kriging] 8 Method H [Moving Average] P50 9 Method I [Colocated Cokriging] P90 P90

7 Table 3: Deviation values of each method generated from the blind test for horizon Top ASH within X-A well and X-D well. No Method Deviation in Deviation in X-A (m) X-D (m) 1 Method A [Direct Conversion] Method B [Direct Conversion] Method C [Function Model Z(t)] Method D [Function Model Z(t)] Method E [Function Model Vavg(t)] Method F [Function Model Vavg(t)] Method G [Simple Kriging] Method H [Moving Average] Method I [Colocated Cokriging] Table 4: Deviation values of each method generated from the blind test for horizon Top TALC within X- A well and X-D well. No Method Deviation in Deviation in X-A (m) X-D (m) 1 Method A [Direct Conversion] Method B [Direct Conversion] Method C [Function Model Z(t)] Method D [Function Model Z(t)] Method E [Function Model Vavg(t)] Method F [Function Model Vavg(t)] Method G [Simple Kriging] Method H [Moving Average] Method I [Colocated Cokriging]

8 Figure 1: Four horizon targets (time structures, TWT), from the shallowest to the deepest, respectively: Top ARC, Top Ash, Base ASH, and Top TALC. A major fault separates the northern and southern zones.

9 Figure 2: The Flow Chart of the Research. The main sources used in this study are the checkshot velocity data, seismic stacking velocity, well tops data (marker), and the seismic time structure (horizons). The process of velocity model building is divided into two groups.

10 Figure 3: Location of the six wells and proposed well KYT. The most wells are clustered in the northern part of the block (X-B, X-D, X-E, and X-F), with a major fault separating the south and the north.

11 Z (m) Depth (Z) versus Time (TWT) X-F X-E X-D X-C X-B X-A Top ARC Top TALC TWT (ms) Figure 4 - T D Curve. The relation of Depth (Z) against two way time (TWT) of the six wells. The colored dots (red and green) are the position of the shallowest and the deepest horizons. Figure 5: T V Curve. The relationship of the average velocity (Vavg) of the six wells against a two way time (TWT). The colored dots (red and green) are the positions of the shallowest and deepest horizons.

12 Figure 6: A comparison of method A (equation derived from checkshot), method F (3D cube model extracted from checkshot), and method G (velocity model from seismic velocity). The red circle indicates the difference of the closure and depth values between the results.

13 Figure 7: Figure (a) is the depth structure map obtained from method A of the horizon Top TALC, and (b) is the depth structure map derived from method B. The two methods result in a similar structure but different depth ranges. Figure 8: (a) is the depth structure map obtained from method G (simple kriging interpolation), and (b) is the depth structure derived from method H (moving average interpolation). Both of them show an identical structure (the closure) and similar values of depth in almost all zones.