DELINEATION OF TIGHT GAS SANDS BY 3D-3C VSP

Transcription

1 DELINEATION OF TIGHT GAS SANDS BY 3D-3C VSP Yousheng Yan1, Zengkui Xu1, Mingli Yi1, Xin Wei1 1. BGP, China National Petroleum Corporation Keywords: 1.delineation, 2.3D-3C VSP, 3.multi-component, 4.tight gas sand, 5.centroid frequency ratio 1 Introduction This paper presents a new attempt to apply 3D VSP technology to delineation of tight gas sands in Sulige gas field, Erdos basin, north-west of China. In this area, the target formations are in the Permian and of thin thickness, poor porosity and low permeability with delta sedimentary environments. In general, the P wave impedance of dry sands is higher than that of cap beds (shale). However, if the sand is saturated with gas, its P wave impedances will be decreased so that the contrasts of P wave impedances between the sand and shale become smaller. Obviously, the conventional P wave exploration faces challenges in the studies of the presence and properties of this type of reservoir. 2D surface seismic technology is a major mean for gas exploration in this area. The reason is that surface conditions in most of the area are of complexity which make the 3D seismic survey difficult. The local geophysicists and geologists have done a lot of works on seismic data interpretation, reservoir analysis, well location design and so on. Although some other methods such as AVO and pre-stack inversion methods have been applied and also beneficial to gas exploration in this area, the poor surface and subsurface conditions decreased the fidelity of compressional and shear wave attributes (AVO) derived from the Z component(vertical component) data and made these methods be of limited help in some area. In recent years, many applications of 3D VSP (3-Dimension 3-Component Vertical Seismic Profiling) technology were presented. However, only a few of them showed the results based on the integration of PP and PSv waves. Some of typical case studies are 3D VSP PP and PS imaging used for structural interpretation in the onshore[1] (Frederico Aguiar Ferreira Gomes et al, 2005), 3D VSP PP and PS imaging for carbonate reservoirs[2] (Yan et al, 2007) and etc. Most of us tried to get the information as much as possible from a 3D-3C VSP data set but had to give up ultimately because of a series of difficulties in the data processing. As a result, the multi-component attributes in 3D-3C VSP data have not been fully applied to reservoir analysis. To solve some of the problems in gas reservoir exploration in this area, a multi-component exploration project was proposed and sponsored by China National Petroleum Corporation. In this paper, we only discuss the application of 3D-3C VSP data to the tight gas sands. Our works include optimizing geometry design in data acquisition, principal methods in data processing and new multi-component attributes applied in data interpretation. By integrating the 3D-3C VSP data with geological, drilling and logging data, we complete a comprehensive evaluation on the tight gas sands and propose a new well location for drilling. 2 Challenges and objectives Geological settings Erdos basin in western China is a large craton basin with several sedimentary cycles. The gas source rocks are coal beds in the Taiyuan and Shanxi group of Permian with alternating continental and marine sedimentary environments, which include dark shale and coal. Sulige gas field is located in the middle of Erdos basin and the biggest one found in the onshore of China, as shown in Figure 1. The major gas-bearing sands are in TP and TC formations in the Permian. The TP8 formation in this area consists of shale and channel sand formed in braided drainage pattern in the delta plane.

2 Its average porosity and permeability at a single layer are12%~15 % and 0.15~2 md respectively.the Shan1 formation is generally of characters of meandering sedimentary with thinner layers. Its average porosity and permeability at a single layer are 7%~10 % and 0.13~0.16 md respectively. The geological environments with alluvial and delta develop stratigraphic traps. All of the favourable configurations of hydrocarbon source rock, reserve, cap bed, migration, path, trap and preservation form the gas reservoirs. Local logging and drilling results indicate the target formations are generally of thin thickness, poor porosity and low permeability with strong lateral variation. Figure 2 shows the distribution of channel sands with several wells tied, which are derived from interpretation of logging and drilling data. In this figure, red curve represents Gama ray logging and blue curves acoustic logging. It is clear that the distribution of channel sands is complicated. Figure 1. Location of 3D VSP project. Figure 2. Well-tie interpretation from logging data. Geophysical characters and challenges Sulige gas field was found by seismic exploration in Surface seismic technology has been widely used in the exploration and exploitation. As the exploration keeps going, more challenges occur. The area we are studying in this paper is around well-a. The surface conditions in this area are complex because of sand domes, alkali flats, alkali lakes and hard rock outcrops. The depth of major reservoirs is generally great than 3000m with a thickness of around 10 m. Because the average porosity and permeability are less than 10% and 1md respectively, it is typical tight gas sand. The works and experiences around ten years tell us the major challenges are following, 1. The attenuation of seismic waves from shallow layers is very strong. More than 40% of high frequency contents of seismic wave propagated in these layers are absorbed. Therefore, the dominant frequency of seismic data is generally less than 30Hz. Figure 3 shows the attenuation versus depth calculated from the zero-offset VSP data. The dominant frequency of first breaks of P wave is decrease from 45 Hz t0 35 Hz at a depth interval of m. It can be easily found that the main attenuation is from the layers above depth of 1200m. What we would like to emphasize is that attenuation shown in this figure is derived from only one way propagation instead of two ways propagation because the its calculation is performed on VSP data. For surface seismic data acquisition, the attenuation should be double or more. Even though some enhancement and compensation can be done in data processing, the whole effectiveness is still unsatisfactory. Hence, How to acquire the seismic data with a higher resolution is an important issue to be solved. 2. The conventional seismic attributes is no sensitive to the tight gas sands. For gas exploration, the amplitude in seismic data has been one of the most important attributes in data interpretation. In this area, however, application of amplitude attributes to tight gas sands becomes more difficult. Poor surface condition results in problems in statics, attenuation, consistency, velocity and so on. Complex subsurface condition increases the uncertainties in data analysis and interpretation. Figure 4 shows a cross-plot of P-wave velocity and S-wave velocity which are derived from logging and drilling data, where the pink triangles stands for dry sand, and blue rectangles for shale and red dots for gas-bearing sand. For dry sand, its P-wave impedance is

3 great than that of cap bed (shale). While the sand is saturated with gas, its P-wave impedance decreases. Thus, the contrast of P-wave impedances between gas-bearing sand and shale becomes smaller. Obviously, it is not a type of bright-spot gas reservoir which can be more easily delineated by seismic attributes. For this type of gas reservoir, the conventional methods such as instantaneous attributes and post-stack inversion have been replaced gradually with AVO and pre-stack inversion to increase the reliability in data interpretation. We find that the key points are the integration of compressional and shear attributes in AVO or pre-stack inversion for indication of gas reservoirs. In fact, the so-called shear attributes in these methods such as shear wave impedances and shear modulus etc, are estimated from the Z-component data by using the Zoeppritz equation. We think that this is a type of pseudo-shear attributes rather than true ones. The complexity of surface and subsurface conditions in some area reduce the fidelities of raw data. In this case, this type of methods, indeed, is of limited help. At this point, we have to find some new method to improve the situation. Helpfully, the multi-component seismic technology would be a better choice. Figure 3. Attenuation estimated from Zero-offset VSP data Figure 4. Crossplot of Vp and Vs from logging data. Geological objectives The major geological objectives in the multi-component exploration project include: 1. To delineate this type of gas sands in TP7, TP8 and TC1 formations by using multi-component seismic data. 2. To propose and deliver new well locations for drilling. Due to the imaging coverage of VSP exploration smaller than that of surface seismic survey, the objectives for VSP data will focus the area near the observation well. 3 Methods To improve the situation described above, two important issues, which include how to acquire seismic data with higher resolution and more information and how to integrate different type of seismic attributes for gas reservoir analysis, should be solved. For the first problem, the key point to acquire the broadband seismic data is to reduce the attenuation caused by the shallow layers. If the shots or receivers can be put below the depth of 1200m, surely more high frequency components of reflection signals can be recorded. As for the second problem, we can use multi-component sensors to record the different wave fields from the target zones. In this way, not only P wave but also converted waves can be integrated to study the gas reservoirs. In order to meet the demands of solving the two issues, we think that application of 3D-3C VSP technology is good choice. Before we introduce the methods, let us review the benefits of 3D-3C VSP technology: 1). One way attenuation caused by near surface. This is because the receiving array is located in borehole

4 instead of on surface. The reflection signals recorded in receivers don t propagate in near surface and probably are of higher frequency components. 2). Multi-component sensor recording. In a VSP survey, 3-C geophones are generally used. The converted waves, even shear waves as well as compressional waves can be recorded simultaneously. Thus, more seismic attributes for reservoir analysis will be available. 3). Accurate depth information of receivers. Receiver array is located in downhole and its positioning is accurate. In this geometry, we know both the depth information and travel time of first arrival at each receive. This means we can use them to build velocity models, which are much more accurate than those built by surface seismic data. This is one of most important information we would like to get for VSP data processing, particularly for imaging of multi-component data. 4). Anisotropic information. Area anisotrophy analysis can be easily done by integration of receiver depths, offsets and travel times of first breaks on VSP data. Under assumption of anisotropic theory, various anisotropic parameters can be estimated. On the one hand, anisotrophy studies can be used in reservoir analysis directly. On the other hand, the anisotropic parameters can be used for imaging. After completing a feasibility study in the late of 2004, China National Petroleum Corporation made a decision to sponsor a multi-component exploration project in this area, which included a surface 3D-3C seismic survey and a 3D-3C VSP survey. In this paper, we only discuss the latter one. The main works we did, including geometry design in data acquisition, target-oriented data processing and results based on data interpretation and multi-component attributes analysis etc, will be introduced. Geometry design and optimization According to the multi-component seismic exploration project, the 3D-3C VSP survey should be conducted simultaneously with a 3D-3C surface seismic data acquisition. Therefore, for the 3D-3C VSP geometry design, the main shooting parameters including shot numbers, shot positions, hole depths and explosive sizes, etc, are shared with those in the surface seismic survey, only the receiving parameters including observation interval, receiving spacing, etc, are involved. To acquire seismic data with a higher resolution and a higher S/N ratio is the most important work in the VSP geometry design. As shown in Figure 3, the most of high frequency components and energy of seismic waves are attenuated in the depth interval of m. This means that the method to reduce the attenuation in data acquisition is to put the receiver array below the depths of 1200m. The recorded signal in this way is only of one way attenuation which makes the seismic data with higher frequency components possible. The longest downhole receiver array we had at that time consists of 8-level 3-C geophones. Based on the geological objectives, a target-oriented 3D-3C VSP survey geometry was designed by incorporating analysis of well data, investigation of near surface, building of velocity model, forward modeling, evaluation of acquisition parameters, test of shooting and recording in the field. Some parameters, including imaging coverage, reflection folds, illumination map and responses of multi-component on the target zones, etc, were calculated in the geometry design. In general, we need to design several geometries for calculation of the parameters. All of them are used to evaluate and determine the final acquisition layout. The designed geometry needs test in the field before operation. For our project, we were told in the filed test that 2-level geophones didn t work. To be a compensation for the observation with the small array, about 2500 shots were added. Thus, the final acquisition parameters for this project are following: total of shots:15294; shooting line spacing: 280m; shooting point spacing: 40m; receiving interval: m;

5 receiver spacing: 20m; receiver array: 6-level 3-C geophones; Target-oriented data processing The strategy in the data processing is still focused on treatment of geophysical and geological problems described above. According to the designed acquisition parameters, no doubt the 3D-3C VSP survey is a massive one (more than shots). Because the number of shots is from the design of surface seismic survey, some of shots are far from the well-a. The largest offset in the VSP survey is great than 10km. This means not all of data can be used in the data processing. For taking full advantage of the acquired data and making the imaging coverage as large as possible, the 3-C raw data within an offset range of m (from wellhead to sources) will be processed. The characters of the 3D-3C VSP data set, including a large offset range changing from m and a small array consisting of only 6 levels geophones, result in many difficulties in processing. The main difficulties are from application of the vector polarization of the multicomponent data to wave fields separation. What we would like to know exactly is not only the wave propagation but also the wave polarization. We divide the processing target into two parts. In the first part, what we need to do is to solve the problems in each processing step which probably influence the quality of PP and PSv waves images. In the second part, we need to determine what kind of the multi-component attributes sensitive to the tight gas sand and how to get them. For these purposes, a detailed full 3-C data processing flow is designed in terms of the analysis of raw data in different domain. In the following text, we will discus some important steps or methods listed in the data processing flow such as wave-field separation, velocity model building, PP and PSv waves imaging and multi-component attributes calculation and evaluation. (1). Wave-field separation In contrast to surface seismic data, the wave fields in VSP data are much more complex because the receivers are located in downhole. Basically, the major wave fields we can see in VSP data sets include five types of waves which are down going p wave, down going converted wave, down going shear wave, up going p wave and up going converted wave. In most cases, up going waves that propagate in the same direction as its polarization (P wave) are commonly used for imaging. The up going signals are often heavily contaminated by the down going waves. It is clear that a high quality imaging is dependent on an effective wave-field separation. The difficulties in the wave-field separation on this data set are, firstly attributed to the offset changes from near to far, which made the conventional hodograms method inapplicable, secondly attributed to the few channels in a common source gather, which made some well-known filtering methods such as median filter, FK filter, etc, unavailable. Hodograms analysis is based on the trajectory of the movement of particles associated to a wave. In general, the three components data are reoriented by two steps. The first step is that the two horizontal components (X and Y) are reoriented to get a radial and tangent component. The second step is that the Z component and radial component are reoriented to get the components in the polarization plane of down going p wave and the components in the polarization plane of down going converted wave which is orthonormal to the former one. Thus, it is easy to separate the up going converted wave on the former plane and the up going P wave on the latter plane. As we know, the hodograms analysis is oriented to offset VSP data processing. Because the offset from wellhead to source is a constant, we can optimize the design of offset on which the hodograms method can be used to separate the wave fields effectively. The effectiveness of hodograms method is usually depended on the selection of the offset. For a 3D-3C VSP survey or a walkaway VSP survey, however, the offsets change from near to far, the relations among various wave fields change as the offset changes. The methods based on the hodograms are no longer available. In order to improve the effectiveness of wave-field separation, we developed a new approach called time-

6 variant vector analysis. The method is based on the propagation and polarizations of various waves in different travel time. We implement this method in two steps. First of all, we calculate the propagation direction and polarization orientation versus time. Under assumption of linear polarization of the wave fields, the up going PP and PSv waves in the Z and radial components are reorientated on their own polarization plane in terms of orthonormal properties of various waves. The key point in this method is to set up two polarization plane, one for up going P wave and another for up going converted wave. Then, a high resolution Radon transform is applied on these data to get the wave fields we need. (2). Velocity estimation Shall we simply apply velocity analysis methods used in surface seismic data processing to 3D VSP data processing? The answer is negative. The reason is that there are many problems to sort the VSP data in CMP gathers. Even though there are some companies who try to do velocity analysis in this way, the accuracy of velocity estimation is limited. Fortunately, the information of receiver depth in VSP data can help us to perform velocity analysis in many ways. In our methods, the travel time picked on the multi-component data and receiver depth are integrated to estimate the velocity. The estimation procedure is iteratively carried out by an inversion algorithm. According to the demands of imaging and the complexity of subsurface, a velocity model is built usually in three ways: 1) One dimension velocity model building. We assume the subsurface consists of horizontal layers with homogenous media. The first breaks of P and S (if any) on zero-offset VSP data are picked and used for estimation of velocity by inversion algorithm. The velocity model built by this method is accurate vertically near the observation well. For a small imaging coverage and simple subsurface condition, this type of model is available. 2). Anisotrophy analysis. In this paper, we take only P wave anisotrophy analysis for example. First, we can calculate the first breaks of P wave by integrating 1D velocity model and offset or walkaway VSP geometry. Then, the differences between the calculated time and first breaks picked on the raw data are calculated. Small differences indicate that the media in this area is isotropic. If the differences are big enough, it is necessary to study the velocity in detail. Anisotrophy analysis is one of the studies. The differences can be used to estimate the anisotropic parameters in terms of Thomsen L. theory. ε and δ are major anisotropic parameters we would like to estimate. η can be derived from the ε and δ. In this case, the velocity model consists of 1D velocity and anisotropic parameters. 3). Complicated velocity model building. For a complicated subsurface condition, velocity model building faces great challenges. Our strategy is to take full advantage of travel time information in VSP data. Not only first breaks but also reflection travel times are needed to be picked. The velocity estimation is also performed by inversion algorithm in which tomography is a popular method. Usually, this method is an adaptive algorithm and don t need to build a geological model before inversion. Shear wave velocity model building is much more complex than P wave s and should be done after completion of the P wave velocity model building (Yan et al, 2004). (3). Imaging of PP and PSv waves For better integrating multi-component information for reservoir analysis, we perform the imaging on the PP and PSv wave data in depth domain instead of in time domain. Its advantages are there are no scaling problems in the PP and PSv images matching if the velocity model building is reasonable. Even though VSPCDP is a routine method for VSP data imaging, Kirchhoff migration is recommended. If the studying area is of strong anisotropy medium, migration with anisotropic parameters should be considered. In this paper, we perform migration on a 1D velocity model and VTI anisotropic parameters. (4). Calculation of CF ratio CF is called centroid frequency. The method of CF calculation was proposed by Mr. Quan et al in 1997 and used to estimate the attenuation on crosswell seismic data. We try to use the CF of various wave fields to

7 generate a new attribute for reservoir analysis. CF is calculated in a frequency domain. Its value is equal to a ratio of the integral of amplitude at a frequency multiplied by the frequency along the frequency direction and the amplitude. The CF calculation indicates its value is independent of amplitudes which will make the data processing simpler. For multi-component attributes, we known that, theoretically, the shear modul is independent of saturation level of fluid. Therefore, we propose a new attribute, called CF ratio of PSv wave to PP wave, for delineation of gas-bearing sand. Because share waves involve only sands and compressional waves involve both sand and fluid in sand, the ratio of shear wave attributes to compressional wave attributes gives prominence to the responds of fluids. We are used to integrating P wave amplitude or impedance with S wave amplitude or impedance for reservoir analysis. But there are many difficulties to preserve their true attributes in data processing. This is why we make an attempt to use the CF ratio. 4 Results The 3D-3C VSP data acquisition was conducted simultaneously with a 3D-3C surface seismic survey in Figure 5 shows the map of shots. Meanwhile, two zero offset VSP surveys were conducted with P and S sources respectively. Analysis of raw data is a prelude to perform data processing. Common receiver gathers (CRG) instead of common source gathers (CSG) were chosen for analysis because of only 6 channels data in the latter one. In addition, a series of data analysis, such as surface consistency, shot static correction, attenuation compensation and 3-C polarization, was implemented. All of them were conducted with QC and had a very good start to realize the entre data processing process optimization. Figure 5. Map of shots in the 3D VSP survey. The red line is about 10km long. Based on the methods and steps in data processing described above, we completed the data processing in 2 months. Firstly the up going PP and PSv waves were separated effectively by using the time-variant polarization and high resolution Radon transform; Secondly, we found the overburden sedimentary is stable, well stratified and flat. The analysis of travel time derived from various offsets VSP data indicates that the strata are of highly VTI anisotropic signatures. So, anisotropic parameters (ε,δ) were estimated and integrated with interval velocity derived from zero-offset VSP data to build a velocity model for imaging. Finally, Kirchhoff migration was performed on these separated data and velocity models, two imaging volumes (PP and PSv waves) with a bin size of 20*20m and depth interval of 2m were generated. The imaging areas of PP and PSv wave cover around 18Km2 and 6.3km2 respectively. Figure 6 shows the two imaging volumes (left: PP wave imaging; right: PSv imaging) where the green polygons indicate the zones of interest. In order to compare with surface seismic data, the imaging volumes were converted from depth domain to time domain. Figure 7 shows

8 the comparisons of surface seismic images and 3D VSP images. The amplitude spectra indicate that the dominant frequency of VSP images is 10-15Hz higher than that of surface seismic images. Figure 6. 3D VSP PP (left) and PSv (right) images. Figure 7. Comparison of VSP (right ) and surface seismic ( left) images.top: PP wave. Bottom: PSv wave. Two imaging volumes were interpreted respectively. The main target horizons in this area are in the Permian and include TP and TC Formations, which are over a depth range of m. Data interpretation started with the horizons identification. Zero-offset VSP data was applied to identify several horizons of interest as shown in figure 8. Horizons interpretation was done to generate the depth/time horizon maps that would be used for further attribute calculation and interpretation. The difference of horizons indicates the interval of formation which can help us to know the distribution of sands and shale. Figure 9 shows the time difference of TP8 and TP7 horizons. The centroid frequencies of PP and PSv images were calculated in given windows. The calculation is based on the horizons interpretation. For calculation of the CF ratio, PP imaging was muted with same coverage size as PSv imaging. Thus, two CF volumes were generated. Then, the CF of PSv is divided by CF of PP to generate the CF ratio volume as shown in figure 10. We find that there are two anomaly zones along north-south direction at depth of 3340m. The local geological settings tell us the source rocks are located in the coal beds in the north and the most of channel sands are distributed in the north-south direction.

9 Figure 8. Target horizons identified by zero-offset VSP data. Figure 9. Time difference between Tp8 and Tp7 horizons. Figure 10. CF ratio volume (left) and a horizon slice of CF ratio (right). Two anomaly zones delineated by the CF ratio were interpreted as gas-bearing sands. Therefore, the two anomaly zones are interpreted as gas-bearing zones by integration of logging, drilling and geological data. For the two gas-bearing zones, our interpretation is that the centriod frequencies of reflection signals of PP wave are influenced by the lithology and probably by fluid in the formation and the centriod frequencies of up going converted waves are only influenced by the lithology in the formation. A lots of results in seismic data demonstrated that centroid frequencies of PP wave were strongly attenuated by gas in the formations. A very important fact is that the CF ratio of PSv and PP images zooms in the seismic responses from gas. Guided by the anomaly zones, one well location for drilling was proposed with a comprehensive evaluation. 5 Conclusions This paper demonstrated the feasibilities and effectiveness in application of 3D-3C VSP data to delineation of the tight gas sands in this area. The ideas of target-oriented 3D-3C VSP exploration were throughout the whole implementing process which includes data acquisition, processing and interpretation. The major research results are concluded as follows: 1) Optimizing geometry design increases the quality of raw data. The downhole receiver array is configured below the depth of 1200m and can records the up going wave with higher frequency components (only one-way attenuation). The fact that the resolution of VSP reflection images is higher than that of surface

10 seismic images makes the more detailed data interpretation possible. 2) Target-oriented data processing is necessary to get the high quality PP and PSv images and to apply the multi-component information to analysis of reservoirs. In this data processing, accurate velocity model and well-separated wave-fields lay a good foundation for high quality imaging. High precision images of PP and PSv waves in depth domain solve the scaling problems between them in time domain. The integration of PP and PSv images in data interpretation provides more information for reservoirs analysis and reduces uncertainties. 3) A new attribute called CF ratio is proposed in this paper. The attribute can zoom in the seismic responses from fluid and also can be thought as an indicator of gas-bearing sands. 3D-3C VSP technology is successfully used for delineation of tight gas sands in this area. This doesn t mean the methods presented in this paper will be available in other area. For different objectives, what we need to do is to make sure the problems, study the corresponding strategy, perform detailed planning, improve the methods and finally get the expected solution. References [1]Frederico Aguiar Ferreira Gomes et al, 2005, Multi-well 3D VSP P-P and P-S imaging used for structural interpretation in the onshore, CAM-field, Brazil.75th SEG annual meeting. [2]Yousheng Yan, Zengkui Xu, Mingli Yi, Xin Wei, D VSP PP and PSv imaging for carbonate reservoirs. 69th EAGE Conference, H016. [3]Yousheng Yan, Mingli Yi, Xin Wei, Zengkui Xu,2004, C wave processing of 3D VSP data, 74th SEG annual meeting. Acknowledgements We would like to thank BGP for permission to publish this paper, and also Mr. Yabin Guo who provided some figures on data interpretation.