Characterizing tight, thin reservoirs using high-density 3D seismic a case study from the central Sichuan Basin

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1 Characterizing tight, thin reservoirs using high-density 3D seismic a case study from the central Sichuan Basin Bo Liang 1, Meng Zhang 1, Fusen Xiao 2, Jinli Yang 2, Xue Lei 3*, Donghai Liang 3, Rong Li 3, Fang Li 3, Qinglin Liu 3, Haiyan Qian 3 and Lufeng Zhan 3 present the results of an integrated reservoir characterization solution applied in southwest China utilizing high-density, full-azimuth broadband seismic data. I n recent years, thin, tight carbonate reservoirs and thin, tight sand reservoirs have represented two important types of hydrocarbon exploration and development targets in Jurassic strata in central, northwest and northeast areas of the Sichuan Basin. Most production from these onshore areas has been closely associated with numerous small faults, fracture corridors, and micro-cracks in thin carbonate layers; and in high-porosity zones in tight, thin sand bodies. Reserves for the Sichuan Basin are most likely higher than current estimates (Liang et al., 2011). Demand for increased production from the area has made it imperative to perform reservoir characterization with the highest possible accuracy. This article presents a case study in which seismic characterization of Sichuan reservoirs was improved by using a full-azimuth, high-density land seismic survey calibrated with well log data. A comprehensive data processing sequence (Li, 2014) was devised utilizing full-azimuth algorithms to analyse the high-density data used in the case study. Quantitative reservoir characterization was achieved by well log data correction, rock physics analysis, simultaneous amplitudeversus-offset (AVO) inversion (Rasmussen et al., 2004) and lithology identification (Takahashi, 2000). Specifically, we have been able to successfully delineate the geometry of the hydrocarbon-bearing sand channels. Quantitative multi-scale fracture characterization was also achieved by simultaneous azimuthal AVO (AVOaz) inversion and seismic post-stack ant-tracking (Paddock et al., 2008). The Gongshanmiao oilfield is located in the central Sichuan basin palaeo-uplift area, and features gently dipping structures, thrust faulting and fractures. Many years of exploration experience and analysis indicate that fault development has played a significant role in the formation of tight carbonate reservoirs in the area and a constructive role in the upward migration of hydrocarbons into shallower traps of tight sands. The region has experienced several phases of tectonic movements since Sinian (late Precambrian) times. Periods of uplift and subsidence have resulted in multiple sedimentary sequences separated by diastems and erosions. The caprock has small structural variation and gentle fold. Large-scale faulting developed in the Huaying and Longquan mountain belts at the eastern and western margins of the basin, but the stiffness of the central Sichuan basement helped to resist tectonic movement, leaving the oil-bearing region with relatively simple structures. Included in this study are three sub-units named Sha ximiao, Liang gaoshan and Da anzhai (for short Sha, Liang and Da, respectively) ranging from shallow to deep within middle and lower Jurassic stratum. Two main reservoir units comprise middle Jurassic tight channel sands and sheet sands in the Sha sub-unit and an interbedded sand/ shale sequence in the Liang stratum. Average porosity of cores from the tight silt-fine sand ranges from 3% to 6%, and it typically has low permeability (less than 110 md). Higher porosity zones (up to 12%) appear within specific sedimentary lithofacies such as various sand banks with increased particle size (Xie et al., 2004) in the Sha stratum, but with bed thicknesses less than 5 m. Significant quantities of oil and gas also are present within a highly fractured carbonate sequence beneath the clastic sequence in the lower Triassic Da stratum. On the basis of its characteristics, the carbonate is classified as sparry clasticshelly limestone, argillaceous shelly limestone and shelly marlstone, among which the sparry clastic-shelly type is the dominant reservoir lithology. Three sub-layers are divided into Da1, Da13 and Da3 within the Da stratum vertically. The dark shale layer of Da13 is a hydrocarbon source rock. The top Da1 and Da3 are the two main fractured reservoirs. Average porosity of these two carbonate sequences is less 1 CNPC Sichuan Geophysical Company (SCGC). 2 PetroChina Southwest Oil and Gasfield Company (SWOGC). 3 Schlumberger. * Corresponding author, xlei@slb.com 2014 EAGE 85

2 first break volume 32, May 2014 than 0.85%. Permeability is less than 100 md (Zhao et al., 2008; Wang et al., 2013). The overall thickness of the top Da1 and Da3 is less than 4 m and 12 m respectively. Extensive studies by Xie and Tang (2004) for the central region of the Sichuan Basin showed that the Da1 and Da3 are typically micro-pore plus micro-fracture reservoirs. Microfracture development caused by tectonism is extensive over a large reservoir area, providing good connectivity and partially contributing to storage space, and it also significantly improves permeability for the carbonate and sand reservoirs. Faults and vertical fissures in the shale strata of Liang and Da13 are conduits for vertical petroleum migration, and also control the distribution of reservoirs in the Sha stratum. The benefits of full-azimuth seismic acquisition have been well documented (Vermeer, 2002). In 2013 PetroChina Southwest Oil & Gas Company (SWOGC) acquired a full azimuth, high-density 3D seismic survey using the UniQ integrated point-receiver land seismic system (Papworth, 2009). Shallow-hole, small-charge-size dynamite sources were deployed over a 50-km 2 area (full fold) in the central Sichuan basin, southwest China (Li et al., 2014). After application of a comprehensive full-azimuth, broadband processing sequence, the new dataset showed high resolution, high signal-to-noise ratio, extended low frequencies and high amplitude fidelity compared to a data from a previous vintage of survey (Figure 1). DSI dipole sonic shear imager measurements were available for three wells outside the survey area. DSI measurements were also available for one well inside the survey area, aimed at a horizontal section through a fluvial braided channel sand. Full-azimuth solutions The analysis and interpretation study comprised six main phases, designed to extract maximum value from the new Figure 1 Top left: Example gathers from the UniQ point receiver acquisition used as input to seismic inversion. The pink and blue lines represent different azimuth and offset respectively. Top right: Comparison of frequency spectra between the vintage and the new UniQ data. The lower panels are 3D blocks with coherence attribute depth slices around the level of the D3 carbonate from vintage data (left) and the new dataset (right) EAGE

3 Figure 2 Cross-plot analysis in the target interval of raw log data (top) and after careful editing (bottom) to one well beyond, but close to, the seismic survey area. seismic data to meet the high prediction objectives in the project area: 1. Initial feasibility study to assess whether AVO inversion could address the reservoir characterization challenges. 2. Data pre-conditioning of seismic gathers and logs to maximize the quality and accuracy of seismic inversion. 3. Pseudo-shear log prediction in the absence of measured logs. 4. Simultaneous AVO inversion to solve for acoustic impedance (AI), compressional and transverse wave velocity ratio (Vp/Vs) and density, followed by rock physics analysis to define lithology classification (Bachrach et al., 2003). 5. Simultaneous azimuthal AVO inversion (AVOaz) to solve for fast and slow shear to address the challenge of microfracture discrimination, and ant-tracking to characterize small faulting and fracture corridors. 6. Blind well validation to understand and verify the suitability of the process and accuracy of the inversion results. Around 30 wells existed within a 100-km 2 zone around the study area, from which log data were available for 12 wells. A complete petrophysical log analysis was performed that extended beyond the seismic survey and included two wells whose seismic well ties on the full PrSTM section had the highest cross-correlation among the seismic calibrations available of the seven wells within the block. Petrophysical log analysis included generation of volume fractions of clay, sand and carbonate; and total porosity based on normalized gamma ray, neutron and density data. Shear velocity information was required for AVO inversion of the seismic data, but no downhole shear velocity measurements existed from within the study area. However, shear measurements were available from four wells in the surrounding area. Comprehensive and careful editing of measured compressional velocity, shear velocity and density log data from these four wells provided a solid foundation for prediction of pseudo shear. Data for one of these wells is show in Figure 2. It was intended to use the petrophysical logs for generating pseudo-shear logs, and the method proposed by Greenberg and Castagna (1992) plus a local correction constant was successfully used to predict pseudo-shear for the formation above the Liang lower top. This method was appropriate as the formation is typically a sand and shale channel sequence in which accurate mineral volume fraction definition is achievable. Inputs were the interpreted sand and shale fractions, and the compressional velocity calculated from acoustic log data. An alternative approach for generating pseudo-shear logs was required below the Liang lower top due to the limited ability to accurately interpret mineral volumes in this shaly calcite formation. As a result, multivariate regression by a neural network approach was used to predict shear velocity. Input log-derived data included gamma ray, resistivity, neutron, density, calcite volume fraction, and compressional velocity calculated from acoustic logs. Predicted shear velocities agreed closely with shear measurements from the available log data, as shown in Figure 3, providing confidence in the modelling approach. Subsequently, pseudo-shear velocities were estimated using the same approach for the two wells (called Well-1 and Well- 2) in the block with the best calibration to the seismic data, providing input to AVO and AVOaz inversion. Four angle stacks (0-11, 11-22, and ) were generated for simultaneous AVO inversion. Six azimuth sectors ( degrees) with the same seismic angle band decomposition as prepared for AVO inversion 2014 EAGE 87

4 first break volume 32, May 2014 Figure 3 Comparison between measured and predicted shear velocity below the Liang lower unit (two left panels) and above the Liang (right three panels). Figure 4 Four angle stacks ( 0-11,11-22,22-33 and ) after alignment (left four panels); one trace extracted from the angle stacks at same location (middle), multi-wavelets extracted from well 1 and well 2 for each angle stack (right). were selected to be the input datasets for simultaneous AVOaz inversion. The six azimuth sectors were defined with consideration of the orientation of the major reverse fault in the block, which mainly has a strike of 120 but laterally varies to 90. Residual misalignment in the angle stacks was corrected using a non-rigid matching method (Nickel and Sonneland, 1999) with reference to the 1-11 angle stack and the data of the 1-11 angle stack at zero degree orientation. Input data and wavelets extracted from the two wells for AVO inversion and AVOaz inversion are shown in Figure 4 and Figure 5. Wavelets extracted from the aligned angle stacks were highly stable and consistent. Besides wavelet extraction, achieving a reasonable low frequency model (LFM) in seismic inversion is of great concern in any absolute inversion procedure aimed at quantitative characterization. Frequencies below 6 Hz were not described in the seismic data at the specific target interval (900 to 1350 ms) so required modelling, and frequencies in EAGE

5 the range 6-8 Hz were weak. A low frequency model up to 8 Hz was calculated using AI, density logs of eight wells, two pseudo Vp/Vs logs of two wells and a structural model. Another challenge encountered in LFM building was how to establish correct contact relations for major reverse faults in the block. Structural modelling was utilized to deal with this issue. Figure 6 is a schematic of the procedure used to build the AI LFM using data from the eight wells. Reverse faulting was an integral part of the low frequency model construction. The inversion algorithm, or engine behind simultaneous AVO inversion, delivers the highest possible resolution through the application of global optimization to a single, nonlinear objective function. The low frequency information can be expected from relative AVO inversion, which is why the relative inversion result was used to generate a guide Figure 5 24 Input angle stacks of 0-11,11-22,22-33 and after alignment applied in six azimuths (left panels); Examples of wavelets (time domain) extracted from two wells (right panels). Figure 6 Information being introduced into the LFM and the procedure used to build the AI LFM utilizing structure modelling. (a) One large reverse fault panel in light blue and four small reverse faults. Three small reverse faults are merged as one panel in light yellow; (b)five horizons (two at the upper wall and footwall for each horizon) and two time slices (not displayed); (c) Guide model derived from PrSTM seismic velocities, relative AI inversion from simultaneous AVO inversion and the horizon-faults framework; (d) A similar AI model of wide frequency band built with the guide model, logs and the horizon-faults framework; (e) AI LFM after 8 Hz low cut filter applied to the AI model; (f) Final AI absolute inversion with the LFM model EAGE 89

6 first break volume 32, May 2014 model with seismic velocity. This was not the 3D seismic velocity directly, but its transformation after an AI trend less than 2 Hz was introduced, which was a calculation of two regression relations of AC and AI derived from logs for two intervals above and below the Liang low sub-unit strata respectively. Reliability of the seismic interval velocity at less than 2 Hz was validated using the low frequency trend behind whole DT logs in the eight wells. The AI, Vp/Vs and density derived from simultaneous AVO are shown in Figure 7. Overall, the inverted seismic AI and Vp/Vs matched with the logs. It is generally difficult to invert for density because of the dependence upon having high-quality data at very far offsets close to critical angles, but in this case the inverted density looked realistic, and was considered to add to the characterization of the reservoir. Reservoir properties were derived from the inverted elastic properties using the LithoCube method. Rock physics properties such as AI, Vp/Vs and density were transformed to lithology classes to predict reservoir properties. For the Sha sand reservoir, lithology was defined by volume of clay (Vcl), total porosity, and water saturation (Sw) for wells A, B and C outside the survey. HC sand class was defined as Vcl less than 40%, porosity more than 5% and Sw less than 50%. Wet sand class was defined as Vcl less than 40% and Sw greater than 50%. Shale was defined as Vcl greater than 40%. For the Da fractured limestone reservoir, lithology was defined as good, tight, or heavy shaly. Probability density functions (PDFs) were chosen after log rock physics analysis with different combination scenarios between rock physics attributes of AI, Vp/Vs and density. The final PDFs generated for the Sha and Da units are shown in Figure 8. Ant-tracking was employed to enhance and track small discontinuities in the seismic data and hence characterize small faults and fracture corridors. The input to the first step of variance attribute extraction was azimuth-stacked data rather than the more commonly used full-stack data. Figure 7 Acoustic impedance, Vp/Vs and density inverted from simultaneous AVO inversion for Well 1 (top) and Well 2 (bottom). Figure 8 PDFs generated using attributes of AI and Vp/Vs of log data for the Sha sandstone (left) and Da fractured limestone units (right) EAGE

7 Figure 9 Lithology classification visualization from the Sha bottom scanning upwardly in 20 m/s intervals. The lithology classification was derived from the PDF shown in Figure 8. Integrated analysis The lithology classification based on the seismic data was in close agreement with geologic knowledge provided from wells, illustrating the potential of the technique for revealing stratigraphic details and supporting more accurate reservoir characterisation. Figure 9 shows a visualization of the lithology classification over the Sha unit presented upwardly in 20 m/s intervals. The deepest image (9a) shows sheet sand zones around the Sha bottom. Moving up through the sequence reveals a series of isolated channel sands and multilevel fluvial braided channels around the central part of the block, aligned in a SW-NE direction. While sand channels increase in number and size upwardly, hydrocarbon-bearing sands become fewer in number. An explanation for this could be that the lower sand units were more likely to have been charged with hydrocarbons due to their having better connectivity with the underlying fault and fracture system that provided migration paths. Three wells named V*, D* and H* (vertical, deviated and horizontal respectively), were drilled before the study and penetrated the fluvial braided channel sand seen in Figure 9(c). These were used for blind validation purposes, because none of the information, including logs and interpretation, had been introduced into previous AVO and AVOaz inversion. Figure 10 is an example lithology classification that incorporates the three well trajectories, and is a powerful demonstration that when sand is positioned where there is higher porosity within better overlying microfacies with rich macro- and micro-fracture development, then this a good prospect to drill. Clearly, such prospects also need good well design and completions such as perforation and sand fracture to achieve high production. Well V* penetrates the hydrocarbon-bearing channel sand where its thickness is 43.9 m true vertical depth (TVD), including an approximately 5 m interval with higher porosity (up to 10%), and rich micro-fracturing interpreted from logs. Drilling measurements show the sand body to be above the dominant reverse fault and close to a fracture corridor with 120 degree strike. Well V*, which was completed with perforations into the sand layers, has delivered high production. Measurements in Well H* indicate ten potential oilbearing zones. The most promising of these is only 48 m long and 2m thick (TVD), so is too thin to be easily resolved using surface seismic techniques. More than half of the other oil-bearing zones can be detected in the seismic data, including two that are more than 10 m thick; however, the lithology classification indicates that they all have poor reservoir quality and little fracturing, which is consistent with the downhole measurements. The deviated well D* penetrates an area that the lithology classification identifies as hydrocarbon bearing and includes the two thicker zones, but these are tight formations in an area without rich fracture development. This helps to explain why the operator gave up trying to drill these zones and instead drilled down to deeper targets. Another example of blind validation was through comparison with interpreted FMI fullbore formation microimager data that was available from logging-while-drilling through the horizontal section of Well 2 in the Da3 sub-unit. The downhole measurements supported the seismic-derived multi-scale fracture characterization based on the ratio of fast-to-slow shear velocity combined with ant-tracking. This provided further confidence that the slow shear ratio information from the high-density point-received data is sufficiently reliable, and is thus valuable for guiding future drilling plans aimed at improving development efficiency in the tight carbonates. Figure 11 includes 11 sections of micro-fracture development, interpreted and labelled from B to A. These are predominantly structural, and feature NNE inclinations, steep dip (around degrees) and strike at ESE-WNW around 120 degrees. From the local ant tracking result extracted from the seismic reflection horizon, it is clear that most fracture corridors appear to have a similar ESE-WNW strike as identified from the FMI data EAGE 91

8 first break volume 32, May 2014 Figure 10 (a) Lithology classification visualization within a 20 m/s window (b) Fast/slow shear impedance mixed with the lithology classification background within 20 m/s (c) partial log geologic interpretation column of the fluvial braided multi-level channel sand in vertical well V*. (d) and (e) are zoomed parts of (a) and (b). Figure 11 Lithology classification result (left) utilizing the PDF in Figure 8; Arbitrary profile of fast/slow-shearimpedance through the trajectory of well 2 (upper middle). The bottom part beneath a lower horizon is the Da3 sub-unit; The 11 fracture sections visualized in a rectangle in the middle of the figure have high similarity to the dense distribution of the fast/slow shear impedance (middle right). Statistical parameters of the 11 fracture zones (upper right) and local ant-tracking result (right corner) EAGE

9 Conclusion Application of the processing, integration, analysis and interpretation workflow to the high-density, full-azimuth seismic dataset acquired using point receivers proved to be successful in helping to more accurately characterize tight sand and carbonate reservoirs in the central Sichuan Basin. It has enabled detailed mapping of stratigraphy, including indications of the presence of hydrocarbons and fracture density and orientation. The integrated tight reservoir and multi-scale fracture characterization solution has improved geologic understanding in the study area. In the sand reservoirs, there is clearer definition of fault and channel boundaries. In the carbonate reservoirs, the known, but previously unseen, fractures were able to be corroborated with well information and mapped. This case study shows that the application of the high-density, full-azimuth approach using point receivers can deliver considerable increases in image quality and improved reservoir and fracture description compared to conventional sparsely sampled, narrow azimuth seismic datasets. Rasmussen, K. B., Bruun, A. and Pedersen, J. m. [2004] Simultaneous Seismic Inversion. 66 th EAGE Annual Conference and Exhibition, Extended Abstracts. Takahashi, I. [2000] Quantifying Information and Uncertainty of Rock Property Estimation from Seismic Data. Ph.D. thesis, Stanford University. Vermeer, G.J.O. [2002] 3-D Seismic Survey Design. Geophysical References Series No. 12, SEG, 205. Wang, S., Li, J. Li, D. and Gong, C. [2013] The potential of tight oil resource in Jurassic Da anzhai Formation of the Gongshanmiao oil field, central Sichuan Basin. Geology in China, 60(2), Xie, J., Jian, Z., Xiaowei, W., Tang, D. and Bing, X [2004] Study of Oil Producing Mechanism for Reservoirs with Low Porosity and Permeability. Journal of Chengdu University of Technology (Science & Technology Edition,) 31(8), Xie, J. and Tang, D. [2004] Reservoir Properties Study on Sha 1 Reserve in Gong Shanmiao Oilfield in Middle Sichuan Basin, Journal of Chengdu University of Technology (Science & Technology Edition), 31(8), Zhao, H., Sima, L., Yan, Q. and Wu, X [2008] Assessment of Fracture and Method of Production Forecast of Da Anzai Reservoir. Well Logging Technology, 32(3), Acknowledgements The authors acknowledge the excellent joint efforts of many people from the Sichuan Geophysical Company, including Xiaolan Wang and Dingjin Liu. Thanks also to the large contribution from Schlumberger, particularly Dominic Lowden, Chester Hobbs, Wai Tin, Vincent Kong, Jennifer Graham, Hongjie Gong and Yanming Tong. References Greenberg, m. L. and Castagna, J. P. [1992] Shear-wave velocity estimation in porous rocks; theoretical formulation, preliminary verification and applications. Geophysical Prospecting, 40(2), Li, R., Xiao, F., Yang, J., Liang, D., Zhang, m., Li, F., Xiao, H., Lei, X., Liu, Q. and Heesom, T [2014] High-density 3D point receiver seismic acquisition and processing a case study from the Sichuan Basin, China. First Break, 32(1), Liang, D., Ran, L., Dai, D., He, Z., Ouyang, J., Liao, Q. and He, W. [2011] A re-recognition of the prospecting potential of Jurassic large-area and non-conventional oils in the central-northern Sichuan Basin. ACTA Petrol EI SINICA 32(1), Nickel, m. and Sonneland, L. [1999] Non-rigid matching of migrated timelapse seismic. 69 th SEG Annual International Meeting, Expanded Abstracts, Paddock, D., Stolte, C. Young, J., Kist, P., Zhang, L. and Durrani, J. [2008] Seismic Reservoir Characterization of a Gas Shale Utilizing Azimuthal Seismic Data Processing, Prestack Seismic Inversion, and Ant Tracking. 78 th SEG Annual International Meeting, Expanded Abstracts. Papworth, S. [2009] Stepping up land seismic. Hart s E&P, March EAGE 93