Direct extraction of the fluid factor based on variable point-constraint
-
摘要: 以Gassmann流体因子(Gassmann Fluid Item, GFI)为目标, 提出了一种流体因子直接提取的新方法.首先, 以贝叶斯反演框架为基础, 将似然函数、先验信息以及Gassmann流体因子近似方程相结合, 得到初始的目标函数;其次, 进一步在初始目标函数中加入可变数量的点约束信息, 并得到最终的目标函数;最后, 通过求解该目标函数, 就直接提取出了Gassmann流体因子.该方法的主要特点是不需要初始模型的参与, 而是通过一个约束模型来控制提取结果的稳定性和准确性, 并且可以从约束模型中选定不同数量的约束点进行约束, 称为可变点约束.给出并讨论了三种常用的不同点约束模式和原则, 并用模型说明了它们不同的约束效果.模型验证和实际应用结果皆以表明, 该方法即使在叠前数据信噪比很低的情况下也能较好地提取出Gassmann流体因子, 流体因子提取结果客观性高、稳定性好, 并且能够与已知的流体解释结果很好地匹配, 益于进一步推广应用.Abstract: Fluid factor extraction plays an increasing important role in fluid discrimination. The conventional way of such extraction through prestack inversion is to calculate fluid factors indirectly from P-wave velocity, S-wave velocity and density data which can be derived from inversion of seismic data. However, this method has two disadvantages. One is that the density data imbedded in fluid factors is more contaminated by noise than the inverted P-wave and S-wave reflectivity even with large incident angles. The other is that the indirect way of fluid factor estimation can create more uncertainties caused by the indirect calculation. This article focuses on the direct extraction of Gassmann fluid item (GFI), which is the real factor that reflects the influence of fluid in porous rock as Russell et al. discussed. The objective is to improve accuracy and stability of fluid factor extraction compared with the conventional way.#br#A novel method for direct extraction of fluid factors, named variable point-constraint fluid factor direct extraction (VPC-FFDE), is developed that uses variable point-constraint strategy to extract GFI the Gassmann fluid item from prestack data directly. The initial objective function is build combining likelihood function, priori information and GFI approximate equation. The final objective function is yielded by adding a variable number of constraint points to the initial objective function. Three different point-constraint patterns are examined, and different constraint effects are illustrated using synthetic data. Instead of the initial model, this method uses a constraint model to improve the accuracy and stability of the extraction results. The core of the proposed approach is to control the extraction results by adding a variable number of constraint points into the extraction process. Either accurate constraint points or the extremely low frequency model can be used, and different numbers of constraint points can be chosen during the constraint process. It does not need to obtain P-wave velocity, S-wave velocity and density first, and therefore can avoid accumulation of errors that often appear with indirect approach.#br#We applied the proposed method to the Chengdao area of the Shengli Oilfield, Sinopec. The area is about 150 km2. We chose prestack angle stacks in this case. Neither the structural high nor the bright spot is unambiguous for the prediction of gas or oil sands in this area, and the SNR is also a little low. We used a constant value constraint model to constrain the extraction process and pattern 3 was chosen in this example. As can be seen from the extraction results, the direct GFI extraction profile in the three layers are all characterized by low values, but the GFI value of the oil layer is relatively lower compared to the water layer, which has already been verified in the fluid substitution model in the previous section. This result gives a clear indication of the lateral extent and vertical extent of the oil layer and water layer. The extraction results are consistent with current oil production and joint interpretation results with only well information. However, the indirect GFI extraction result is somewhat more ambiguous, and the resolution is also lower. The actual application results show that compared with the indirect GFI extraction results, the direct ones have higher resolution and accuracy, and can match the well logging interpretation results perfectly. We implemented the procedure to distinguish different fluids. The proposed method is accurate and reliable. We implemented the purpose of more accurate fluid discrimination through fluid factor direct extraction.#br#We proposed a novel approach to extract GFI directly based on GFI linearized approximation, Bayesian inversion framework and variable points-constrain strategy. The likelihood function and priori information contribute to the high extraction resolution. The strategy of variable points-constraint renders the extraction process more stable and not sensitive to the constraint model. Model validation and actual application results show that the proposed method can produce good application effects even if the SNR of prestack data is low, and therefore is beneficial for further popularization and application.
-
Key words:
- Fluid Factor /
- Prestack Inversion /
- Variable Point Constraint /
- Direct Extraction /
- Objectivity /
- Stability
-
-
[1] Bosch M, Mukerji T, González E F. 2010. Seismic inversion for reservoir properties combining statistical rock physics and geostatistics: A review. Geophysics, 75(5): A165-A176.
[2] Buland A, Omre H. 2003. Bayesian linearized AVO inversion. Geophysics, 68(1): 185-198.
[3] Chi X G, Han D H. 2006. Fluid property discrimination by AVO inversion. // 76th Annual Meeting, SEG Expanded Abstracts, 2052-2056.
[4] Connolly P. 1999. Elastic impedance. The Leading Edge, 18(4): 438-452.
[5] Downton J E. 2005. Seismic parameter estimation from AVO inversion[Ph. D. thesis]. Calgary: University of Calgary.
[6] Fatti J L, Smith G C, Vail P J, et al. 1994. Detection of gas in sandstone reservoirs using AVO analysis: A 3-D seismic case history using the Geostack technique. Geophysics, 59(9): 1362-1376.
[7] Goodway B, Chen T, Downton J. 1997. Improved AVO fluid detection and lithology discrimination using Lamé petrophysical parameters. // 67th Annual meeting, SEG, Expanded abstracts, 183-186.
[8] Gray D. 2002. Elastic inversion for Lame parameters. 72th Annual Meeting, SEG, Expanded Abstracts, 213-216.
[9] Haas A, Dubrule O. 1994. Geostatistical inversion—A sequential method of stochastic reservoir modeling constrained by seismic data. First Break, 13(12): 561-569.
[10] Hampson D. 1991. AVO inversion, theory and practice. The Leading Edge, 10: 39-42.
[11] Mora P. 1987. Nonlinear two-dimensional elastic inversion of multioffset seismic data. Geophysics, 52(9): 1211-1228.
[12] Ning Z H, He Z H, Huang D J. 2006. High sensitive fluid identification based on seismic data. Geophysical Prospecting for Petroleum, 45(3): 239-242.
[13] Quakenbush M, Shang B, Tuttle C. 2006. Poisson impedance. The Leading Edge, 25(2): 128-138.
[14] Russell B H, Gray D, Hampson D P. 2011. Linearized AVO and poroelasticity. Geophysics, 76(3): C19-C29.
[15] Russell B H, Hedlin K, Hilterman F J, et al. 2003. Fluid-property discrimination with AVO: A Biot-Gassmann perspective. Geophysics, 68(1): 29-39.
[16] Shi Y M, Zhao W Z, Cao H. 2007. Nonlinear process control of wave-equation inversion and its application in the detection of gas. Geophysics, 72(1): R9-R18.
[17] Simmons J L, Backus M M. 1996. Waveform-based AVO inversion and AVO prediction error. Geophysics, 61(6): 1575-1588.
[18] Smith G C, Gidlow P M. 1987. Weighted stacking for rock property estimation and detection of gas. Geophysical Prospecting, 35(9): 993-1014.
[19] Smith G C, Gildow P M. 2000. A comparison of the fluid factor with λ and μ in AVO analysis. // 70th Annual Meeting, SEG, Expanded Abstracts, 1940-1945.
[20] Tarantola A. 2005. Inverse Problem Theory and Methods for Model Parameter Estimation. Philadelphia: Society for Industrial and Applied Mathematics.
[21] Ulrych T J, Sacchi M D, Woodbury A. 2001. A Bayes tour of inversion: a tutorial. Geophysics, 66(1): 55-69.
[22] Wang B L, Yin X Y, Zhang F C. 2005. Elastic impedance inversion and its application. Process in Geophysics (in Chinese), 20(1): 89-92.
[23] Whitcombe D N. 2002. Elastic impedance normalization. Geophysics, 67(1): 60-62.
[24] Yang P J. 2008. Seismic wavelet blind extraction and non-linear inversion[Ph. D. thesis] (in Chinese). Dongying: China University of Petroleum.
[25] Yang P J, Yin X Y. 2008. Non-linear quadratic programming bayesian prestack inversion. Chinese J. Geophys. (in Chinese), 51(6): 1876-1882, doi: 10.3321/j.issn:0001-5733.2008.06.030.
[26] Yin X Y, Yang P J. 2008. A Novel Prestack AVO Inversion and Application. // 78th Annual Meeting, SEG, Expanded Abstracts, 2041-2044.
[27] Yin X Y, Zhang S X, Zhang F. 2013a. Delicate construction of fluid factor and its application based on two-phase media theory. Process in Geophysics (in Chinese), 28(6): 2911-2918, doi: 10.6038/pg20130611.
[28] Yin X Y, Zhang S X, Zhang F. 2013b. Two-term elastic impedance inversion and Russell fluid factor direct estimation method for deep reservoir fluid identification. Chinese J. Geophys. (in Chinese), 56(7): 2378-2390, doi: 10.6038/cjg20130724.
[29] Zhang S X. 2012. Methodology and application of fluid identification with seismic information[Ph. D. thesis] (in Chinese). Dongying: China University of Petroleum.
[30] Zheng J J, Yin X Y, Zhang G Z. 2011. Fluid factor analysis and the construction of the new fluid factor. Process in Geophysics (in Chinese), 26(2): 579-587, doi: 10.3969/j.issn.1004-2903.2011.02.024.
[31] Zong Z Y, Yin X Y, Wu G C. 2012. Fluid identification method based on compressional and shear modulus direct inversion. Chinese J. Geophys. (in Chinese), 55(1): 284-292, doi: 10.6038/j.issn.0001-5733.2012.01.028.
[32] 宁忠华, 贺振华, 黄德济. 2006. 基于地震资料的高灵敏度流体识别因子. 石油物探, 45(3): 239-242.
[33] 王保丽, 印兴耀, 张繁昌. 2005. 弹性阻抗反演及应用研究. 地球物理学进展, 20(1): 89-92.
[34] 杨培杰. 2008. 地震子波盲提取与非线性反演[博士论文]. 东营: 中国石油大学.
[35] 杨培杰, 印兴耀. 2008. 非线性二次规划贝叶斯叠前反演. 地球物 理学报, 51(6): 1876-1882, doi: 10.3321/j.issn:0001-5733.2008.06.030.
[36] 印兴耀, 张世鑫, 张峰. 2013a. 双相介质理论指导下的流体因子精细构建与应用. 地球物理学进展, 28(6): 2911-2918, doi: 10.6038/pg20130611.
[37] 印兴耀, 张世鑫, 张峰. 2013b. 针对深层流体识别的两项弹性阻抗反演与Russell流体因子直接估算方法研究. 地球物理学报, 56(7): 2378-2390, doi: 10.6038/cjg20130724.
[38] 张世鑫. 2012. 基于地震信息的流体识别方法研究与应用[博士论文]. 东营: 中国石油大学.
[39] 郑静静, 印兴耀, 张广智. 2011. 流体因子关系分析以及新流体因子的构建. 地球物理学进展, 26(2): 579-587, doi: 10.3969/j.issn.1004-2903.2011.02.024.
[40] 宗兆云, 印兴耀, 吴国忱. 2012. 基于叠前地震纵横波模量直接反演的流体检测方法. 地球物理学报, 55(1): 284-292, doi: 10.6038/j.issn.0001-5733.2012.01.028.
-
计量
- 文章访问数: 2052
- PDF下载数: 1816
- 施引文献: 0
下载: