Nonlinear AVA Inversion Based on a Novel Quadratic Approximation for Fluid Discrimination
Fluid discrimination plays an important role in reservoir exploration and development. At present, the fluid factors used for fluid discrimination are estimated by linear AVA inversion methods based on the linear approximations of the Zoeppritz equations. However, the Zoeppritz equations show that t...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Geofluids |
| Online Access: | http://dx.doi.org/10.1155/2020/8860119 |
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| _version_ | 1849690385326014464 |
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| author | Lin Zhou Xingye Liu Tianchun Yang Jianping Liao Mingfeng Zhu Gan Zhang |
| author_facet | Lin Zhou Xingye Liu Tianchun Yang Jianping Liao Mingfeng Zhu Gan Zhang |
| author_sort | Lin Zhou |
| collection | DOAJ |
| description | Fluid discrimination plays an important role in reservoir exploration and development. At present, the fluid factors used for fluid discrimination are estimated by linear AVA inversion methods based on the linear approximations of the Zoeppritz equations. However, the Zoeppritz equations show that the relationship between prestack AVA reflection coefficients and reservoir parameters is highly nonlinear. Therefore, inversion methods based on linear approximations will seriously influence the nonuniqueness and uncertainty of inversion results. In this paper, a nonlinear inversion based on the quadratic approximation is carried out to reduce the nonuniqueness and uncertainty of fluid factor. Firstly, in order to directly invert the fluid factor, a novel quadratic approximation in terms of the fluid factor (ρf), shear modulus, and density on both sides of the reflection interface is derived based on poroelasticity theory. Then, a nonlinear inversion objective function is constructed using the novel quadratic approximation in a Bayesian framework, and the Gauss-Newton method is adopted to minimize this objective function. The synthetic data example shows that the new method can obtain reasonable fluid factor inversion results even in low SNR (signal-to-noise ratio) case. Finally, the proposed method is also applied to field data which shows that it can effectively discriminate reservoir fluids. |
| format | Article |
| id | doaj-art-efa3b7518ff04e75aee9ccd8eaa4f7c6 |
| institution | DOAJ |
| issn | 1468-8115 1468-8123 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Geofluids |
| spelling | doaj-art-efa3b7518ff04e75aee9ccd8eaa4f7c62025-08-20T03:21:19ZengWileyGeofluids1468-81151468-81232020-01-01202010.1155/2020/88601198860119Nonlinear AVA Inversion Based on a Novel Quadratic Approximation for Fluid DiscriminationLin Zhou0Xingye Liu1Tianchun Yang2Jianping Liao3Mingfeng Zhu4Gan Zhang5Hunan Provincial Key Laboratory of Shale Gas Resource Utilization, Hunan University of Science and Technology, Xiangtan 411201, ChinaCollege of Geology and Environment, Xi’an University of Science and Technology, Xi’an 710054, ChinaHunan Provincial Key Laboratory of Shale Gas Resource Utilization, Hunan University of Science and Technology, Xiangtan 411201, ChinaHunan Provincial Key Laboratory of Shale Gas Resource Utilization, Hunan University of Science and Technology, Xiangtan 411201, ChinaPetroChina Pipeline Compressor-Set Maintenance, Repair&Overhaul Center, Langfang 065000, ChinaSichuan Water Resources and Hydroelectric Investigation & Design Institute, Chengdu 610072, ChinaFluid discrimination plays an important role in reservoir exploration and development. At present, the fluid factors used for fluid discrimination are estimated by linear AVA inversion methods based on the linear approximations of the Zoeppritz equations. However, the Zoeppritz equations show that the relationship between prestack AVA reflection coefficients and reservoir parameters is highly nonlinear. Therefore, inversion methods based on linear approximations will seriously influence the nonuniqueness and uncertainty of inversion results. In this paper, a nonlinear inversion based on the quadratic approximation is carried out to reduce the nonuniqueness and uncertainty of fluid factor. Firstly, in order to directly invert the fluid factor, a novel quadratic approximation in terms of the fluid factor (ρf), shear modulus, and density on both sides of the reflection interface is derived based on poroelasticity theory. Then, a nonlinear inversion objective function is constructed using the novel quadratic approximation in a Bayesian framework, and the Gauss-Newton method is adopted to minimize this objective function. The synthetic data example shows that the new method can obtain reasonable fluid factor inversion results even in low SNR (signal-to-noise ratio) case. Finally, the proposed method is also applied to field data which shows that it can effectively discriminate reservoir fluids.http://dx.doi.org/10.1155/2020/8860119 |
| spellingShingle | Lin Zhou Xingye Liu Tianchun Yang Jianping Liao Mingfeng Zhu Gan Zhang Nonlinear AVA Inversion Based on a Novel Quadratic Approximation for Fluid Discrimination Geofluids |
| title | Nonlinear AVA Inversion Based on a Novel Quadratic Approximation for Fluid Discrimination |
| title_full | Nonlinear AVA Inversion Based on a Novel Quadratic Approximation for Fluid Discrimination |
| title_fullStr | Nonlinear AVA Inversion Based on a Novel Quadratic Approximation for Fluid Discrimination |
| title_full_unstemmed | Nonlinear AVA Inversion Based on a Novel Quadratic Approximation for Fluid Discrimination |
| title_short | Nonlinear AVA Inversion Based on a Novel Quadratic Approximation for Fluid Discrimination |
| title_sort | nonlinear ava inversion based on a novel quadratic approximation for fluid discrimination |
| url | http://dx.doi.org/10.1155/2020/8860119 |
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