台灣西南近海Formosa Ridge天然氣水合物和游離氣岩石物理參數估算

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安蒂安(Dipika Anggun Ardianti) 查詢紙本館藏

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論文名稱

台灣西南近海Formosa Ridge天然氣水合物和游離氣岩石物理參數估算
(Rock Physics Parameters Estimations of Gas-hydrate and Free Gas in Formosa Ridge, Offshore SW Taiwan)

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摘要(中)

岩石物理參數通常可以通過基於測井的分析方法使用各種建議的模型和經驗方程來估計。沒有測井數據，我們能否從地震數據中估計岩石物理參數？我們提出了一種策略，將地震數據視為偽測井，然後結合疊後和疊前建模以及岩石物理研究的反演工作。該方法有助於在台灣西南部的 Formosa Ridge Offshore 確定存在流體的天然氣水合物和游離氣及其宿主岩性。我們開發了四步工作流程。首先，我們通過外觀分析改進了傳統 NMO 堆棧建議的速度模型。這項工作估計了真實速度的一維低分辨率特徵，作為時間和堆棧數據偏移的函數。其次，在疊後和疊前過程中分別使用卷積模型方法和反射率方法導出的初始阻抗模型。第三，依次實施疊後和疊前反演以提取最佳可解析模型。疊後和疊前正向模擬產生疊後和疊前數據集，用於通過相應的數據集進行質量檢查。合成炮點和 CMP 道集分別通過反射率方法和卷積模型方法創建。反射率理論利用偏移和角度相關信息來生成合成道集。如果合成和真實道集在偏移域和角度域中都適合，則確認估計參數。反演涵蓋了疊後和疊前方法，目的是估計 P 和 S 阻抗模型，並從偏移依賴數據集。同時疊前反演基於三個假設 (a) 反射率的線性近似，(b) 角度相關的 Fatti 方程（簡化的 Aki-Richard 方程），具有 30 度限制和 P-、S-阻抗和密度之間的線性關係(Hampson, Russell, & Bankhead, 2005) 被用來約束數據擬合程序。將反演工作與前向模擬的約束相結合，以分配良好的初始猜測模型並尋求最佳估計反演解。推斷的基本參數包括 Vp、Vs 和密度是所涉及的關鍵工作，用於進一步的岩石物理參數估計。通過測井分析技術建立的經驗方程和模型，可以獲得用於了解岩性條件的速度、密度、孔隙度、體積模量、剪切模量、電阻率和含水飽和度等參數。結果可以幫助我們通過交會圖評估導出參數之間的相互關係，並劃定潛在的天然氣水合物和游離氣富集區。所提出的方法使我們能夠獲得岩石物理特性，並希望將來能夠從鑽孔數據中獲得更多的可行性評估和確認。

摘要(英)

Rock physics parameters usually can be estimated through well log-based analysis approaches with various proposed models and empirical equations. Without well-log data, can we estimate petro-physical parameters from seismic data along? We propose a strategy which treat seismic data as a pseudo-log then combine post-stack and pre-stack modeling and inversion efforts for rock physics study. The approach helps to identify the gas hydrate and free gas and its host lithology with fluids existence in Formosa Ridge Offshore, Southwestern Taiwan. We developed four steps workflow. First, we refined the velocity model suggested from conventional NMO stack with semblance analysis. The effort estimates the 1D low resolution feature of the true velocities as function of time and offset from stack data. Second, initial impedance model derived from convolutional model approach and reflectivity method are used separately in post- and pre-stack procedure. Third, post- and pre-stack inversion were implemented sequentially to extract the best resolvable models. Both post- and pre-stack forward simulations produce post- and pre-stack dataset are used for quality check with the corresponding data gathers. The synthetic shot and CMP gathers are created by reflectivity method and convolutional model approach respectively. The reflectivity theory utilizes offset- and angle-dependent information for generating the synthetic gathers. If the synthetic and the real gather are fit in both offset and angle domains, then confirms the estimated parameters. Inversion covers both post- and pre-stack approaches with the purpose to estimate P- and S-impedance models and also extract the best estimated source wavelet from offset-dependent dataset. Simultaneous pre-stack inversion which based on three assumptions (a) linearized approximation for reflectivity, (b) Angle-dependent Fatti’s equation (a simplified Aki-Richard equation) with 30 degree limitation and linear relationship among P-, S-impedance and density (Hampson, Russell, & Bankhead, 2005) were used to constrain data fitting procedure. Combine inversion efforts with constraints from forward simulation for allocating good initial guessed model and seeking best-estimate inverted solution. The inferred basic parameters including Vp, Vs and density are the key efforts involved which were used for further rock physic parameters estimations. The parameters including velocity, density, porosity, bulk modulus, shear modulus, resistivity, and water saturation for understanding the lithology conditions can be obtained through empirical equations and models established by well-log analysis technique. The results can assist us to evaluate the interrelationships among the derived parameters through cross-plots and delineate the potential gas hydrate and free gas concentration zones. The proposed approach enables us to obtain petro-physical properties with the hope that additional feasibility evaluation and confirmation from borehole data will be available soon in the future.

關鍵字(中)

★ 地震反演
★ 疊後
★ 疊前
★ 岩石物理學
★ 天然氣水合物
★ 游離氣
★ 偽測井

關鍵字(英)

★ Seismic Inversion
★ Post-stack
★ Pre-stack
★ Rock Physics
★ Gas-hydrate
★ Free gas
★ Pseudo-logs

論文目次

Chapter1 ……………………………………………………………………………….…..…..1
1.1 Introduction……………………………………………………………………………..….1
1.1.1 Bottom-Simulating Reflectors (BSRs)…………………………………………………...1
1.1.2 Gas Hydrate Prospect……………………………………………………………….........2
1.1.3 Geological Setting and Tectonic Evolution of Study Area…………… ……………........6
1.1.4 Gas Hydrate Investigation in Formosa Ridge, Offshore SW Taiwan………………........7
1.2 Objectives of study and outline of this thesis…………………………………………......10
References……………………………………………………………………………...……..13
Chapter 2……………………………………………………………………………………...19
Seismic Attributes……………………………………………………………………….........19
2.1 The Complex Trace and Hilbert Transform………………………………………………19
2.2 Attributes Analysis for Resrvoir Characterization in the Study Area……………….........21
2.2.1 Instantaneous Amplitude (IA, Envelopre)………………………………………………22
2.2.2 Instantaneous Phase………………………………………………………………..........23
2.2.3 Instantaneous Frequency (IF)……………………………………………………….......24
2.2.4 Relative Acoustic Impedance (RAI)……………………………………………...…….25
2.2.5 Sweetness……………………………………………………………………………….26
2.2.6 Variance ………… ………………………………………………………………….......27
2.2.7 Chaos……………………………………………………………………………………28
References…………………………………………………………………………………….41
Chapter 3…………………………….………………………………………………………..43
Synthetic Data, Post-Stack Modeling and Post-Stack Acoustic Inversion……………………43
3.1 Data Description…………………………………………………………………………..43
3.1.1 Post-Stack and Pre-Stack Seismic Data…………………………………………………44
3.2 Research Instruments………………………………………………………………….......45
3.3 Post-stack Synthetic Seismogram Simulation……………………………………………..45
3.3.1 Convolutional Model, Wavelet and Polarity…………………………………………….45
3.3.2 Forward Modeling for Flat Hydrate Layers Velocity Model………………………..…...48
3.3.3 Forward Modeling of Reservoir Anticline Velocity Model… ……………………….….49
3.3.4 Tuning Effects for Pinch-out (Wedge) Model…………………………………………...50vi
3.3.5 Pseudo-log and Synthetic Seismogram Generation ……………………………………..60
3.4 Post-Stack Modeling Approaches…………………………………………………….…...61
3.5 Post-Stack Seismic Inversion…………………………………………………………..….65
3.5.1 Model-Based Inversion (MBI) Procedure – Advantages and Disadvantages………..…..65
3.5.2 Theory of Model-Based Inversion…………………………………………………..…..66
3.6 The Application of Acoustic Impedance Post-Stack Constrained Inversion………………68
3.7 Stratigraphic, Structural and Lithological Properties Interpretation……………………....72
References…………………………………………………………………………………….80
Chapter 4…………………………………………………………………………………….134
Offset and Angle Domain Pre-Stack Elastic Modeling and Inversion……………………….134
4.1 Workflow of Pre-stack Modeling, Velocity Model Refinement and NMO Correction…134
4.2 Pre-stack Seismic Interpretation…………………………………………………………136
4.3 Forward AVO Modeling of Synthetic Gather via Aki-Richards Approximation………...136
4.3.1 S-Wave Velocity Estimation from Greenberg-Castgana Vp-Vs relations……………..138
4.3.2. Forward AVO Synthetic Gather Analysis……………………………………………..140
4.4 Assumptions and Workflow of Pre-stack Simultaneous Inversion and Theory………….142
4.5 Data Pre-Conditioning by Converting Offset CMP Gather to Super and Angle Gather….145
4.6 Simultaneous Pre-stack Matrix Inversion Theory………………………………………..149
4.7 Inversion Results with and without Constraints………………………………………….156
4.8 Concluding Remarks………………………………………………………………….....161
References……………………………………………………………………………..…….163
Chapter 5 ……………………………………………………………………………….……201
Rock-Physics Estimations, Quantified Geological Processes and Geophysical
Observations…………………………………………………………………………………201
Quantitative Seismic Interpretation …………………………………………………………201
Rock Physics Models, Sand/Shale Compaction and Microstructure Interpretation …… ..…..202
Rock Physics Templates (RPTs) ………………………………………………………….…203
Inferred Rock Physics Properties from Pre-stack Inversion Data …………………………....204
Faust’s Relation ……………………………………………………………………………..206
Derive Porosity from Density ……………………………………………………………… .210
Pseudo-logs at Five Controls CMPs …………………………………………………………212
Fluid, Gas and Lithology Discrimination ……………………………………………………212vii
Potential Extension Approaches: Machine Learning and Multidiciplinary Studies …………214
Machine Learning with Supervised Clustering Analysis …………………………………....215
Potential Linkage with Micro-gravity Survey, Modeling and Inversion of Gravity Data … …215
References……………………………………………………………………………..…….216
Chapter 6
Discussions and Conclusions …………………………………………….………………….234
AVO Results and Classification …………………………………………..…………………234
Vp, Density and Vs Model Estimation ……………………………………..………………..236
Cross-Plot …………………………………………………………………………………...240
References……………………………………………………………………………..…….241

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指導教授

陳浩維(How-Wei Chen)

審核日期

2021-10-28

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