油藏描述中的地震相分析新技術(shù)PPT幻燈片

1、油藏描述中的地震相分析新技術(shù),油藏描述中的地震相分析新技術(shù),緒論 地震相分析技術(shù)在油藏描述中的作用 波形分類地震相分析技術(shù)的特點和應(yīng)用實例 -以Stratimagic軟件為例 地震相分析技術(shù)存在的問題和發(fā)展趨勢,緒論,地震相是個“古老”，寬泛的名詞，概念。60年代中期，有人開始使用，70年代初隨著地震地層學(xué)的興起，被廣泛使用。 地震相的定義，多種多樣不統(tǒng)一，本人愿意定義為： 地震信號特征的一種表征形式，并且這種表征形式所表征的信號特征可以在橫向或縱向上劃分成單元或分類。 地震信號外形，內(nèi)部結(jié)構(gòu)，振幅，相位，頻率，速度等均可以作為地震相?，F(xiàn)在一般將振幅，相位等表征地震動力學(xué)特征的信號稱為屬性，而

2、把反射外形等靜力學(xué)特征的信號定義為地震相的較多。 地震相可應(yīng)用于地震地層學(xué)，巖性地震學(xué)以及油藏描述中的儲層預(yù)測等。 本次講座主要集中在地震相的概念，以及工業(yè)界已應(yīng)用的地震相分析方法的原理，實例的介紹上。,油藏描述中的地震相分析新技術(shù),緒論 地震相分析技術(shù)在油藏描述中的作用 波形分類地震相分析技術(shù)的特點和應(yīng)用實例 -以Stratimagic軟件為例 地震相分析技術(shù)存在的問題和發(fā)展趨勢,地震相分析技術(shù)在油藏描述中的作用,油藏描述的任務(wù)： -儲層/油藏分布預(yù)測 -儲層/油藏物性的確定 解決油藏描述的地震方法： - 屬性/地震相分析方法 -井約束AI/EI反演方法 推薦的工作流程,構(gòu)造解釋,沿層選定目

3、的段,層段內(nèi)地震相分析,靶區(qū)的井約束反演,地震相和反演結(jié)果 的綜合解釋,定性到定量的地震相分析,What is Results from the Seismic Facies Analysis Technology ?,Comparison with Traditional Methods,Map of Amplitude Standard Deviation (the best Amplitude Map),Seismic Facies superimposed on the Amplitude map,EACH real trace is assigned a color accordin

4、g to which model trace it is most closely correlated.,The Seismic Facies Map,地震方法用于油藏描述的現(xiàn)狀,Attributies Analysis/地震相 地震屬性分析方法 所提取屬性種類不斷增加（20，50種，更多？） 用戶選擇屬性 缺少合適的方法對多種屬性解釋地質(zhì)意義不明確。 Well Calibration and Inversion 地震的井標(biāo)定和反演 外推估算地震信號的橫向變化通常是困難的 需要先驗的初始模型 花費和計算吞吐量仍是系統(tǒng)化工業(yè)化應(yīng)用的障礙 先驗約束往往出現(xiàn)誤差,地震相分析技術(shù)在油藏描述中的作用,

5、快速進行地震信號特征的分類，研究地震信號的變化規(guī)律 從地震信號某種/多種特征的變化規(guī)律中確定反映地質(zhì)體沉積，物性等變化的規(guī)律，從而直接進行沉積相研究，儲層預(yù)測和物性預(yù)測等。 地震相分析可以快速的為井約束AI/EI反演等確定靶區(qū)，指導(dǎo)反演結(jié)果的解釋。 新的地震相分析方法可以進一步確定地震微相，地震相的定量化等，從而進行油藏的精細(xì)描述。,Well 1,Well 2,Example - Channel Definition (1),Conventional Instantaneous / Average Amplitude Maps,Example - Channel Definition (2),

6、Seismic Facies Map of isolated channel region, using a Neural Network derived classification,Model - 12 classes,B,A,Example - Channel Definition (3),Detail of Central Channel Differences in production from wells A and B are explained Well B - central (clean) channel Well A - point bar,地震相分析技術(shù)在油藏描述中的

7、作用,快速進行地震信號特征的分類，研究地震信號的變化規(guī)律 從地震信號某種/多種特征的變化規(guī)律中確定反映地質(zhì)體沉積，物性等變化的規(guī)律，從而直接進行沉積相研究，儲層預(yù)測和物性預(yù)測等。 地震相分析可以快速的為井約束AI/EI反演等確定靶區(qū)，指導(dǎo)反演結(jié)果的解釋。 新的地震相分析方法可以進一步確定地震微相，地震相的定量化等，從而進行油藏的精細(xì)描述。,油藏描述中的地震相分析新技術(shù),緒論 地震相分析技術(shù)在油藏描述中的作用 波形分類地震相分析技術(shù)的特點和應(yīng)用實例 -以Stratimagic軟件為例 地震相分析技術(shù)存在的問題和發(fā)展趨勢,波形分類地震相分析技術(shù)的特點和實例,Attributies Analysis

8、/地震相 地震屬性分析方法 所提取屬性種類不斷增加（20，50種，更多？） 用戶選擇屬性 缺少合適的方法對多種屬性解釋地質(zhì)意義不明確。 Well Calibration and Inversion 地震的井標(biāo)定和反演 外推估算地震信號的橫向變化通常是困難的 需要先驗的初始模型 花費和計算吞吐量仍是系統(tǒng)化工業(yè)化應(yīng)用的障礙 先驗約束往往出現(xiàn)誤差,Stratimagic-地震地層解釋/地震相分析軟件,專門用于解釋巖性，地層，油藏，地質(zhì)相對比的新的地震解釋技術(shù) 源于ELF公司獲得專利的波形分類技術(shù)，由CGG-FLAGSHIP開發(fā)為軟件產(chǎn)品。 2002年P(guān)aradigm購并Flagship后，進一步與其

9、它的地震相分析技術(shù)結(jié)合，如Seisfacies, NexModel, VoxelGeo等，使其更加完整，功能強大。,Stratimagic: a unique solution Stratimagic: 獨特的解決方案波形分類地震相分析,A process : characterization based on trace shape 一種處理： 基于道形狀的特征描述 Trace shape classification represents the true heterogenity of the seismic signal 道形狀分類代表了地震信號的真實的橫向異常 A technolog

10、y: self-organizing neural networks 一項技術(shù)：自組織的神經(jīng)網(wǎng)絡(luò) An industrial shape-recognition process, robust and unaffected by noise or spurious events 一個工業(yè)化的形狀識別處理，它穩(wěn)定，不受噪音和假同相軸的影響 A method: many years of operational success applied. 一種方法：成功地應(yīng)多年 to exploration, appraisal and reservoir studiesin clastics and ca

11、rbonates for oil and gas, onshore or offshore on 5 continents, from sea-bottom to 20.000 ft. 可用于勘探評價和油藏研究，碎屑巖和碳酸巖，油或氣，陸上和海上。,The Basic Assumption is Changes in any of the physical parameters of the subsurface are always reflected in a change in shape of the seismic trace. For example change in poros

12、ity will result in a differently shaped trace. “shape” is quantified in the change of sample value from sample to sample.,What is the Seismic Facies Classification Technology Mentioned Here?,What do you see ?,Your brain is a neural network - SHAPE is used to decide how many different types of vegeta

13、ble are here.NOT color (how many peppers?) or size (how many tomatoes?).,Are these the same shape?,Now What Do You See?,What is the Seismic Facies Classification Technology Mentioned Here?,How to understand the meaning of seismic data through Facies Identification and Classification using Trace shap

14、e?,-XX% amplitude,+/-2ms,Sampling to nearest 4ms sample generates +/-2ms unbiased noise on time up to 25% biased noise on amplitude,FIXED VERTICAL SAMPLING,Reduces sampling noise Takes full advantage of propagation beyond seismic sample,TRACE RECONSTRUCTION,Trace Reconstruction: a critical step.,WHA

15、T ARE BASIC NEURAL NETWORKS?,Signal Flow: Input Output,Synapse,INPUT SEISMIC INTERVAL,OUTPUT TRACES,Dendrites,Cell Body,Synapses,Axon,Looking for seismic shape changes,Neural Network,Clustering analysis,A Neural Network looks for a suite of traces that describe the progressive changes in the seismic

16、 shape.,Looking for seismic shape changes,Neural Network ordered color changes,Clustering analysis abrupt color changes,What Do We Classify?,Whole cube? Significantly exceeds actual volume of interest (reservoir), good for early exploratory work only Attribute maps? Demands prior knowledge, can be u

17、sed to refine insight, but not to define it Problem: Which maps to use as input? Problem: Some information could be bypassed Trace shape in interval? Focused on geological volume of interest Seismic signal shape includes all attributes,Comparison of Benefits and Drawbacks,What is the Seismic Facies

18、Classification Technology Mentioned Here?,How to understand the meaning of seismic data through Facies Identification and Classification using Trace shape?,工作流程(work flow)I. Learning from the data, and only the data從地震數(shù)據(jù)中學(xué)習(xí)，且僅僅從地震數(shù)據(jù),The model traces 模型道 These synthetic traces are constructed by the

19、neural network process, using a learning set extracted from the seismic interval. No well data is used at this stage. The user has no influence on the selection of data, and there are no weighting criteria. The result is 100% repeatable. 這些合成道是用從地震層段中提取出來的由神經(jīng)網(wǎng)絡(luò)處理建造的， 這一階段不需要井?dāng)?shù)據(jù)。用戶在數(shù)據(jù)選擇方面沒有影響，沒有加權(quán)標(biāo)準(zhǔn)，

20、結(jié)果. 100% 可重復(fù)。,INPUT SEISMIC INTERVAL,OUTPUT TRACES,Synapses,Dendrites,Cell Body,Axon,What are Basic Neural Networks?,Signal Flow: Input Output Synapse,The Process,The Neural Network trains itself on the actual trace shapes within a 3D seismic interval, and constructs synthetic seismic traces that re

21、present the signal diversity over the entire defined volume,Traces are refined by an iterative process until the best correlation to the real data is obtained,The Seismic Facies Map,EACH real trace is assigned a color according to which model trace it most closely correlates to,NEURAL NETWORK PARAME

22、TERS,Number of model traces (number of colours in the output facies map),Number of iterations Rate of learning (epsilon), Continuity (sigma),Reference surfaces, interval thickness, sub-sampling parameter,OUTPUT,INPUT,PROCESSING,Classification Maps: Class Range 2 to 100,3 Classes,7 Classes,15 Classes

23、,Increasing the number of classes results in greater detail,Small number of classes identifies first order trace variability,Unlike clustering, Neural Networks do not require preconceived ideas about the number of classes,Number of Iterations: Range 1 to 100,1 Iteration,20 Iterations,50 Iterations,1

24、00 Iterations,CLASSIFICATION MAPS,The seismic facies map 地震相圖,The map 地震相圖 Each trace has been assigned the number (and color) of the model trace to which it has the best correlation. 每一道賦給它與模型道最相關(guān)的號碼和顏色。 By observing the distribution of color on this map, we can assess the distribution of seismic s

25、hapes throughout the interpreted area. 通過觀察圖上顏色的分布，我們可以評定解釋區(qū)域的地震形狀的分布。反映了巖性，地層，地質(zhì)相的變化。,Projecting facies information on seismic將相的信息投影到地震剖面上,The classification result can be projected directly above the interval on which the process was applied, allowing a one-to-one visualization of the actual data

26、 traces and their corresponding assignement to one of the classes. 分類結(jié)果可以直接投影到處理過的層段的上，允許一對一的實際數(shù)據(jù)道及其中一個相應(yīng)的賦值分類的可視化， 為地震相的變化確定其具體反射特征。 利用專門的解釋工具（Reflector Termination shares Stratimagics user interface and infrastructure Robust solution for multi-attribute classification and calibration of seismic da

27、ta, incorporating technologies and methods developed by ENI AGIP Enables effortless work on multiple versions of a seismic survey, or a set of attributes computed over time Enables a detailed description of the reservoir, resulting in better informed business decisions based on more accurate predict

28、ion of reserves Improves reservoir characterization within field development projects,A New Methodology Based on Seismic Facies Analysis and Litho-Seismic ModelingThe Elkhorn Slough Field Pilot Project Solano County California,By Manuel Poupon (Flagship Geosciences, today Paradigm) and Kostia Azbel

29、(CGG-Geoscience) Offshore, March 1999,The Elkhorn Slough Field Pilot Project Solano County California,Scope of the Project The Data: Winters Pinchout 3D The Play: Deep Water Fan/Channel Structural Interpretation Conventional Horizon Attributes Geological Horizon Attributes Stratigraphic Interpretati

30、on Conventional Interval Attribute Analysis Seismic Facies Analysis Modeling Seismic Facies Trace Revised Geological Model and Business Impacts Conclusions,Interpretation of horizons & faults,Before,After,Seismic Classification: The Missing Link?,Calibration to wells,Inversion or Interval map analys

31、is,Geostatistical analysis, modeling,Interpretation of horizons & faults,Analyze surface attributes,Seismic Facies Classification,Interval attribute analysis,Interpretation of geological shapes,Geostatistical analysis modeling,The Data: Winters Pinchout 3D,3D Survey - Solano Co., California Shot and

32、 processed by CGG-Americas in 1995. 830 in-lines, 700 cross-lines - 110 x110 bin spacing (52 sq. miles). Sample interval 2 msec - Record length 6 sec. Well Data 4 wells drilled on a turbiditic play (based on 2D interpretation). Well C: “70 Feet of netpay in the A sand”. Recoverable reservesincreased

33、 to 14-18 BCF,The Play: Deep Water Fan/Channel,Winters Sands“The Winters pinchout play is turbiditic in nature, sands being transported through channels incised into the shelf and deposited into deep water fans surrounded by shales” (K. Lanning 1998).,Refining the Channel/Fan Area,We can do two thin

34、gs: 1. Limit the area of analysis to the channel/fan only 2. Use the wellbore trace to pilot the seismic facies map,Well A: 15ft gas Well B: 45ft water Well C: 70ft gas Well D: no sand,A,B,C,D,Time map (Top of Winters sand),Mixed map (Time + Dip),Azimuth map,Structural Interpretation,Conventional Ho

35、rizon Attributes Sediment layers dip toward the South West. Dip and Azimuth maps respectively highlight the channel and fan system.,Stratigraphic Interpretation,Conventional Horizon AttributesHorizon Amplitude Map: Two different geological environments are expressed with similar high amplitude value

36、s,Bright spots,This Neural Network Technology is licensed from TotalFinaElf,Seismic Facies Analysis using NNT: What Is It?,Seismic Facies: The description and geologic interpretation of seismic reflection patterns including configurations, (continuous, sigmoidal, etc.), frequency, amplitude, and con

37、tinuity. Neural Network Technology (NNT): The ability to analyze and classify trace shapes using a discriminating process. Seismic Facies Map: This is a similarity map of actual traces to a set of model traces that represents the diversity of various trace shapes present in an interval.,Model Traces

38、,Interval of interest,Stratigraphic Interpretation,Unpiloted Regional Seismic Facies AnalysisClassifying the 60-ms interval above the reference horizon using Neural Network shape recognition. Seismic facies map shows turbidites deposited along a NNW-SSE paleo-coastline. Several channels incising the

39、 shelf can also be identified.,Stratigraphic Interpretation,Unpiloted Channel Seismic Facies Analysis Classifying the 60-msec interval below the reference horizon using Neural Network shape recognition. Seismic facies map highlights the outline of an asymmetric fan.,Seismic response at Well C,Main s

40、tream NW to SE or tilted sea bottom towards SE?,Asymmetric fan,Model Traces,15-25,70,0,Seismic Facies Map,Stratigraphic Interpretation,Piloted Seismic Facies Analysis Using the seismic response at Well C as an indicator of gas-charged sands and focusing over the channel/fan area only, piloted seismi

41、c facies map highlights the distribution of the thicker reservoir sands.,Seismic trace at Well C used as model #9,Model traces,Piloted Seismic Facies Map,Only a limited area in the channel/fan system have seismic responses similar to Well C (thick sand) Well A & B (thin sand) are out of the main fan

42、 area, Well D (shaled out) has a distinct seismic facies.,Petro-Acoustic Modeling,Modeling Seismic Facies Trace Using log traces from Well C and model trace #9, seismic response is calibrated as 70 of gas-charged sands.,?,Petro-Acoustic Modeling,Seismic Facies Calibration Using log traces at Well C

43、and D to respectively calibrate seismic response of gas-charged A sands and seismic response of a no-sand zone.,Seismic Well C,Seismic Well D,Note: Model traces are not “True Amplitude” Data,Petro-Acoustic Modeling,Petro-acoustic Modeling of Reservoir Characteristics Perturbing Seismic Response from

44、 Well C using sand thickness, reservoir porosity, and fluid content as variable parameters.,Decreased Sand Thickness,Decreased Porosity,Decreased Gas Saturation,Petro-Acoustic Modeling,Perturbation of Reservoir Characteristics Thickness variations are modeled from C to D wells (70 to 0).Synthetic tr

45、aces and seismic traces are similar,Decreased Thickness (70 to 0),Flattened seismic section,Interval of interest,W,E,Petro-Acoustic Modeling,Perturbation of Reservoir Characteristics Synthetic model traces are generated between C and D wells using a combination of reservoir thickness, water saturati

46、on and porosity. Synthetics are then used to pilot the seismic facies analysis.,Synthetic model traces,Seismic Facies Map,Post-Mortem Analysis of Well D,Revised Geological Model Synthetic Classifying the 82-msec interval below the “despiked” reference horizon using 20 model traces. This new unpilote

47、d seismic facies map highlights the outline of a late shale plug fan that could explain the absence of A sand in the D well.,Seismic Facies Map,Model traces,Business Impacts,Cost to Expose Pay Drilling program is directly related to the geological model Maximize Production RateIdentify the sweet spo

48、ts High-grade prospects,Conclusions,Exploration within the Elkhorn Slough field had been mostly driven by amplitude anomalies, inversion techniques and coherency technology.Seismic Facies Analysis combined with litho-seismic modeling of well data was applied to the Elkhorn Slough Field. This methodo

49、logy is accurate, cost-effective, quick and often reveals subtle geological features only expressed in the shape of the seismic trace. The geological model was tested with the E well which found 100 of gas sand.,Seismic facies map (present work),Coherency slice (K. Lanning 1998),Turbidite Characteri

50、zation Using Multi-Attribute Volume Classification,Data Offshore Angola,3D Survey Offshore Angola, Africa650 in-lines, 650 cross-lines 6.25X6.25 bin spacing Sample interval 2 msec - Volume used from 2500-2900ms Generated Attributes Amplitude, Dip, Azimuth, AI, Porosity and Semblance Well Data2 wells

51、 drilled on a turbidite play Well 2 good producer from massive sands Well 4 bad producer from poorly sorted sands,Part Two: Volume Classification,The Data: Offshore Angola, Post Stack 3D data Structural Setting: Extensional Faulting Depositional Setting: Turbidite Slope Channels Stratigraphic Interp

52、retation Classic Seismic Trace Shape Analysis Attribute Map Classification Multi-Attribute Volume Visualization Multi-Attribute Volume Classification Subvolume Detection in 3D environment Conclusions,Structural Setting,Top System (blue) Top Sequence A (blue) Top Channel A (violet) Intra Channel A (y

53、ellow) Base Channel A (red) Erosion Base Sequence A (green),Wells with GR Log,Well 2 is a good producer in both upper and lower A Unit. Well 4 is a poor producer from upper A unit and shows a low GR response for the second unit of corresponding massive sands in Well 2.,Inline 2258 Well 4,Inline 2120

54、 Well 2,Stratigraphic Interpretation Workflow,Interval Attribute Maps,Interval Attributes between Top Channel A and Intra Channel A Interval Isopach Third Derivative Fourth Derivative Amplitude Peaks Peak-Trough Ratio Frequency Peaks Amplitude Positive Polarity,Map Classification with NNT 14C,Major

55、Channel System (yellow and red classes) with inner Channel (blue classes). Well 4 (poorly sorted sands) is located on blue classes and Well 2 (massive sands) on yellow classes. Axis of eroding channel is clearly defined.,Trace Shape Analysis on Upper Unit,Trace shape analysis on a non-constant inter

56、val between Top Channel A and Intra Channel A,Three major families of traces , facies,Principal Component Analysis - Why?,Why do we want to perform PCA ? To analyse data redundancy and bring several individual attribute volumes down to fewer PCA volumes (Eigenvalues greater than 1) for further analy

57、sis and classification To understand which attributes contribute the most in describing the trend in a data set To get a better understanding of data/attribute dependencies and correlations To eliminate noise,What Attributes to Classify ?,A possibility would be to classify PCA volumes, and here we h

58、ave a cut off at Component 3.We have reduced the amount of data from 8 to 3 volumes to be handled.,AI and Azimuth have low correlation value and it will thus be interesting to classify them together,Attribute Volumes, AI,Crossline 3858, Well 2,Well 4 Random Line Well 2,Acoustic Impedance Scale Bar,A

59、ttribute Volumes, Azimuth,Horizon Slice +4 From Top Channel A,Crossline 3858, Well 2,Azimuth Scale Bar,-180,+180,Manual Attribute Classification,CLASSIFICATION,DATA COMPRESSION BY ZONATION (OPTIONAL),PRINCIPAL COMPONENT ANALYSIS (OPTIONAL),AI and Azimuth Crossplot,Analysis in Vanguard shows two major cut-offs for acoustic impedance values, corresponding to clean massive sands and intermediate sands and shales,Acoustic