SEISMIC RESERVOIR
CHARACTERIZATION
Prepared by
\
Ramy mohammed Fahmy Abo-Alhassan
Supervisor professor
\
Ahmed Sayed Abu El-Ata
Geophysics Department
Reservoir Characterization
A model of a reservoir that incorporates all the characteristics of the reservoir those are pertinent to its ability to store hydrocarbons and also to produce them. Reservoir characterization models are used to simulate the behavior of the fluids within the reservoir under different sets of circumstances and to find the optimal production techniques that will maximize the production.
Seismic Reservoir Characterization
Seismic data, in particular 3-D seismic data, are a mainstay of the petroleum industry. These geophysical data provide subsurface images and other information that may be used by geophysicists, geologists and engineers alike to identify and effectively drain hydrocarbon reservoirs. Seismic data should not be interpreted in a stand-alone fashion.
Geological, geophysical and engineering expertise and data need to be included in a “complete” interpretation. This report will emphasize the use of seismic data for reservoir evaluation. The objectives are to provide a basic tool set and workflow for interpretation.
The primary tool for probing the entire reservoir is 3-D reflection seismic surveying. While 3-D seismic data cover the entire reservoir volume. Well logs and core measurements usually are made on the scale of 0.1-1.0 m. Although the scale of resolution is quite fine, the volume of investigation is small, confined to within a few metres of the borehole. To fill in the “missing wavelengths” between borehole data and surface seismic data, we can acquire borehole seismic data. Such data, gathered by using seismic sources and receivers to fill the wavelength gap, would include vertical seismic profiling and cross-borehole seismic surveys. The key is to characterize the reservoir’s physical properties by integrating the various data sets with different wavelengths.
Acknowledgment
First and foremost, I would like to thank ALLAH for all that I have been given. I thank my father, my mother and all my family for keeping me going through all of those stressful times with their motivation.
Lot of thanks to my friends’ with a great appreciates to Mr. Emad Eid Fahmy.
A special thank you with a special appreciates to
Professor. Dr\ Ahmed Sayed Abu El-Ata and Professor .Dr\ Abdel Moktader.
TABLE OF CONTENTS
SECTION PAGE
List of figures...
IXIntroduction...
XII1.0 Amplitude variations with offset (AVO)
1.1 Seismic Energy partitioning at boundaries………1 1.2 AVO Model- Zoeppritz Equations……….…..….. 1.3 Mechanics of quantitative AVO analysis………...…… 1.4 Example quantitative AVO analysis……….. 1.5 Fatti Methodology………... 1.6 Lambda/Mu-Rho……… 1.7 AVO to Carbonate Reservoirs...…...10
2.0 Seismic Attributes
2.1 Seismic Attributes……….…11
2.1.1 Some about Seismic Attributes……….. 2.1.2 The Classification of Attributes………. 2.1.3 Some Basic Attributes and Its Characteristics………..…….
2.2 Quantitative Use of Seismic Attributes Reservoir
Characterization………15
2.2.1 Does the Data Warrant the Use of Attributes? 2.2.2 What Seismic Measures and What We Require for
Reservoir Characterization………. 2.2.3 Seismic Attributes - Property Predictors or False? ... 2.2.4 Predict porosity using seismic acoustic impedance…...….. 2.2.5 What about using Linear Regression? ………....…… 2.3 Enhancing Fault Visibility Using Bump Mapped Seismic
Attribute
2.3.1 Shaded relief of the coherency……….18 2.3.2 Bump Mapping Attributes……… 2.3.3Bump Mapping Coherency………..….20
3.0 Reservoir Characterization
3.1 pore-pressure prediction………...………….21
3.1.1 Methodology………...….21 High-resolution velocity analysis (step 1)……….…....…. Pore-pressure and effective-stress prediction (step 2)…...…. Multi-attribute seismic inversion (step 3)………...….. Lithofacies classification using σeff, IP, and IS (step 4)…...
3.2 Reservoir Characterization and Heavy Oil Production
3.2.1 Methodology………... 3.2.2 Results………...…...30
4.0 Time-Lapse
4.1 Reservoir Surveillance...31
4.1.1 The concept………...,,...………. 4.1.2 The Physical Basis………...,...…… 4.1.3 Seismic Repeatability………..……...…… 4.1.4 4-D Interpretation………...…..
4.2 Detecting flow-barriers with 4D seismic
4.2.1 Fault analysis………...………...35 4.2.2 Combining 4D seismic and reservoir simulation…….……. 4.2.3 Fault sealing analysis………...… 4.2.4 (4-D) fluid saturation mapping
techniques……….……
405.0 Seismic Inversion
5.1 The Post-Stack Inversion Method………..……….41 5.2 The Details - Constraints, QC, Annealing, Global and
Colored Inversion……….……...………. 5.3 AVO Inversion………..……….. 5.4 Geostatistical Inversion………...………. 5.3 Merging Technologies ...……....………….. 5.4 Stochastic Seismic Inversion………...50
6.0 Application
to Reservoir characterization by combining time-lapse seismic analysis with reservoir simulation
6.1 Introduction……….………...……51 6.2 Reservoir simulation………...…….…..….
6.3 Synthetic seismic section………...….. 6.4 Discussion………...
6.5 Result…...55
7.0 Case study
Fractured Reservoir Characterization using AVAZ on
the Pinedale Anticline,Wyoming………...…56
7.1 Introduction………...……. 7.2 AVAZ Method………...…… 7.3 Results………...…. 7.4 Fractured Reservoir Characterization………...60
Conclusion...*
Reference...**
Appendix
...ALIST OF FIGURES
FIGURE PAGE
1-1 Think of a seismic wavefront hitting a reflector. The physical properties are
different on either side of the reflector...1
1-2 Plot of reflection amplitude versus offset showing the different classes of AVO response...4
1-3 the amplitudes at each time sample are analyzed linearly and two AVO attributes are extracted the intercept and slope of the best-fit straight line…...5
1-4 This velocity model was compiled from regional and gas wells from the study area. The point to note about it is that even though the seismic that will be derived from it is synthetic, the rock physics as measured in the boreholes, is real...6
1-5 The Fatti ‘P’ (normal-incidence P reflectivity) & ‘S’ displays extracted from pre-stack seismic gathers………..7
1-6 The scheme of the LMR™ (Rock Property Inversion) method. Post-stack inversion of P and reflectivities……….8
1-7 The LambdaRho™ and MuRho displays for our model………9
1-8 AVO responses of gas charged and brine-saturated dolomite reservoir……..10
2-1 images illustration of the integration of geological features...12
2-2 Input seismic data...13
2-3 Instantaneous Phase...13
2-4 Instantaneous Frequency...13
2-5 Instantaneous Dip Azimuth...14
2-6 Wavelet Apparent Polarity...14
2-7 Isometric display of reflection strength...14
2-8 Reflection parallelism...14
2-9 Reflection divergence...15
2-10 Scatterplot of porosity percent and seismic acoustic impedance at 7 well locations...16
2-11 Map of seismic acoustic impedance and porosity percent at 7 well...16
2-12 Scatter plot of measure porosity versus predicted porosity...17
2-13 The map of porosity based on a regression relation...17
2-14 Variable density time slice of a faulted seismic section...18
2-15 Coherency slice of the same data shown in Figure (2.14)...18
2-16 Shaded relief of the seismic amplitude time slice shown in Figure (2.14)...19
2-17 Composite density (bump mapped) display using the variable density...19
2-18 Shaded relief of the coherency slice...20
2-19 Composite density display using the variable density display...20
2-20 Close-up coherency of the faulted region using shaded relief...20
2-21 using the bump mapped...20
1 4 5 6 7 8 9 10 12 13 13 13 14 14 14 14 15 16 16 17 17 18 18 19 19 20 20 20 20
3-1 Structural framework of the study area with wells (the orange surface
Represents the top of salt)...C 3-2 (a) High-resolution seismic velocity (in ft/s). (b) Final high-resolution
velocity after trend-kriging with well data (in ft/s)...22
3-3 (a) Final pore-pressure volume (in units of psi). (b) Final effective stress Volume in units of psi for the same cross section...23
3-4 Reservoir characterization workflow...24
3-5 Absolute IP (top) and IS (bottom) along an inline...24
3-6 Schematic plot of IP compaction trend with respect to effective stress ...D 3-7 Maps of the (pdf) at different effective stress intervals... D 3-8 Hydrocarbon sand probability...26
3-9 Effective stress and hydrocarbon sand probability values...26
3-10 Hydrocarbon sand probabilities posted on a horizon without using Effective stress (left) and with effective stress (right)... 27
3-11 a possible flow diagram for reservoir characterization...28
3-12 Difference seismogram for seismic surveys in heavy oil field...28
3-13 Difference of synthetic seismograms...29
3-14 Time-lapse seismic survey over cold production field...30
3-15 Difference in isochron maps for heavy oil cold production fields...30
4-1 Illustration of time-lapse seismic...31
4-2 Dependence of 4-D (COS) on detectability and repeatability...33
4-3 Time-lapse seismic data from the Jotun Field...35
4-4 Example of a property cube (instantaneous attenuation)...36
4-5 Statistical reservoir volume analysis on the baseline survey...38
4-6 Shows a seismic saturation change map...39
4-7 4-D interpretation from Shell’s Gannet-C study...40
4-8 This figure shows the saturation ... 40
5-1 seismic section and shows a post-stack, mixed-norm inversion...43
5-2 Above is the post-stack seismic inversion of the overlain seismic reflection...43
5-3 The inversion in Figure (5.2) has been converted to depth...44
5-4 using the observed distribution of sandstone impedances ...44
5-5 The two panels compare interpretations done from the seismic using traditional methods and from the inversion...45
5-6 The two panels show slices of P Impedance and Vp/Vs...45
5-7 Vp/Vs from AVO Inversion is shown in perspective view...46
5-8 The logs in the left panel from a heavy oil play were used to compute the synthetic PP and PS gathers in the 2nd and 3rd panels...46
5-9 Geostatistical simulation of porosity...48 5-10 This is a perspective view showing the results of the simultaneous
C 22 23 24 24 D D 26 26 27 28 28 29 30 30 31 33 35 36 38 39 40 40 43 43 44 44 45 45 46 46 48
5-11 The upper panel shows the results of an AVO Inversion at Blackfoot...49
5-12 Schematic depiction of the stochastic inversion process...50
5-13 Input seismic data volume...50
5-14 Recursive inversion result...50
5-15 Stochastic inversion result...50
6-1 Reservoir model geometry...52
6-2 Temperature distributions from the reservoir simulations ...52
6-3 Pressure distributions from the reservoir simulations...53
6-4 Velocity and density model for seismic modeling...53
6-5 the synthetic seismic difference section (bottom) and the seismic survey...55
6-6 Gas saturation in the three layers versus the synthetic...55
6-7 Pressure in the three layers versus the synthetic seismic difference section...55.
6-8 Temperature in the three layers versus the synthetic seismic ...55
7-1 Map of the Lance Sand Depositional Fairway over the Pinedale Anticline...E 7-2 Geologic formations in the Pinedale Anticline...E 7-3 Cretaceous and Tertiary stratigraphic column...E 7-4 Time structure of the Lower-Lance horizon interpreted...F 7-5 Map of the migrated Crack Density estimates calculated using the AVAZ...F 7-6 Estimated crack density by the migrated stack & by shear-wave stack...58
7-7 visualization of fracture swarms around the well...58
7-8 View from the north end of the Pinedale Anticline showing well traces ...58
7-9 output of SUREFrac ,new well, Riverside 4-10, is added & is the output using all attributes including the A VA Z attributes...60
7-10 AVA Z crack density results along a dip line ...60
7-11 Dip line from 3D volume after fractured reservoir characterization...60 2.1 Table (2.1) structure, stratigraphic targets (Sheriff, 1992)... 2.2 Table 2.2 structure, stratigraphic targets (Sheriff, 1992)... 2.3 Table 2.3 flow simulation model factors (Sheriff, 1992)...
49 50 50 50 50 52 52 53 53 55 55 55 55 E E E F F 58 58 58 60 60 60 A B C
Introduction
In this article, we’ll examine the basics of amplitude variations with offset (AVO) and rock properties derived from them. First we’ll look at the physical cause for AVO behavior. We’ll then discuss the quantitative methodologies for calculating attributes which describe the AVO behavior of pre-stack seismic data. At the same time, we’ll show methods of interpreting these attributes and how they can help to
differentiate between regional geology and hydrocarbon reservoirs. We will then introduce some great efforts have been made to apply AVO analysis to carbonate reservoir characterization.
ARTICLE
Since their introduction in the early 1970’s, Complex Seismic Trace Attributes have gained considerable popularity, first as a con-venient display form, and later, as they were incorporated with other seismically-derived measurements, they became a valid analytical tool for lithology prediction and reservoir characterization. The amplitude content of seismic data is the principal factor for the determination of physical parameters which useful for seismic attribute. In this article we will discuss the Complex Trace Attributes, their classification and their characteristics. Then review issues related to selecting and using seismic attributes quantitatively in reservoir characterization projects, rather than to describe or classify them, with some example to predict porosity. Finally, we introduced the technique of using two separate attributes for the shaded relief and coloring of a bump mapped time slice to enhancing fault visibility.
Pore pressure and effective stress overpressured sediments are predicted by some steps, we firstly show how high-resolution seismic velocities were used for thsis purpose. Effective stress allows mapping nonstationary sedimentary compaction in space and is used as an additional attribute to allow for consistency between well and seismic data and stratigraphic layers, geostatistical mapping (trend-kriging) techniques were applied using several key horizons. The final trend-kriged model is constrained by the structural framework and the geology of the basin. Finally we will introduce how these informations can utilize to allow comparison of the hydrocarbon probability.
In order to optimize production from heavy oil fields, its as primary application for reservoir characterization. Effective reservoir
characterization and production will require teamwork between
geosciences and engineering. This is especially true in the development of heavy oil fields where there are dozens (possibly hundreds) of wells to be combined with 3-D seismic data sets. We focus on the combined use of engineering and geoscience, while reviewing past results and
forecasting future directions.
Time-lapse 3-D, or 4-D, seismic technology was first introduced in the early 1980s but has gained widespread interest and use only in the past 5 years. For some companies, 4-D seismic programs are now an essential part of their global reservoir management strategy. Reservoir surveillance during production is a key to meeting goals of reduced operating costs and maximized recovery. Differences between actual and predicted performance are typically used to update the reservoir's
geological model and to revise the depletion strategy. The fluid flow depends mainly on the efficiency of the reservoir seals. The primary objective of this article is to present fault characterization at the reservoir scale combined with 4D seismic.
The principle objective of seismic inversion is to transform seismic reflection data into a quantitative rock property, descriptive of the
reservoir. In its most simple form, acoustic impedance logs are computed at each CMP. In other words, if we had drilled and logged wells at all CMP’s, what would the impedance logs have looked like? Compared to working with seismic amplitudes, inversion results show higher
resolution and support more accurate interpretations. This, in turn
facilitates better estimations of reservoir properties such as porosity and net pay.
An integration time lapse seismic method with reservoir simulation is image a steam front and by-passed oil. Two seismic 2D surveys, acquired in 1991 and 2000, respectively, had been reprocessed to preserve amplitudes. The results indicate that the present reservoir model is reasonably good at describing the reservoir. finally ,It has been shown that open, fluid- (or gas-) filled fractures can be identified through the use of Amplitude Versus Angle and Azimuth (AVAZ) analysis on seismic data. This technique uses variations in the amplitudes of the long shot-receiver offsets of P-wave seismic data to determine the intensity and orientation of the fractures. It assumes that the fracturing generates a rock medium. This method can be modified to produce attributes that may be useful in the identification of fractures between wells.
1.0 Amplitude variations with offset
(AVO)
1.1 Seismic Energy partitioning at boundaries
Amplitude variation with offset comes about from something called ‘energy partitioning’. When seismic waves hit a boundary, part of the energy is reflected while part is transmitted. At a boundary where the incident angle is zero (normal incidence) there is no mode conversion. For example, a downward moving P-wave only generates reflected and
transmitted P-waves and a normally incident S-wave only generates
reflected and transmitted S-waves. At a boundary where the incident angle is not zero (non-normal incidence) the seismic energy typically generates four waves, at the boundary by splitting (mode conversion): reflected P-wave and S-P-wave and transmitted P-P-wave and S-P-wave (Figure 1-1). Most reflections are a superposition of events from a series of layers and will have a more complex behavior than what is shown here.
Figure (1-1) Think of a seismic wavefront hitting a reflector. The physical properties are different on either side of the reflector. The part of the P wave striking at a particular angle-of-incidence (represented by a ray) will have its energy divided into reflected and transmitted P and S waves. Another part of the incident wave with a different angle-of-incidence (represented by the second ray) will have its energy broken up into P and S waves too. How this ‘energy partitioning’ happens depends on the contrast in properties and also on the angles-of-incidence.
The amplitudes of the reflected and transmitted energy depend on the contrast in physical properties across the boundary. For us seismic people, the important physical properties in question are compressional
wave velocity (Vp), shear wave velocity (Vs) and density (ρ). But, the important thing to note is that reflection amplitudes also depend on the angle-of-incidence of the original ray. So if we know how the amplitudes of a reflector from a CDP change with angle-of-incidence, we can figure out something about how the physical properties of the rocks are changing across the boundary. How amplitudes change with angle-of-incidence for elastic materials is described by the quite complicated ‘Zoeppritz
equations’. But there are many different simplifications of these equations that make analysis of amplitudes with angle much easier. One thing to point out now is that ‘amplitude variation with offset’ is not always an appropriate term. For proper analysis, we need to examine ‘amplitude variation with angle’.
1.2 AVO Model- Zoeppritz Equations
Zoeppritz equations can be used to determine the amplitude of reflected and refracted waves at this boundary for an incident P-wave. The original equations are valid for any incident waves but only the P-wave is presented here and used in this study. The reflection and transmission coefficients depend on the angle of incidence and the material properties of the two layers. The angles for incident, reflected, and transmitted rays at a boundary are related by Snell’s l aw. The variation of reflection and transmission coefficients with incident angle and corresponding increasing offset is referred to as offset-dependant-reflectivity and is the fundamental basis for AVO.
Zoeppritz (1919) equations provide a complete solution for
amplitudes of transmitted and reflected P- and S- waves for both incident P- and S- waves.
Zoeppritz Equation: R(θ) = A + Bsin2(θ) Where:
R (θ) = Reflection coefficient (function of θ) θ = Angle of incidence
A = Zero-offset reflection coefficient (AVO intercept) B = Slope of amplitude (AVO Gradient)
A is the normal incidence reflection coefficient. B describes the variation at intermediate offsets and is called the AVO gradient or slope factor which is a function of average values of Poisson’s ratio,
compressional velocity and density. A and B are both highly dependant on the properties of the reservoir and the overlying formation. In general, P-wave velocity is dependant on both lithology (rock type) and fluid
content. S-wave velocity is dependant on lithology, but not sensitive to fluid content. S-wave velocities are not generally measured directly so the Vp/Vs ratio or Poisson’s ratio is used to determine the shear velocity from
the compres-sional velocity.
This commonly used form of Zoeppritz equations has the
interpretation that the near-offset traces reveal the P-wave impedance, and the intermediate-offset traces image contrasts in Poisson’s ratio.
Amplitude variation with offset, or more simply amplitude versus-offset (AVO), computes the seismic response of an interface between two beds from the contrast in elastic properties between the overlying and underlying formations. A normal incident, or zero offset, reflection (Ro) is
readily found from the contrast in acoustic impedance.
Where: Ro = Reflection Coefficient r1 = Density of medium 1 r2 = Density of medium 2 V1 = Velocity in medium 1 V2 = Velocity in medium 2
The change in amplitude of the reflection coefficient with offset is a function of the contrast in elastic properties across the interface where the AVO response is divided into three classes, Figure 1-2:
1.) A Class I AVO response has a large positive reflection at zero offset
and become smaller with increasing offset.
2.) A Class II AVO response has a small positive reflection at zero offset
and becomes very small or even negative with increasing offset.
3.) A Class III AVO response has a negative reflection at zero offset and
increasingly large negative reflections at increasing offsets. This is the classical AVO behavior. For example, a sand-shale interface often displays a negative reflection response that is increasingly large with offset.
Figure (1-2) Plot of reflection amplitude versus offset showing the different classes of AVO response.
1.3 Mechanics of quantitative AVO analysis
Quantitative AVO analysis are done on common-midpoint-gathers (or super-gathers, or common-offset gathers, or as they’re called in the AVO business ‘Ostrander gathers’. At each time sample amplitude values from every offset in the gather are curve-fit to a simplified, linear AVO relationship (Figure 1-3). A better way of saying this is that we fit a best fit straight line to a plot of amplitude versus some function of the angle of incidence. This yields two AVO attributes basically the slope and
intercept of a straight line - which describes, in simpler terms, how the amplitude behaves with angle of incidence.
There are a lot of equations that have been used over the last 15 years or so, but all of them, no matter what interpretation is given to the ‘intercept’ and ‘slope’, work in this same way.
The NIP is simply an estimate of the normal incidence reflectivity while the gradient indicates whether the amplitude is increasing or decreasing with offset and how strongly.
Figures (1-3) the amplitudes at each time sample are analyzed linearly and two AVO attributes are extracted essentially the intercept and slope of the best-fit straight line. These two attributes give us the basic description of the AVO behavior. The intercept of the best fit straight line is interpreted as the ‘normal incident P’ value describes the “normal-incidence
P-reflectivity” (NIP) and the slope is the gradient, or how the NIP (amplitude) changes with angle.
Shuey (1985) presented another approximation to Zoeppritz equations were the AVO gradient is expressed in terms of Poisson’s ratio and it extremely useful for application.
a
b
1.4 Example data set of quantitative AVO analysis
In order to examine a couple of the methodologies available for quantitative AVO analysis, we’ll use a model that was constructed from real well logs from the western Canada basin (Figure (1-4). In this example we have Bluesky and Halfway gas sand reservoirs. There are two things to note about AVO and this data set. First, though the seismic response is synthesized, the physical properties, as measured in well-bores, are real and so then are the AVO responses. Second, even though the examples are clastic gas reservoirs, the basic concepts can be applied to many different lithologies and reservoir types including carbonates.
The Bluesky gas sand is higher impedance than the encasing shales (this is a Class I anomaly in the classification scheme of Rutherford and others, 1989 as above figure (1-2). This means that on gathers, we would see a peak which dims with offset.
Figure (1-4) This velocity model was compiled from regional and gas wells from the study area. The point to note about it is that even though the seismic that will be derived from it is synthetic, the rock physics as measured in the boreholes, is real.
The Halfway gas sand is lower impedance than the encasing evaporites and carbonates (a Class III AVO anomaly). This means that we see a trough that increases in amplitude with offset on gathers. This is the classic “bright spot” anomaly we’ve all heard of.
1.5 Fatti Methodology
The Fatti methodology (Fatti and others, 1994) is a more
complicated amplitude-vs.-angle relationship than is the Shuey equation. Though it does not look particularly linear, there are only two unknowns, the P and S reflectivities that we solve for.
This methodology not only is more accurate to higher angles-of-incidence, but is independent of any assumption of density and allows any meaningful Vp/Vs as a constraint. Also, the two attributes that we can solve for, normal-incidence P and S impedance reflectivities (Figure 1-5) are much more intuitively accessible to interpreters. We can also easily calculate other meaningful interpretive displays from these attributes. For instance, a ‘fluid display’ combines both the P and S reflectivities to highlight areas that are anomalous with respect to the regional Vp/Vs trend (the regional ‘mudrock line’).
Figure (1-5) The Fatti ‘P’ (normal-incidence P reflectivity) and ‘S’ (normal-incidence S reflectivity scaled to P arrival time) displays extracted from pre-stack seismic gathers. The top right hand panel is the ‘Fluid’ stack derived from the P and S values and the bottom right is a cross-plot of the P and S reflectivities showing how the different regions can be differentiated.
One thing to note is that the calculated S reflectivities have P travel times. This is due to the fact that we are dealing with S reflectivity
estimates derived from P wave seismic gathers. We’d also like to point out that those who still use Shuey commonly estimate S reflectivities from the gradient (and other assumptions). It is a valid method, but not necessarily as reliable as Fatti.
One of the most powerful techniques of AVO interpretation is crossplotting (Figure 1-5). Here we are taking P reflectivity values (Rp), sorting them and plotting them against the corresponding S reflectivity values (Rs). We can easily see the Bluesky regional and gas sands and Halfway regional and gas sands display different trends, making them easily distinguished. In interpretational practice, modeling and well templating help to define the type of trends an interpreter should be looking for.
1.6 Rock Properties from AVO attributes:
Lambda/Mu-Rho
We can go even further to analyze the rocks and fluids within them. With the Fatti P and S impedance, we can estimate the layer impedances by post-stack impedance inversion.
Inverting the P and S reflectivities gives us P and S impedances for the geological layers. Taking the common equations for Vp and Vs (which depend on Lame’s parameter, modulus of rigidity and density), we can arrive at simple equations that combine the impedances to arrive at estimates of the elastic properties of the rock. We cannot isolate the density term, but are left with attributes which characterize the
incompressibility (LambdaRho™) and the rigidity (MuRho) of the rocks and the fluids in their pore spaces (Figure 1-6). Note that while the
common
Figure (1-6) The scheme of the LMR™ (Rock Property Inversion) method. Post-stack inversion of P and S reflectivities gives us layer impedances (pseudo well logs) which can be combined into rock properties (LambdaRho™ and MuRho) by the Vp and Vs relationships.
LMR and LambdaRho technique is also known as ‘Rock Property Inversion’ (RPI) as well as by other commercial names.
What does this all mean for interpreters?
Generally, sandstones are more incompressible than shales. Water filled sandstones are more incompressible than gas filled sandstones. Shales have less rigidity than sandstones. Carbonates can have considerably different incompressibilities and rigidities depending on such things as amount and type of porosity. Changes in fluid would not affect rigidity.
Figure (1-7) The LambdaRho™ and MuRho displays for our model. Note low values of lambda and relatively unchanged values of mu for the gas sands.
For our model example, we’ve calculated LambdaRho™ and MuRho (Figure 1-7). As we might expect, the gas sands show relatively lower values of LambdaRho™ and relatively little variation in MuRho. Here, the interpretation is straight forward, but the real world can often be more perversely difficult.
1.7 Recent Advances in Application of AVO to
Carbonate Reservoirs
Until recently, the seismic analysis of data from carbonate reservoirs relied mainly on interpreting zero-offset (stacked) seismic volumes. Common knowledge within the world of AVO suggests that zero-offset information is often insufficient to differentiate shale from carbonate porosity, or to discriminate gas-saturated from brine-saturated reservoirs. In the last few years, great efforts have been made to apply AVO analysis to carbonate reservoir characterization. In addition to this, a lack of carbonate rock property information is considered as one of the
obstacles in applying AVO to carbonate reservoir characterization. So the differences between clastic AVO and carbonate AVO need to be
clarified.
In contrast to clastic rocks, Vp/Vs ratio experiences an increase with increasing velocity or decreasing porosity. Also gas sands have a relatively low Vp and low Vp/Vs ratio in comparison with brine-saturated sandstone. Fluid effects in carbonates, especially the gas effect, are
contentious but of great interest. Common wisdom is that fluids have little or no effects on carbonate rock properties as these rocks have very high elastic moduli. Put another way, the high velocity of the carbonate rock matrix causes seismic waves to travel primarily through the matrix with less influence from pore fluids. However, an analysis of the
dolomite data indicates that gas does influence carbonate rock properties and its effect is significant. Notice that the behavior of dolomite rocks due to gas saturation is similar to that of sandstones. Namely, P-wave velocity and Vp/Vs ratio decrease, and S-wave velocity increases slightly due to decrease of density. The results of limestone are not shown. In General, sensitivity to fluid increases with increasing porosity. In general they are similar to dolomites except less sensitive to fluid.
Due to the complexity of clastic reservoirs, making a one-to-one comparison between clastic AVO and carbonate AVO is rather difficult. An apparent difference is that class I AVO represents a tight reservoir in carbonates, whereas in clastics it represents the wet “mudrock”
background or a reservoir with higher impedance. Another unique character is that amplitude for brine-saturated reservoirs has little variation until at large offsets. These unique characters of the AVO response in carbonate reservoirs remind us that clastics and carbonates should be treated differently.
Synthetic gathers for the gas charged reservoir and its brine
substituted case were then generated and shown in Figure (1-8). There is a class III AVO response at the base of the gas charged reservoir that changes to a weak class II AVO after brine substitution.
2.0 Seismic Attributes
The Oxford Dictionary defines an attribute as, “A quality ascribed to any person or thing”. Seismic attributes describe seismic data. They quantify specific data characteristics, and so represent subsets of the total information. Seismic attributes should be unique andSeismic attributes should not vary greatly in response to small changes in the data.
2.1 Seismic Attributes
2.1.1 Some about Seismic Attributes
Seismic Attributes are all the information obtained from seismic data, either by direct measurements or by logical or experience-based reasoning. In effect, attribute computations decompose seismic data into constituent attributes. This decomposition is informal in that there are no rules governing how attributes are computed or even what they can be. The attributes discussed here are the outcome of the work relating to the combined use of several attributes for lithology prediction and reservoir characterization.
The study and interpretation of seismic attributes provide us with some qualitative information of the geometry and the physical parameters of the subsurface. It has been noted that the amplitude content of seismic data is the principal factor for the determination of physical parameters, such as the acoustic impedance, reflection coefficients, velocities, absorption etc. The phase component is the principal factor in
determining the shapes of the reflectors, their geometrical configurations.
2.1.2 The Classification of Attributes
Attributes can be computed from prestack or from poststack data. The procedure is the same in all of these cases. For detailed studies, pre-stack attributes may be incorporated. Attributes can be classified in many different ways. Here we will introduce a classification based on by their computational characteristics:
Instantaneous Attributes
Instantaneous attributes are computed sample by sample, and represent instantaneous variations of various parameters. Instantaneous values of attributes such as trace envelope, its derivatives, frequency and phase may be determined from complex traces.
Wavelet Attributes
This class comprises those instantaneous attributes that are
computed at the peak of the trace envelope and have a direct relationship to the Fourier transform of the wavelet in the vicinity of the envelope peak.
Physical Attributes
Physical attributes relate to physical qualities and quantities. The magnitude of the trace envelope is proportional to the acoustic impedance contrast; frequencies relate to bed thickness, wave scattering and
absorption. Instantaneous and average velocities directly relate to rock properties.
Geometrical Attributes
Geometrical attributes describe the spatial and temporal relationship of all other attributes. Lateral continuity measured by semblance is a good indicator of bedding similarity as well as
discontinuity. Bedding dips and curvatures give depositional information. Geometrical attributes are also of use for stratigraphic interpretation since they define event characteristics and their spatial relationships.
Integrating Geometrical Attributes
shown in
(Figure 2.1) whichillustrates the integration of geological features. The image on the left represents a time slice through a 3D seismic amplitude cube. The image shows the presence of channel-like features, and their reproduces the shapes, locations, and orientations of the channel-like features in the seismic amplitude data. The center image depicts a conceptual model of the channels, in this case, a meandering channel system, with a user specified wavelength, sinuosity, etc. The right image is one of many possible pixel-based simulations. The advantage of this method is the easy implementation and conditioning to well data, regardless of the number of wells, unlike object-based modeling.
Figure (2.1) these images illustrate the integration of geological features. The left image is seismic amplitude data showing channel-like features, the center image is the conceptual model, and the right image is one pixel-based realization.
2.1.3 Some Basic Attributes and Its Characteristics
Instantaneous Phase. Because wave fronts are defined as lines of
constant phase, the phase attribute is also a physical attribute and can be effectively used as a discriminator for geometrical shape classifications:
(Figure 2.2) shows a 3-D section display of original seismic data. (Figure 2.3) is the corresponding instantaneous phase display.
Figure 2.2 Input seismic data Figure 2.3 Instantaneous Phase
Instantaneous phase is:
• Good indicator of lateral continuity,
• Relates to the phase component of wave-propagation. • Used to compute the phase velocity,
• Devoid of amplitude information, hence all events are represented, • Detailed visualization of stratigraphic elements.
Instantaneous Frequency. It has been shown that the
instantaneous frequency attribute relates to the centric of the power spectrum of the seismic wavelet (Figure 2.4). The instantaneous frequency attribute responds to both wave propagation effects and depositional characteristics, hence it is a physical attribute and can be used as an effective discriminator. Its uses include:
• Hydrocarbon indicator by low frequency anomaly.
• Fracture zone indicator, may appear as lower frequency zones. • Bed thickness indicator. Higher frequencies indicate sharp interfaces such as exhibited by thinly laminated shales, lower frequencies are indicative of more massive bedding geometries. Another piece of
information we can extract from the seismic data are the locations where instantaneous frequencies jump or exhibit a negative sign. These sign reversals are caused by closely-arriving reflected wavelets.
instantaneous dip azimuth is shown in (Figure 2.5). The colors
indicate various dip azimuths as measured from North. A wavelet attribute example, the apparent polarity is shown in (Figure 2.6) Red shows the positive and blue shows the negative apparent polarities.
Figure (2.5) Instantaneous Dip Azimuth Figure (2.6) Wavelet Apparent Polarity
Reflection strength. Attribute was an amplitude measure, which he
developed for bright spot analysis (Figure 2.7). Reflection strength cast seismic.
(Figure 2.7) Isometric display of reflection strength.
The excitement was reminiscent of that of bright spots, for, like amplitude, discontinuity had clear meaning and enabled interpreters to see something they couldn’t easily see before. This success breathed new life into attribute analysis. Other multi-dimensional attributes soon
followed, such as parallelism and divergence (figure 2.8 & 2.9).
Figure (2.8) Reflection parallelism, a seismic stratigraphic attribute. It is quantified as the local degree of variation of reflection dip from the average. Parallel reflections indicate a lower-energy depositional
environment, suggestive of shales; nonparallel reflections indicate a higher-energy deposi-tional environment, suggestive of sands.
B A
Figure (2.9) Reflection divergence, a seismic stratigraphic attribute. It is quantified as the degree to which successive reflections diverge looking downdip. Yellow indicates divergent reflections and blue indicates convergent. (a) Vertical view; (b) 3D opacity view. The analysis window captured only small-scale divergence, such as that in the channel fill, thereby revealing the extent of the channel
2.2 Quantitative Use of Seismic Attributes Reservoir
Characterization
2.2.1 Does the Seismic Data Warrant the Use of Attributes?
Before dashing headlong into the computation of numerousattributes, look at quality of the data, determine the processing workflow. Too often we have seen that the data simply does not warrant use beyond a basic structural interpretation because of poor signal quality, low
frequency content at the reservoir level, and improper processing. Data can be processed for structural interpretation using a minimum phase wavelet and a gain to enhance structural surfaces. Processing seismic data for stratigraphic and rock and fluid properties requires zero-phase, true amplitude, and migrated data, which is more costly and time consuming, but necessary if most attribute studies are to succeed. Perhaps geometrical attributes describing spatial and temporal continuity do not require such rigorous processing. for example, the purpose is acoustic impedance inversion, the data must be zero-phase, with true-amplitude recovery. Success depends on zero-phase, true-amplitude seismic data.
2.2.2What Seismic Measures and What We Require for
Reservoir Characterization
The parameters measurable from the seismic data are (1) travel time, (2) amplitude, (3) the character of events, and (4) the patterns of events. From this information we often compute a lot of structure, stratigraphic targets (Sheriff, 1992):
2.2.3Seismic Attributes — Property Predictors or False
Prophets?
For our purposes we consider a seismic attribute as any seismically derived parameter computed from prestack or poststack data, before or after migration. Amplitude, phase, and frequency are fundamental
parameters of the seismic wavelet and from these few all other attributes are derived, either singly or in combinations, and many of the new
attributes duplicate each other because of the nature of the computations. One principle should be kept in mind when using attributes; the physical basis for the correlation with properties measured at the wells.
Unfortunately there is a common practice of selecting attributes based solely on the strength of their observed correlations with properties measured at the wells, but with little thought given to the validity of the correlation, except that it looks good.
2.2.4 Predict porosity using seismic acoustic impedance
Following example illustrates some of these principles. The seismic attribute is acoustic impedance derived from zero-phase, true amplitude, and 58-fold 3D seismic data. Inversion to acoustic impedance followed the method using sonic and density logs from three wells to calibrate the inversion. The objective of this example is to use seismic acoustic impedance to predict porosity away from the wells.(Figure 2.10) shows a scatter plot of seismic acoustic impedance and porosity measured at 7 well locations. The negative correlation coefficient is expected because acoustic impedance (AI) varies inversely with the magnitude of porosity. Even with these high correlations, we assume that the pattern displayed by the seismic AI (Fig. 2.11) is related to the true distribution of porosity and that the high correlation isn't serendipitous.
(Figure 2.11) Map of seismic acoustic impedance and porosity (Figure 2.10) Scatterplot of
(Figure 2.12) illustrates the value of integrating a seismic attribute when mapping porosity using only 7 porosity values. For this study we had the advantage of knowing the porosity values at 48 other well
locations, thus we can test the accuracy of using the seismic attribute as a predictive variable. Of the 48 'new' locations, only 9 were misclassified using the criterion of finding porosity > 9 percent. The wells shown as open circles are the values for the 7 wells and are predicted perfectly, because colocated cokriging is an exact interpolator, like kriging (the red line shows the perfect line of correlation).
(Figure 2.12) Scatter plot of measure porosity (Y-axis) versus
predicted porosity (X-axis). Nine wells were misclassified out of the 48 'new' wells.
The additional 48 wells were drilled in a typical west Texas 40-acre 5-spot pattern. If seismic data had been available when designing the
original in-fill program, we can see that many wells should not have been drilled using the porosity cutoff criteria.
2.2.5 What about using Linear Regression?
Reviewing the scatterplot shown in (Figure 2.10) and recalling that the correlation coefficient is -0.95, it would seem logical to simply use linear regression to predict porosity from seismic acoustic impedance. The map can be made using the following regression equation: Y= c-bX.
Porosity = 62.13 - (0.00157 * AI) Where c is the intercept and b is the slope.
Figure (2.13) The map of porosity based on a regression relation.The map of porosity based on a regression relation is shown in (Figure 2.18). The fact that regression uses only one data point during the estimation is valid, because traditional regression assumes data independence. Although the results seem fine, the b term (slope of the function) imparts a spatial linear bias (trend) in the estimates during the mapping process.
2.3 Enhancing Fault Visibility Using Bump Mapped
Seismic Attributes
2.3.1 Shaded relief of the coherency
Seismic coherency is a measure of the consistency of a seismic section. Where events are continuous, coherency is high. But where events are terminated either by faulting or stratigraphy, there is a loss of continuity, which is clearly evident as dark lines on coherency displays. Coherence is calculated directly from the seismic data and thus provides an unbiased view of the faulting in a section. Consider (Figures 2.14 and 2.15), which show the time slice and coherency slice for a region around a series of prominent faults. The faults are evident on the vertical section but on the time slice of (Figure 2.14) they are difficult to follow spatially. If all we had to go on were the time slices, interpreting even the major faulting would be a time consuming task.
Fig (2.15) Coherency slice of the same data shown in Figure (2.14).
Fig (2.14) Variable density time slice of a faulted seismic section.
Notice how difficult it is to follow Notice how much clearer and more distinct the faulting is. But the faults from the vertical section onto the time slice.
The faults show up very clearly, however, on the corresponding coherency slice shown in (Figure 2.15). On this display the
discontinuities (faults) show up as dark lineations making their spatial interpretation a simpler task. Coherency has become one of the staples of fault interpretation.
As useful as coherence displays are, they are not perfect. They show discontinuities but without the seismic amplitude data it is difficult to assign relevance to what we are seeing. To interpret a coherency slice correctly we need to make continual reference back to the amplitude data which is a time consuming and less than optimum task.
2.3.2 Bump Mapping Attributes
For example, (Figure 2.16) is a shaded relief image of the time slice shown in (Figure 2.14) with the vertical seismic removed for
simplicity. The variable density display of (Figure 2.14) shows the gross features of the time slice but it lacks any direct mechanism for identifying the actual structure. We cannot tell intuitively what is high and what is low. We can make continual reference back to the color palette, which in this case shows, red-yellow as high amplitude and blue-gray as low but we have no inbuilt mechanism for automatically equating these colors to structure. If we didn’t know the color palette we would have no idea what the time slice represented.
The shaded relief display (Figure 2.16) shows how the amplitude structure of the time slice would reflect light. It is a map of reflectivity and the highs and lows are now obvious. This is because the visual cortex is proficient at interpreting these patterns of light and dark. We don’t need any other information here to see the structure – it is intuitively obvious.
From an interpretation perspective, however, (Figure 2.17) is incomplete. It shows us the structure of the data but without the variable density coloring it is still hard to interpret.
.
Compare this now with (Figure 2.17), which is the bump mapped
Fig (2.16). Shaded relief of the seismic amplitude time slice shown in Figure (2.14). The vertical section has been removed for clarity. Notice the
pronounced 3-D effect produced by the shading.
Fig (2.17) Composite density (bump mapped) display using the variable density of Figure 2.14 and the shaded relief of Figure 2.15. Notice that the 3-D effect visible on Figure 2.15 persists.
2.3
.3Bump Mapping Coherency
Having seen the improvements in perceptibility brought about by bump mapping we can ask the question, “What would happen if we used a different attribute for the shaded relief”. (Figure 2.17) uses a shaded relief of the seismic amplitudes to modulate the colors of the variable density display (again of the seismic amplitudes). We are not restricted,
however, to using the same attribute for the light source that we use for the variable density component. In this section we use the shaded relief of a coherency slice to modulate the variable density seismic amplitudes.
The discontinuities now appear as pronounced ridges whereas the Fig (2.18) Shaded relief of the
coherency slice shown in Figure (2.15) Note how much clearer and sharper the discontinuities are than on Figure 2.15
Fig (2.19) Composite density display using the variable density display of Figure (2.14) and the shaded relief of Figure (2.18).
bulk of the seismic amplitudes remain unaffected. Notice again how distinct the discontinuities
(Fig 2.20) Close-up coherency of the faulted region using shaded relief.
Fig (2.21) Same close-up as Figure (2.20) but using the bump mapped image of Figure (2.19).
Consider (Figure 2.18), the shaded relief image of the coherency slice shown in (Figure 2.15) Here we see that viewing the coherency slice as shaded relief has, by itself, an immediate benefit. The discontinuities, on (Figure 2.15), are now more clearly defined and easier to follow.
Consider now Figures (2.19) and 2.21) where we used the shaded relief of (Figure 2.18) to bump map the seismic amplitude time slice of (Figure 2.14). The shaded relief coherency now appears as ridges on the
amplitude time slice and clearly relates the discontinuities back to the amplitude data.Considering that coherency provides an unbiased view of discontinuities, (Figures 2.19 and 2.21) could be thought of almost as automatically interpreted time slices.
3.0 Reservoir Characterization
3.1 pore-pressure prediction
(A combined geomechanics-seismic inversion workflow using trend-kriging techniques in a deepwater basin)
To optimize drilling decisions and well planning in over pressured areas, it is essential to carry out pore-pressure predictions before drilling. Knowledge of pore pressure implies knowledge of the effective stress, which is a key input for several geomechanics applications. It is also a required input for 3D and 4D seismic reservoir characterization. (Figure 3.1- see appendix) shows the structural framework associated with the geostatistical mapping
3.1.1 Methodology
The four components that comprise the joint workflow are a high-resolution velocity analysis, a porepressure and effective-stress
prediction, a multi-attribute seismic inversion, and a Bayesian lithofacies classification using IP, IS, and effective stress.
High-resolution velocity analysis (step 1).
The interval velocities in the present study were obtained using a
method that maximizes the stacking power of spatially continuous events in prestack gathers. The initial interval velocity model wasn't built by inversion of it and it's regularized using a semblance-based interactive velocity analysis system.
The velocity model, thus obtained, went through geostatistical mapping (trend-kriging) using the upscaled welllog velocities within several key stratigraphic layers. The kriging is done within a stratigraphic framework, to ensure consistency between the interpreted horizons and geology.
(Figures 3.2a and b) show the velocity model before (Figure 3.2a) and after trend-kriging (Figure 3.2b). Note that near the wells (within the correlation length) the resolution approaches the well-log resolution and the data are influenced more by the well-log data.
Figure (3.2) (a) High-resolution seismic velocity (in ft/s). (b) Final high-resolution velocity after trend-kriging with well data (in ft/s).
Pore-pressure and effective-stress prediction (step 2).
Most methods for pore-pressure prediction are based a principle which implies that the elastic wave velocities are a function of the effective stress tensor, which is defined as the difference between the total stress tensor and the pore pressure p. In the study presented here, it is assumed that the elastic-wave velocity is a function only of the vertical effective stress σeff (weight of the rock matrix and the fluids in the pore space overlying the interval of interest).
σeff = S – p
S is calculated by integrating the bulk density from the surface to
the specific depth:
S = g ∫ ρ(Z) dZ
where ρ(z) is the density at depth z below the surface and g is the acceleration due to gravity.
The velocity-to-pore-pressure transform was derived from data from wells in the area of interest or offset wells. The formation pore pressure is assumed to be represented by the mud weights used for drilling these wells, because during drilling operations mud weights are increased to prevent fluid and gas influxes from the formation into the wellbore; therefore these relevant mud weights can provide a reasonably close estimate of formation pressure. By inverting to the available pore pressure data in these nearby wells in order to calibrate the velocity to- pore-pressure transform, the normal velocity can be accurately defined, allowing identification of possible shallow overpressures. This method contrasts with current methods that fit a trend line to velocity data as a
function of depth below mudline. This trend is often referred to as a “normal trend” which captures the expected velocity variation with depth when the pore pressure is hydrostatic.
The calibrated velocity-to-effective-stress transform was then applied to the trend-kriged velocities. To apply the pore-pressure transform, it is necessary to determine density at all locations so that a 3D volume of total vertical stress can be calculated. To do this, a density cube was built by geostatistically mapping the available well-log data in the area, constrained by depth horizons and a 3D trend. The geostatistical mapping (trend-kriging) of the upscaled density log data is guided by a 3D density-trend volume. This density- trend cube was resampled into 3D curvilinear stratigraphic grids representing stratigraphic layers for use as a 3D trend (local mean). The upscaled density log data were then kriged in each layer assuming a geostatistical model consisting of a single spherical structure with a given vertical correlation length and an
isotropic lateral correlation length (parallel to bedding). Integration of the density cube using Equation 2 thus allows the total vertical stress to be determined anywhere in the model.
Note that the velocity model went through geostatistical mapping (trend-kriging) using the upscaled well-log velocities within several key stratigraphic layers, similar to the method applied in creating the density model. The use of horizons helped to maintain consistency of the well data and the geologic structure. The velocity-to-pore-pressure transform, which is established from nearby well data, is then applied to this trend-kriged velocity volume. The final volumes of pore pressure and effective stress are shown in (Figures 3.3a and b).
Figure (3.3) (a) Final pore-pressure volume (in units of psi). (b) Final effective stress volume in units of psi for the same cross-section.
Multi-attribute seismic inversion (step 3).
The multi-attribute seismic inversion process has been presented previously. The workflow is schematically shown in (Figure 3.4). The processes enclosed in the light blue rectangles describe the needed
modification in the workflow for incorporation of the effective stress. The background models for IP and IS were derived from the trend-kriged velocity and density volumes. This step is important, because all
attributes used in the model must have a common background model and follow a consistent set of assumptions. (Figure 3.5) shows absolute IP and IS along an inline, derived from multi-attribute seismic inversion after low-frequency compensation.
Figure (3.4)- Reservoir characterization workflow. The multi-attribute seismic
inversion includes effective stress and background model from seismic velocity. SCVA stands for surface-consistent velocity analysis. VMB stands for velocity model
building.
Figure (3.5). Absolute IP (top) and IS (bottom) along an inline from multiattribute seismic inversion. Color bar is in AMO units.
Lithofacies classification using effective stress, IP, and IS (step 4).
The key to using effective stress as an attribute for reservoir characterization is to understand the underlying behavior of the elastic parameters (IP and IS) with respect to lithology and effective stress. (Figure 3.6a see Appendix) shows a schematic relationship between IP versus effective stress for shale and brine-filled sand representing sediment compaction in clastic basins. In general, IP will increase with effective stress for shales (black line) and brine sands (green line). It is clear that the presence of hydrocarbons in the pore space will reduce IP, as both VP and density will be lower than in the brine-filled case. The scatter of shale and brine sands around the compaction trend is used to derive a probability density function (pdf). It is clear that discriminating between oil and gas in this system is very difficult due to the scatter of the data. Therefore, the oil sands and gas sands are included in a single “hydrocarbon” class.
The scatterplot allows the discrimination of three lithofacies classes that were used for inversion in the basin: shale, brine sand, and hydrocarbon sand. The derived 4D pdf P(IP, IS, σeff, Lithoclass) is plotted at different effective stress intervals. Note that the ability to discriminate shale, brine sand, and hydrocarbon sand changes as a function of the effective stress according to the compaction model. Furthermore, the effective stress is spatially varying, as seen clearly in (Figure 3.3). Thus, to correctly classify a lithology type, knowledge of IP and IS is necessary, and the spatially varying effective stress is needed. This allows defining the position with respect to the compaction curve The class probability values were obtained for each set of IP, IS, and effective stress for each seismic data sample and effective stress value by deriving the posterior pdf P (Lithoclass IP, IS, σeff. From (Figure 3.7) it is clear that the ability to correctly identify the pay zone is related also to the vertical effective stress, and not only to the seismic attributes.
(Figure 3.8) shows the computed hydrocarbon sand probability values along an inline using the method described above. A control well shows good agreement between a saturation log and high hydrocarbon probability. (Figure 3.9) shows another inline, which allows comparison of the hydrocarbon probability and effective stress at the same location.
Figure (3.8) Hydrocarbon sand probability along an inline derived from IP, IS, and effective stress attributes. The magenta line is a measured saturation log at a control well location.
Figure (3.9) Effective stress and hydrocarbon sand probability values along another inline (see Figure 3.8).
In (Figure 3.10), hydrocarbon sand probability values are posted on a horizon with and without effective stress as an attribute. As is evident from (Figure 3.7), without effective stress the results are not as clear as
when effective stress is used, because the probability functions are
“smeared.” Furthermore, if effective stress is not taken into account, only the shallower events are identified as potential hydrocarbon sands. This can be seen in the left of (Figure 3.10), where potential hydrocarbons are visible only in the structurally shallower part of the horizon. If effective stress is included as an attribute (right of Figure 3.10), however, the hydrocarbon probabilities in the deeper, more compacted areas, can be mapped as well. The good match between the well and seismic (shown in Figure 3.8) is possible because the effective stress “riding” on IP and IS efficiently captures the spatial variation in the rock model (both vertically and laterally) for lithofacies class discrimination.
Figure (3.10) Hydrocarbon sand probabilities posted on a horizon without using effective stress (left) and with effective stress (right).
3.2 Reservoir Characterization and Heavy Oil
Production
3.2.1 Methodology
We will focus primarily on how geophysics will fit into the reservoir development strategy of heavy oil fields. Generally, we try to resolve the changes in the reservoir as a function of the production processes. Hence, we repeat the seismic survey, keeping the acquisition and processing parameters as constant as possible so that changes in the seismic response will reflect reservoir changes (no pun intended) during the production process. Seismic surveys must generally be acquired over producing fields during the production processes. Here the goal was to map steam injection fronts during the production of heavy oil from the Formation. The time-lapse seismic method becomes an enhanced oil recovery tool in which production processes such as infill drilling and steam injection can be adjusted according to reservoir changes.
The role of geophysics and reservoir simulation in reservoir characterization is illustrated in (Figure 3.11). Much of our geophysical work involves the acquisition, processing and interpretation of seismic data. Generally, the seismic surveys are repeated so that we may see the changes in the reservoir. The repetition of seismic surveys results in “4-D” or time-lapse seismology, where we examine differences in the seismic data over time. Hopefully, these differences exhibit reservoir changes. Eventually we wish to produce a seismic model whose response (the synthetic seismogram) matches the time-lapse seismic data. These models of seismic velocity and density can then be converted into earth models of porosity, permeability and temperature change. Ultimately, we would hope to link together the seismic data, the seismic model and the reservoir model.
Figure (3.11) a possible flow diagram for reservoir characterization.
3.2.2 Results
As illustrated by (Figures 3.12 and 3.13), we see encouraging results in the area of heavy oil field reservoir characterization. (Figure 3.12) shows a seismic reflectivity difference section. This difference section from the heavy field was obtained by differencing seismic surveys completed in nine years in order to show the effects of steam injection into the heavy oil sands. A large difference is seen in the circled zone of interest.
Figure (3.12) Difference seismogram for seismic surveys in heavy oil field, illustrating that the zone of greatest change is in the steam flood zone.
(Figure 3.13) shows the differencing of synthetic seismograms for nine years seismic models. The encouraging agreement between the model responses and the real data indicates that our seismic model is probably realistic. Following a successful modeling of the seismic data then developed a reservoir steam flow model. In essence, this is the goal of the reservoir characterization procedure shown in (Figure 3.11).
Figure (3.13) Difference of synthetic seismograms for nine years seismic models; the seismic model is created by the fluid substitution based on steam zones.
Our research in heavy oil fields has involved the use of seismic monitoring over fields that involve both hot and cold flow production. In both cases, we see a growing need for extensive use of multicomponent time-lapse seismology. In the case of monitoring steam injection, there are several methods of seismic monitoring, including reflectivity
differencing, impedance differencing, and the computation of Vp/Vs ratios from multicomponent data. A case is made for the extensive use of multicomponent methods for mapping sand content and steam fronts.
In cold production of heavy oil, seismic monitoring can be used for mapping both wormholes and foamy oil production then we can show the seismic anomalies. There are some geoscientists have describes the
physics of cold production footprints and have laid the groundwork of reservoir characterization in cold production problems. Although much remains to be done in the integration of petrophysical and geophysical methods to map subsurface changes, the initial findings of geoscientists suggests that multicomponent seismology holds considerable promise. Some intriguing examples of the effects of cold production on seismic responses, in which reservoir production is related to seismic anomalies, are shown by geoscientists.
(Figure 3.14) shows a subtle contrast between seismic sections recorded in nine years over an Upper sand channel for other field. There appears to be a time delay in the later survey as a result of the cold
production process. This delay is confirmed by isochron maps for the interval between the Top of two reflectors as indicated on Figure 4. If the isochron maps are differenced for the two data, we note travel time delays for the wells under production, as shown in (Figure 3.15). The time delay can be attributed to the presence of foamy oil or wormholes created by the cold production process. Both of these effects will tend to decrease the P wave velocity, as shown.
In both hot and cold production, it shown that we need to integrate geophysics and reservoir simulation models for a complete description of the reservoir. Reservoir characterization is an evolving science. There is a constant need for integration and “closing the loop” between geoscience and reservoir engineering. Developments that have occurred in recent years include the following:
• Integration of borehole and seismic information in the development of reservoir simulation models.
• Increased use of AVO (amplitude variation with offset) methods. • Optimized use of multicomponent seismology.
• Development of reservoir simulation models from geophysical and geological data.
Figure (3.14) Time-lapse seismic survey over cold production field.
Figure (3.15) Difference in isochron maps for heavy oil cold production fields. Zones of greatest time delay (blue polygon) are near the best producing wells. The wells circled in black have a cumulative production of over 15,000 cubic metres.
An exciting feature of reservoir characterization is that we are continuously able to prove or disprove our models due to the dynamic flow of information from production.
