SEISMIC INVERSION: COMPARISON OF ACOUSTIC AND ELASTIC IMPEDANCE INVERSION MODELS FOR ROCK PROPERTY PREDICTION
ABSTRACT
Niger Delta is one of the major hydrocarbon-producing basins in the world. This basin has a quite complex geology which makes routine seismic interpretation a challenging task for understanding the reservoir properties such as lithology and fluid content. Seismic inversion has proven to be a reliable tool for a detailed understanding of the reservoir, especially for lithological identification.
In this study, the effort was made to compare acoustic and elastic impedance volumes with regard to litho-fluid discrimination in an offshore field in the Niger Delta. For this purpose, five horizons were interpreted to determine geological inputs for the impedance model building. Well, log data was tied to a near post-stack seismic volume and this was used in creating an initial acoustic impedance model. Thereafter, an initial elastic impedance model was created using well log data tied to a far post-stack seismic volume. The initial elastic impedance model was created with an elastic impedance log generated at 360 which conforms to the incident angle for the far offset stack. Following analyses of the initial models at the well location, a full model-based acoustic and elastic impedance inversion were carried out separately for the entire area, using the interpreted horizons as controls.
The inverted results reveal reservoir tops and show lateral variations in lithology away from the well location. In particular, the elastic impedance inversion gave superior results only in areas where the acoustic impedance log used in inverting the near seismic volume is near-constant through top reservoir transition. In areas where the acoustic impedance log could clearly distinguish the reservoir top, the acoustic and elastic impedance volumes gave comparable results. In comparison to the individual input seismic volumes, the inverted results would greatly improve reservoir property interpretation with possible integration with seismic stratigraphy.
TABLE OF CONTENTS
CERTIFICATION ............................................................................................................................................... i
DEDICATION .................................................................................................................................................. ii
ACKNOWLEDGEMENT .................................................................................................................................. iii
ABSTRACT ..................................................................................................................................................... iv
TABLE OF CONTENTS ..................................................................................................................................... v
LIST OF FIGURES .......................................................................................................................................... vii
CHAPTER ONE ............................................................................................................................................... 1
INTRODUCTION ......................................................................................................................................... 1
1.1 BACKGROUND OF STUDY .......................................................................................................... 1
1.2 STATEMENT OF PROBLEM ........................................................................................................ 3
1.3 OBJECTIVE OF THE STUDY ......................................................................................................... 5
1.4 SIGNIFICANCE OF THE STUDY ................................................................................................... 5
1.5 SCOPE OF STUDY / LIMITATION ................................................................................................ 6
1.6 STUDY AREA .............................................................................................................................. 7
CHAPTER TWO .............................................................................................................................................. 9
LITERATURE REVIEW ................................................................................................................................. 9
2.1 THEORETICAL/CONCEPTUAL FRAMEWORK .............................................................................. 9
2.2 REVIEW OF IMPEDANCE INVERSION METHODS ..................................................................... 16
a) Band Limited (Recursive & Advance recursive)| .................................................................... 17
b) Blocky (or Model-based) Inversion ......................................................................................... 19
c) Sparse spike inversion ............................................................................................................. 19
d) Neural Network Inversion ....................................................................................................... 20
e) Geostatistical Inversion ......................................................................................................... 20
2.3 REVIEW OF PREVIOUS WORKS ................................................................................................ 20
2.4 GEOLOGY OF THE NIGER DELTA .............................................................................................. 22
CHAPTER THREE .......................................................................................................................................... 26
METHODOLOGY (IMPEDANCE INVERSION) ............................................................................................ 26
3.1 RESEARCH DESIGN .................................................................................................................. 26
3.2 NATURE OF DATA / SOURCES OF DATA .................................................................................. 26
3.3 METHODS OF DATA ANALYSIS ................................................................................................ 27
3.3.1 Data Loading ................................................................................................................... 27
3.3.2 Data Transformation ....................................................................................................... 29
3.3.3 Well to Seismic Tie .......................................................................................................... 31
3.3.4 Initial model .................................................................................................................... 35
3.3.5 Analysis of the Initial Model............................................................................................ 36
3.3.6 Inversion .......................................................................................................................... 36
CHAPTER FOUR ........................................................................................................................................... 37
RESULTS AND DISCUSSION...................................................................................................................... 37
4.1 PRESENTATION OF DATA ........................................................................................................ 37
4.3 DISCUSSION OF FINDINGS ....................................................................................................... 43
CHAPTER FIVE ............................................................................................................................................. 44
CONCLUSION AND RECOMMENDATION ................................................................................................ 44
5.1 CONCLUSION ........................................................................................................................... 44
5.2 RECOMMENDATION ............................................................................................................... 44
REFERENCES ................................................................................................................................................ 46
CHAPTER ONE
INTRODUCTION
1.1 BACKGROUND OF STUDY
The development of structurally composite oil and gas fields requires a careful understanding of
reservoir characterization so as to make the field performance more efficient. This requires
combined scrutiny and understanding of the existing data such as seismic data and well log data.
Seismic data make available vital information about the common geology of the area. However,
extracting geological information such as porosity, density, and shale volume is a great challenge
for an interpreter. In seismic studies, seismic inversion is one of the great tools used in estimating
detailed properties of the reservoir rock. Inversion is the process of extracting, from seismic data,
the underlying geology which gave rise to that seismic (Hampson Russell).
The basic objective of seismic inversion is to transform seismic reflection data into a quantitative
rock property, descriptive of the reservoir (Ogagarue, 2016). Conventionally, inversion has been
applied to post-stack seismic data due to their ready availability with the aim of extracting
acoustic impedance. In recent times, inversion has been extended to pre-stack seismic data, with
the aim of extracting both acoustic and shear impedance. This allows the calculation of pore
fluids.
During the last decades, several methods for estimating rock properties from seismic data were
introduced and tested with the objective of providing further information for detailed reservoir
characterization. The first deterministic inversion methods for acoustic impedance mapping were
developed in the late 1970s and became known generally as recursive inversion (Lavergne and
Willem, 1977; Lindseth, 1979).
Nowadays, most of the research efforts in this field are focused on the inversion and
interpretation of variations of seismic reflection amplitude with change in distance between
source and receiver (amplitude vs. offset) from pre-stack data. Because wells in a reservoir field
are often spaced at hundreds or even thousands of meters, the ultimate goal of a seismic
inversion procedure in the context of reservoir characterization is to provide models not only of
acoustic impedance but also of other relevant physical properties, such as effective porosity and
water saturation, for the inter-well regions. Such quantitative interpretations may sometimes
require the use of other seismic attributes in addition to the traditional seismic reflection
amplitudes (Rijks and Jauffred, 1991; Lefeuvre et al., 1995; Russell, 2004; Sancevero et al.,
2005; Soubotcheva, 2006).
Diverse seismic inversion methods are viably used to map detailed reservoir rock properties such
as lithology and fluid properties. These properties are estimated by using different inversion
algorithms on the seismic data with erstwhile geological knowledge and well log data. The
relationship between seismic and lithology is empirical. The reduction of uncertainty in this
relationship will have large effect on the reservoir model building, thus on development and
production of the hydrocarbon (Badri et al., 2002). The inverted impedance model is also used
for building facies and facies-based porosity and permeability model (Shrestha et al.,
2002).Seismic characteristics obtained from time, amplitude and frequency do not make
available satisfactory information of reservoir properties on a layer by layer basis. Layer by layer
information can be derived by means of stratigraphic inversion of post stack seismic data in
terms of acoustic impedance.
There are many inversion techniques which are utilized in the industry for extraction of acoustic
impedance from post stack seismic data, these techniques are band-limited, model based, and
neural network nonlinear inversions (Russell, 1988, Duboz et al., 1998, Keys and Foster, 1998,
Van Reil, 2000).
1.2 STATEMENT OF PROBLEM
The global energy market is still determined to get more oil and gas. Greater demands for
hydrocarbon in recent years caused oil and gas industry players to focus on deep offshore, and
hot areas around the world. Even by overwhelming the geographical challenges there still remain
serious challenging areas to deal with.
In addition to collecting and evaluating data quickly and competently, another challenge in the
world of seismic exploration is the subsurface indistinctness which can be a difficult task in most
regions. The best appearance of the subsurface is always considered for more precise and low-
risk decision making, to reduce drilling risk (dry wells) and increase yield. Cutting-edge
techniques provide enormous amount of information that can help to address the challenges
which improves our interpretation of subsurface structures and also make known more facts
about hydrocarbon prospects. Universal struggle for hydrocarbon continues to motivate the
necessity to intensify exploration and boost recovery rate; nonetheless the cost of the operation is
critically essential.
“Wells can measure several reservoir properties at high vertical resolution, but offer only sparse
sampling laterally”, often at considerable cost (Russell, 1988). In addition difficulties arise;
however, when we encounter poor wellbore condition or unexpected lithology or complexities
related to subsurface structure. On the other hand, “seismic data provide nearly continuous
lateral sampling at relatively low expense but with much less vertical resolution” (Russell, 1988).
To address the challenges, seismic inversion for estimating the elastic properties was introduced.
It is the latest advancement in an integration approach which is the inverse modeling of the logs
from seismic data.
The seismic reflection method was used initially as a useful tool for structure identification;
some kind of structures could act as trap (such as anticline) for hydrocarbon reservoir (Russell et
al, 2006). So much efforts have been made to improve our understanding of the amplitudes of
seismic reflection data. It has been proved that a considerable amount of information is contained
in seismic amplitude reflection that could be connected with porosity, lithology and even fluid
change within the subsurface (Russell et al, 2006). Though seismic amplitude is equally a good
indicator in the subsurface, several case studies depict that it is a vague indicator of hydrocarbon.
In some cases, acoustic impedance alone may not be sufficient to quantify reservoir rock
properties such as lithology and pore fluid, for an in-depth understanding of the reservoir.
In order to obtain more accurate seismic reservoir characterization (also known as reservoir
geophysics) all available seismic, petro-physical and geological information need to be
integrated into volumetric distribution of reservoir properties like porosity and saturation. Each
of them has a piece of information which assists us in delineating or describing a reservoir or
monitoring the change (Walls et al, 2004).
By inversion, we convert seismic reflection amplitude to impedance profile (rock property
information) and estimate model parameters (in term of impedance instead of reflectivity). Using
inversion process, we try to “reduce discrepancies between observed and modeled seismic data”
(Russell, 1988). The main objective here is to extract underlying geology and reservoir
properties from some set of observed seismic data to use for better lithology and fluid prediction
and prospect delineation (Russell, 1999). That is to say, the purpose is to obtain reliable estimate
of P-wave velocity, S-wave velocity and density to calculate the physical properties and the
earth`s structure.
With more complex geological conditions and rise in cost of hydrocarbon explorations, the
inversion technique has become more popular and is widely used in the seismic industry for
exploration and development of existing field. Inversion technique is a useful tool to derive
elastic properties such as P-impedance, bulk modulus, Poisson`s ratio and so forth, which largely
control the seismic response. As a result the outcomes we obtain from seismic inversion make up
better volumetric estimation (hydrocarbon anomalies are better predicted) than seismic attributes
derived from band limited seismic data (Connolly, 1999).
1.3 OBJECTIVE OF THE STUDY
In seismic inversion, the aim is to transform seismic reflection data, which are interface-based
property, into a layer-based quantitative rock property, descriptive of the reservoir.
In this work, an acoustic and elastic impedance volumes were inverted from near and far angle
stacks, respectively. The objective was to underscore which of these volumes was more effective
in litho-fluid delineation in a given well in the Niger Delta.
1.4 SIGNIFICANCE OF THE STUDY
Seismic inversion enables the specialist to separate the seismic wavelet from the reflection series
characterized by the geologic formations and results in an estimate of residual impedance for
each layer. Post-stack inversion is one alternative to conventional velocity analysis that provides
higher resolution by inverting for impedance from the reflection strength (Bell, 2002).
Inversion replaces the seismic signature by a blocky response, corresponding to acoustic and/or
elastic impedance layering. It facilitates the interpretation of meaningful geological and petro-
physical boundaries in the subsurface. Inversion increases the resolution of conventional seismic
in many cases and puts the study of reservoir parameters at a different level. It results in
optimized volumetric, improved ranking of leads/prospects, better delineation of drainage areas
and identification of ‘sweet spots’ in field development studies.
The ability to estimate acoustic impedance and a parameter related to shear impedance increases
the interpreter’s ability to discriminate between different lithologies and fluid phases, resulting in
a detailed reservoir characterization for improved hydrocarbon recovery.
1.5 SCOPE OF STUDY / LIMITATION
The scope of this project is confined to the comparison of acoustic and elastic impedance
inversion for rock property prediction. This exercise is not trivial, however, because the post-
stack inversion technique ignores the fact that offset-dependent behavior (amplitude vs. offset) is
buried in the stacked response and can cause significant perturbation of the results. One way to
overcome this limitation and also boost resolution of the results at the same time is to use only
the near-offset traces for the analysis. This is a good method to use because it provides higher
resolution results due to the removal of far-offset data that are degraded by normal move-out
(NMO) stretch.
One of the challenges of inversion is that the method does not normally include a low-frequency
trend for velocity, but instead predicts variations in residual impedance that must be separated
into velocity and density trends using well log data. Incorporating a low-frequency velocity trend
in the analysis is possible, but it is commonly observed that the low-frequency trend, where
combined with the residual impedances on the seismic data, does not match the predicted
impedances from well logs. Thus, the calibration of this method still remains a limitation in
many cases.
Seismic inversion is not a unique process. There are several acoustic impedance earth models
that generate similar synthetic traces when convolved with the wavelet. The number of possible
solutions is significantly reduced by putting constraints on the modelling and, in doing so, a most
plausible scenario is retained. The support of other investigation techniques, like AVO analysis
and forward modelling, increases the confidence in the inversion results. Seismic inversion
depends heavily on the proper integration of well data. Seismic inversion is gradually becoming
a routine processing step in field development studies as well as for exploration purposes. Even
time lapse inversions are now conducted. The positive track record of case histories clearly
demonstrates the added value of this type of seismic analysis (Curia D, 2009).
1.6 STUDY AREA
Data used for this study were obtained from XY field, located in Niger Delta offshore, several
kilometers south of Port Harcourt. The Niger Delta is one of the world’s largest and sandiest
basins, situated on the West African continental margin at the apex of the Gulf of Guinea. It lies
between latitudes 400N and 600N and longitudes 300E and 900E, and covers a surface area of
approximately 75,000 sq. km. Figure 1 below shows the location of the area.
.