While reflections associated with conformal sedimentary layers are usually coherent and continuous, other reflections such as mass transport complexes, karst collapse, and salt, may appear to be quite chaotic, without any specific orientation. We may also see chaotic events that have little to do with the target geology, but rather are artifacts due to variations in the overburden and surface or budget limitations resulting in a suboptimum acquisition program. While some of these artifact issues can be handled at the time of processing, a certain level of randomness remains in most seismic data volumes. Geologic features of interpretational interest such as fault damage zones, unconformities, and gas chimneys often have randomness associated with them, which can be characterized in terms of seismic disorder attribute amongst others.
We demonstrate the application of seismic disorder attribute to two different datasets and find that it is a useful attribute for assessing the signal-to-noise ratio and data quality, in addition to helping delineate damage zones associated with large faults, and the interior of salt dome structures.
Presentation Date: Monday, October 17, 2016
Start Time: 2:15:00 PM
Location: Lobby D/C
Presentation Type: POSTER
Chopra, Satinder (TGS) | Sharma, Ritesh (TGS) | Ray, Amit (Reliance Industries Ltd) | Nemati, M. Hossein (Arcis Seismic Solutions, TGS) | Morin, Ray (Talisman Energy, Inc) | Schulte, Brian (Geokinetics Inc) | D'Amico, David (Repsol)
The Devonian Duvernay Formation in northwest central Alberta, Canada has become a hot play in the last few years due to its richness in both liquid and gaseous hydrocarbon resources. The oil and gas generation in this shale formation made it the source rock for many oil and gas fields in its vicinity. This case study attempts to showcase the characterization of Duvernay Formation by using 3D multicomponent seismic data, and integrating it with the available well log and other relevant data. This characterization has been done by deriving rock physics parameters (Young’s modulus, Poisson’s ratio, etc.) through deterministic simultaneous and joint impedance inversion, with appropriate quantitative interpretation. In particular, we determine the brittleness of the Duvernay interval which helps us determine the sweet spots therein. The scope of this characterization exercise was extended to explore the induced seismicity observed in the area (i.e. earthquakes of magnitude >3), that is perceived to be associated with hydraulic fracture stimulation of the Duvernay. This has been a cause of media coverage lately. We attempt to integrate our results with the induced seismicity data available in the public domain, and have obtained reasonably convincing results.
Presentation Date: Wednesday, October 19, 2016
Start Time: 2:20:00 PM
Presentation Type: ORAL
Perz, Michael (TGS) | Chopra, Satinder (TGS) | Sharma, Ritesh (TGS) | Cary, Peter (TGS) | Li, Xinxiang (Arcis Seismic Solutions) | Ohlhauser, Wendy (Arcis Seismic Solutions) | Pike, Kimberly (PennWest) | Creaser, Brian (Enerplus) | Nemati, M. Hossein (Arcis)
A high-effort, multicomponent 3C3D seismic data set was acquired over a mature oil field in central Alberta in order to better understand the characteristics of a waterflood operation. True-amplitude processing of the data was undertaken, and joint PP-PS prestack impedance inversion reveals a pronounced set of anomalous low-impedance lineaments at the target level which exhibit a very strong spatial correlation with known water injector locations. Rock physics modeling demonstrates that fluid pressure effects are heavily influencing the seismic response in the vicinity of the injectors, and are accounting for the observed low-impedance anomalies. Analysis of injection and production data suggests that the seismic data can play a vital role in identifying zones of unswept pay in this area.
Presentation Date: Monday, October 17, 2016
Start Time: 1:00:00 PM
Presentation Type: ORAL
Spectral decomposition carried out with the use of the continuous wavelet transform requires the choice of a mother wavelet, which in turn is used to derive a family of wavelet functions. These wavelet functions are scaled and shifted to ‘fit’ them to the input seismic data traces. Unlike the fixedlength discrete Fourier transform method, the continuous wavelet transform (CWT) window varies with frequency, resulting in better temporal resolution at high frequencies and better frequency resolution. We evaluate the relative value and use of Morlet, Mexican Hat, Derivative of Gaussian (DOG), and the Shannon wavelets in the analysis of a fluvial-deltaic system. Spectral decomposition carried out on two seismic data volumes shows that the Morlet wavelet is more robust and yields better results than the others. While we do not suggest that this conclusion be generalized, we do recommend that this exercise be carried out on a test volume to select the best mother wavelet to be used in the spectral decomposition.
Over the last decade or so, spectral decomposition has become a well-established tool that helps in the analysis of subtle stratigraphic plays and fractured reservoirs. As the name suggests, spectral decomposition decomposes the seismic data into individual frequency components that fall within the measured seismic bandwidth, so that the same subsurface geology can be seen at different frequencies. Thin beds or features will be tuned and have relatively higher amplitude at higher frequencies.
Spectral decomposition is carried out by transforming the seismic data from the time domain into the frequency domain. (Partyka et al. (1999) and Marfurt and Kirlin (2001) used a fixed length short window discrete Fourier transform (SWDFT). Since then other methods have been introduced, including the continuous wavelet transform (CWT) (Sinha et al., 2005), the S-transform (Stockwell et al., 1996), or the matching pursuit decomposition (Mallat and Zhang, 1993). Each of these methods have their own applicability and limitations (e.g. Chakaborty and Okaya, 1995; Leppart et al., 2010), and the choice of a particular method often depends on the end objective. For example, the discrete Fourier transform uses an explicit user-defined time window for its computation, and this choice has a bearing on the resolution of the output data. For instance, if the window is defined to be the laterally varying thickness of stratal slices of a picked geologic formation, then the SWDFT will generate cycles/million years vs. cycles per seismic seconds of recording time.
The Late Jurassic-Early Cretaceous Vaca Muerta (VM) Formation in the Neuquén Basin has served as an important source rock for many of the conventional oil and gas fields in Argentina. With the interest in developing and exploiting the shale resources in the country, many companies there have undertaken the characterization of the VM Formation in terms of the elements of shale plays.
Shale plays can be identified based on, amongst other characteristics, the total organic carbon (TOC), as better TOC leads to better production. However, there is no way of measuring it directly using seismic data, and it can only be estimated in an indirect way. Considering the influence of TOC on compressional, shear velocities and density, geoscientists have attempted to compute it using the linear or nonlinear relationship it may have with P-impedance. Given the uncertainty in using such a relationship for characterizing the VM formation, a different approach has been followed for characterizing it. In addition to P-impedance, gamma ray (GR) is another parameter of interest for characterizing the VM Formation as a linear relationship seems to exist between GR and TOC.
In this study, using P-impedance and GR volumes, a Bayesian classification approach has been followed to obtain a reservoir model with different facies, based on TOC and its associated uncertainty. As the first step, we defined different facies based on the cutoff values for GR and P-impedance computed from well-log data. Having defined the different facies, Gaussian ellipses were used to capture the distribution of data in a crossplot of GR vs P-impedance. Next, 2D probability density functions (PDF’s) were created from the ellipses for each of the facies. Combining these PDF’s with GR and P-impedance volumes, different facies were identified on the 3D volume. Poststack model-based inversion was used to compute the Pimpedance volume while a probabilistic neural network (PNN) approach was used to compute GR volume. Both derived Pimpedance and GR volumes which correlated well at blind wells on the 3D volume, and lent confidence in the characterization of VM Formation.
Stratigraphic interpretation of seismic data requires careful interpretation of the amplitude, phase and frequency so as to gauge the geologic subsurface detail. Sometimes the interpretation of the changes in amplitudes is not easy and the equivalent phase is difficult to comprehend. In such cases seismic attributes are utilized to provide additional information that could aid the interpretation. One of the earliest set of ‘instantaneous’ attributes was based on complex trace analysis, and instantaneous phase has been used for interpretation of stratigraphic features such as pinchouts and discontinuities as well as fault edges.
In this study, we demonstrate that the interpretability of seismic data can be enhanced with the use of spectral phase components derived during spectral decomposition. As there are different methods for decomposing seismic data into its component frequencies and phase within the seismic bandwidth, we consider two of the common methods in our analysis here, namely the continuous wavelet transform and the matching pursuit methods. We also show that the principal component analysis of spectral magnitude and phase components yields additional insight into the data. The first principal component ‘churned’ out of the phase components shows clarity in the features of interest and compares favourably with the discontinuity attributes commonly used for the purpose.
The strength of seismic reflections carry subsurface information sensitive to absorption and scattering, propagation through fluids and complex interference patterns from stacked stratigraphy. Quantitative interpretation requires that the seismic amplitudes be as ‘true’ as possible, and are not contaminated with noise or other distortions of the acquisition process. In addition to its reflection strength, seismic events are characterized by their frequency and phase. Thin bed interference often increases the high frequency and decreases the low frequency components of the seismic wavelet. For adjacent reflectors having equal but opposite reflection coefficients, the peak amplitude occurs or “tunes” at the quarter wavelength frequency. This latter thin bed tuning phenomena also gives rise to a 90° change in phase. Linear increases and decreases in impedance that may be associated with upward fining or coarsening also give rise to a 90° phase change, as does the reflection from an interface between two units of equal impedance but a finite change in attenuation, or 1/Q. While changes in reflection strength are easy to see, the recognition of such phase and frequency changes are subtle and more easily overlooked on large 3D seismic data volumes. Seismic attributes quantify such subtle changes.
Estimation of density plays an important role in characterizing subsurface reservoirs. Reliable determination of density from noise-free seismic data requires long offsets, or it can be determined from measured converted waves. As the acquisition, processing and interpretation of multicomponent seismic data entails more time and cost, their use has been slow in our industry. Considering the importance of the density attribute in the determination of lithology and fluid discrimination, we describe a novel approach for its determination from conventional (PP) seismic data. The key point of this approach is that it does not require long offsets. Though this methodology has been applied to a variety of reservoir characterization exercises, we describe its application to the Montney Shale Formation in the Montney-Dawson area of British Columbia, Canada. We also demonstrate a comparison of the proposed approach with simultaneous impedance inversion application to longoffset seismic data for determination of density. The proposed approach has shown encouraging results.
The determination of density and fluid discrimination plays an important role in reservoir characterization exercises as its accuracy leads to the sinking of fewer wells in the ground, drawing higher recoveries from them, improving stimulation and completion practices, and lowering uncertainty in production forecasts (Jia et al., 2012). Both the compressional wave velocity (VP) and shear velocity (VS) are important for reservoir characterization, with the former being related to the rock matrix and the fluid contained in the pores of the rock, and the latter being only related to the rock matrix. Besides VP and VS, density is an important elastic parameter that relates to porosity, fluid type and its saturation, as well as mineral composition (Li, 2005). It has also been observed in several instances that hydrocarbon reservoirs do not show an appreciable lowering of VP compared with the nonreservoir rocks, as would be expected. In contrast, density has proven to be a hydrocarbon indicator (Silva et al., 2013). Crossplotting between different rock parameters and pore fluids also demonstrates that density provides the best differentiation between hydrocarbon reservoirs and other rock/fluid types (Van Koughnet et al., 2003).
Seismic impedance inversion is an important tool to estimate rock and reservoir properties from the seismic data. Seismic data is band-limited in nature and lacks the low-frequency component. As the lowfrequency component holds the basic information on geological structure, the lack of low-frequency information degrades the quantitative prediction based on seismic inversion. So, it is essential to build an accurate low-frequency model to have confidence in seismic inversion and in turn on the quantitative predictions made therefrom.
In this paper, we develop a new workflow of predicting the lowfrequency impedance model that uses a single-well low-frequency model apart from other relevant seismic attributes in the multi-attribute regression analysis. This study was carried out on a dataset from northeastern British Columbia in Canada. Inversion results using this approach have been validated at the blind well locations and an excellent match between well logs and inversion results has been observed. We have also attempted the collocated cokriging technique for building a low-frequency model and used it for seismic impedance inversion. A comparison of both the methods has been discussed.
Currently, impedance inversion of seismic data is a standard tool to estimate the elastic properties from seismic data for reservoir characterization projects. Knowledge of absolute impedance is necessary for quantitative as well as qualitative predictions of the reservoir. As the seismic data is band-limited and does not contain the low-frequency band of the spectrum, it is essential to build a proper low-frequency model for better estimates of the reservoir properties. Sams and Saussus (2013) have shown some practical implications of low-frequency model selection on quantitative interpretation results.
Typically such a model is built by using well log data, interpreted horizons and sometimes the seismic velocities provided the velocity data is of good quality. There are a variety of interpolation techniques that could be used to construct the low-frequency model from well log data. These include linear interpolation of single well data, inversedistance, triangulation, kriging, and cokriging methods. If there is considerable lateral variation in the elastic properties across the 3D area, a single-well model does not work very well. Also, inverse distance and triangulation methods usually generate some kind of bull’s eye effect on the low-frequency model that creates artifacts on the inversion results, which are not geological.
The Duvernay shale liquids play running along the foothills east of the Rocky Mountains, possesses all the prerequisites of being a successful unconventional play, and has gained the attention of the oil and gas industry in Alberta, Canada. Even though, the net shale thickness ranges between 25 and 60 m for most of the play, in places it thins further. Considering the poor vertical resolution of the available seismic data, it is not possible to identify and characterize the thin Duvernay sweet spot zones using seismicallyderived attributes. In a recent case study, we found it to be challenging to characterize the thin Duvernay reservoir zone, and consequently developed a workflow that successfully addressed the challenge and identified the thin sweet spots.
Although conventional reservoirs remain a very important part of the world’s natural gas supply, horizontal drilling and multistage fracturing have now made it possible to develop and exploit unconventional reservoirs. With the successful development of unconventional shale reservoirs in North America, the oil and gas industry has shifted its attention to the Devonian Duverney shale liquids play in Alberta. The Duvernay shale play has been recognized as the source rock for many of the large Devonian oil and gas pools in Alberta, including the early discoveries of conventional hydrocarbons near Leduc. The Duvernay shale basin spans approximately 50,000 square miles, with an estimated 7,500 square miles within the thermally mature or wet gas window (Davis et Al. 2013), from northwest to southeast across Alberta. Its stratigraphic age is equivalent to the Muskwa Formation of the Horn River dry shale gas play to the northwest in the neighboring province of British Columbia (Rivard et al. 2013).
The Duvernay was deposited in a broad marine setting as a basin-filling shale, surrounded by equivalent aged Leduc reef build-ups. Due to rapid basin filling during maximum sea-level transgressions, enormous quantities of organic sediments were dumped in this deep, oxygen-starved basin that are the present day Duvernay source rocks, where TOC (total organic carbon) is as high as 20% (McMillan et al.,2014). The Duvernay shale is fine-grained and silica rich. As a result of the fine grains, rocks have increased total surface area that leads to a higher absorbed gas component in organic-rich rocks. Moreover, silica-rich rocks are more brittle and favorable for fracking.
Carbonate sedimentary rocks that have been fractured, or dolomitized and laterally sealed by tight undolomitized limestone, are frequently seen to produce hydrocarbons. However, the differentiation between limestones and dolomites is a challenge. The purpose of this work is to describe a workflow for discriminating limestones and dolomites, and to map the lateral extent of dolomite reservoir rocks that have a thickness below the seismic resolution.
For this study, we have used the photoelectric index (Pe) well log curve as it is a sensitive indicator of mineralogy. At any well location, Pe exhibits somewhat higher, but flat trend for background limestone. Relative to this flat trend the dolomite units are represented by low values of Pe. However, such well log curves are available only at the location of the wells. We demonstrate an approach of computing Pe volume from the seismic P- and S-impedance volumes. We begin our exercise by crossplotting the P-impedance (IP) against the S-impedance (IS) color coded with Pe curve using the well log data. In IP-IS crossplot space, we highlight the discrimination between the limestone and dolomite clusters by choosing an axis of rotation to highlight the desired discrimination. The result of such a rotation is a single display attribute we call lithology impedance (LI) to identify the formation lithology. Furthermore, its relationship with the Pe curve is established for obtaining Pe volume from the seismic data. The issue of the resolution of the seismic data is addressed by using a thinbed reflectivity inversion.