Seismic Interpretation Below Tuning with Multiattribute Analysis

Seismic Interpretation Below Tuning with Multiattribute Analysis

 

By: By Rocky Roden, Thomas A. Smith, Patricia Santogrossi, Deborah Sacrey, and Gary Jones
Published with permission: The Leading Edge
April 2017

Seismic Interpretation Below Tuning with Multi-Attribute Analysis

Abstract

The tuning-bed thickness or vertical resolution of seismic data traditionally is based on the frequency content of the data and the associated wavelet. Seismic interpretation of thin beds routinely involves estimation of tuning thickness and the subsequent scaling of amplitude or inversion information below tuning. These traditional below-tuning-thickness estimation approaches have limitations and require assumptions that limit accuracy. The below tuning effects are a result of the interference of wavelets, which are a function of the geology as it changes vertically and laterally. However, numerous instantaneous attributes exhibit effects at and below tuning, but these are seldom incorporated in thin-bed analyses. A seismic multiattribute approach employs self-organizing maps to identify natural clusters from combinations of attributes that exhibit below-tuning effects. These results may exhibit changes as thin as a single sample interval in thickness. Self-organizing maps employed in this fashion analyze associated seismic attributes on a sample-by-sample basis and identify the natural patterns or clusters produced by thin beds. This thin-bed analysis utilizing self-organizing maps has been corroborated with extensive well control to verify consistent results. Therefore, thin beds identified with this methodology enable more accurate mapping of facies below tuning and are not restricted by traditional frequency limitations.

Introduction

Fundamental to the evaluation of a prospect or development of a field is the seismic interpretation of the vertical resolution or tuning thickness of the associated seismic data. This interpretation has significant implications in understanding the geologic facies and stratigraphy, the determination of reservoir thickness above and/or below tuning, delineation of direct hydrocarbon indicators (DHIs), and project economic and risk assessment. The foundations of our understanding of seismic vertical resolution come from the influential research by Ricker (1953), Widess (1973), and Kallweit and Wood (1982). The conventionally accepted definition of the tuning thickness is a bed that is ¼ wavelength in thickness, for which reflections from its upper and lower surfaces interfere constructively where the interface contrasts are of opposite polarity, resulting in an exceptionally strong reflection (Sheriff, 2002). Below tuning, the time separation of the reflections from the top and bottom of a reservoir essentially do not change, and the thickness must be determined by other methods. Some of the earliest work in determining bed thickness below tuning was by Meckel and Nath (1977), Neidell and Poggiagliolmi (1977), and Schramm et al. (1977), who related the normalized or sum of the absolute value amplitudes of the top and bottom reflections of a thin bed to have a linear relationship to net thickness of sands. Brown et al. (1984, 1986) employed seismic crossplots of gross isochron thickness versus composite amplitude to determine an amplitude scalar to calculate net pay. With sufficient well calibration, Connolly (2007) presented a map-based amplitude-scaling technique based on band-limited impedance through colored inversion for thin-bed determination. As Simm (2009) indicates, various techniques to determine thin beds have limitations and require assumptions that may not be met all of the time. Therefore, what is required is an approach that exploits other properties of thin beds even though our fundamental understanding of seismic amplitudes and time separation of reflections from the top and bottom of a reservoir implies this cannot be done consistently and accurately with conventional amplitude data. A seismic multiattribute approach takes advantage of analyzing several attributes at each instant of time and identifies patterns from these data to produce results below tuning that relate to bed thickness and stratigraphy.

Background

A seismic attribute is any measurable property of seismic data, such as amplitude, dip, phase, frequency, and polarity that can be measured at one instant in time/depth, over a time/depth window, on a single trace, on a set of traces, or on a surface interpreted from the seismic data (Schlumberger Oilfield Glossary). Instantaneous seismic attributes are quantities computed sample by sample at a time/depth instance and indicate a continuous change in properties along the time and space axis. The original instantaneous attributes are features of a complex seismic trace and are based on the analytical signal of the original trace and its Hilbert transform; these include instantaneous amplitude (envelope), instantaneous phase, and instantaneous frequency. Taner and Sheriff (1977) state that the conventional seismic trace may be thought of as a measure of kinetic energy, and as a wavefront passes through the earth, the particle motion is resisted by an elastic restoring force so that energy becomes stored as potential energy. The Hilbert transform may be thought of as a measure of elastic potential energy. Therefore the measured seismic amplitude trace and the complex trace attributes, as well as the dozens of attributes derived from them over the last few decades, represent instantaneous attributes that define the kinetic and potential energy of the seismic trace through space and time/depth.

Robertson and Nogami (1984) and Tirado (2004) identified a frequency increase or frequency tuning effect below conventional seismic tuning. Hardage et al. (1998), Radovich and Oliveros (1998), Taner (2003), and Zeng (2010) confirmed the relationship of frequency spikes and bed thickness on instantaneous frequency data but, in general, recommend the integration of lithofacies and depositional facies analysis because of the nonuniqueness of the frequency attribute. From instantaneous phase and frequency data, Davogustto et al. (2013) identified phase discontinuities that produce phase residues above and below tuning depending on the frequency content of the data. Zeng (2015) showed with wedge models that a 90° phase-rotated wavelet, similar to the Hilbert transform, produces better definition of beds below tuning than zero-phase wavelets. This research has identified various phenomena at or below tuning, but a consistent interpretation of thin beds is still a challenge and requires another approach.

Multiattribute analysis

In an effort to understand which instantaneous attributes produce phenomena below tuning, a noise-free wedge model (Figure 1) was generated from which dozens of instantaneous attributes were computed. The wedge model runs from 80 feet to zero thickness with the associated densities and velocities indicated in Figure 1. Displayed below the wedge model is an amplitude display with an Ormsby 5-10-60-90 wavelet applied. Designated in Figure 1 is the tuning thickness, ¼ wavelength, which is the vertical resolution of this model and is annotated as a vertical dashed line on all the displays in the figure. Tuning thickness for this model is 30 feet or 10 ms. From the amplitude model, seven instantaneous attributes are displayed that produce phenomena at or below tuning. These are some of the most prominent attributes that display at or below tuning effects. It is evident from these displays that information is available below tuning because various seismic attributes are seen to change values on the right side of the wedge model. Regardless of identified phenomena below tuning, these features depend on complicated constructive and destructive wavelet interference patterns. The acoustic parameters to produce these below-tuning features depend on the geology that can be laterally and vertically changing. To interpret any of these attributes individually for thin beds is certainly not conclusive, and understanding their interrelationships is essentially impossible. A multiattribute approach is required to sift through the different attribute types and identify the combination of attributes that produce patterns that are indicative of thin beds. The fundamental limits of signal detection in the presence of noise are as important in the multiattribute approach as it is in previous works.

wedge model for Eagle Ford

Figure 1. Wedge model from which the amplitude and seven attributes were generated. These attributes exhibit effects at or below tuning, which is designated by the associated vertical dashed lines.

Self-organizing maps

Taking advantage of today’s computing technology, visualization techniques, and understanding of appropriate parameters, self-organizing maps (SOMs) (Kohonen, 2001) efficiently distill multiple seismic attributes into classification and probability volumes (Smith and Taner, 2010). SOM analysis is a powerful, nonlinear cluster analysis and pattern recognition approach that helps interpreters identify patterns in their data that can relate to inherent geologic characteristics and different aspects of their data. Seismic data contain huge amounts of data samples, are highly continuous, greatly redundant, and significantly noisy (Coléou et al., 2003). The tremendous amount of samples from numerous seismic attributes exhibits significant organizational structure in the midst of noise (Taner et al., 2009). SOM analysis identifies these natural organizational structures in the form of clusters. Figure 2 displays a single amplitude trace and seven different seismic attributes computed from the amplitude. All of these traces are displayed in a wiggle-trace variable area format. This display represents 100 ms vertically, and each horizontal scale line represents a sample (4 ms). Each of these attributes is at different scales and in some cases vastly different scales. It is evident from this figure that each of the attributes measures a different component of the total acoustic energy at every sample. SOM analysis identifies clusters from different combinations of attributes and reveals significant information about the classification structure of natural groups that is difficult to view any other way. It is important that the original amplitude data, from which all the attributes are generated, are processed accurately and with as high a signal-to-noise (S/N) ratio as possible. This is because the SOM process identifies all patterns in the data including signal that represents geologic features and stratigraphy, as well as coherent and noncoherent noise. Based on numerous SOM analyses on seismic data, the SOM results reveal significant detail in systematic stratigraphic variations (toplap, downlap, etc.).

wiggle-trace of seismic attributes

Figure 2. Wiggle-trace variable area display format of a 100 ms window of seismic data with the amplitude trace and seven associated traces of attributes. Each attribute trace is at a different scale, and each horizontal scale line is separated by the sample rate of 4 ms. If all these traces were employed in a SOM analysis, each red circle indicates the samples that would be input.

To graphically explain how SOMs identify patterns in the data as related to thickness, Figure 3a displays a model trace that represents reflections from: (1) the top of a thick high-acoustic impedance layer; (2) the top of a thin low-acoustic impedance layer; (3) the base of the same thin low-acoustic impedance layer; (4) the top of a deeper thin high-acoustic impedance layer; (5) the base of the deeper thin high-acoustic impedance layer; and (6) the base of a yet deeper thick high-acoustic impedance layer. A low level of Gaussian-distributed reflection coefficient noise was added. A wavelet centered at 30 Hz and a bandwidth of 40 Hz was applied to the reflection coefficients in Figure 3a. The sample interval is 2 ms, which is the thickness of the two thin beds in the model. Conventional tuning thickness is 12 ms. From the amplitude trace (labeled real), the Hilbert transform was generated and resulted in 411 samples of paired values. The real and Hilbert transform analytic traces were realized 100 times to produce a significant number of data points. The set of 41,100 data point pairs were input into a SOM analysis.

SOM analysis of a trace model; amplitude, Hilbert transform

Figure 3. SOM analysis of a simple trace model. (a) Reflection coefficients, wavelet employed, the associated amplitude (real) trace, and Hilbert transform trace. (b) Attribute space with data points from real and Hilbert transform traces repeated 100 times and presented in a 2D crossplot. (c) Classification of data point clusters by SOM analysis with each cluster denoted by a specific neuron color as seen on the associated 2D color map.

Figure 3b displays a crossplot of the data points from these two attributes based on their associated attribute values (attribute space). Figure 3c displays after SOM analysis how the different patterns and clusters in the data are identified by winning neurons distinguished by the associated colors. Random colors were selected to illustrate contrasts between adjacent areas. A winning neuron is a point in the SOM analysis that is associated with a natural pattern in the data. Each pattern in this crossplot has been identified by a winning neuron and is represented in the 2D color map where there are 64 winning neurons. Figure 4 displays the crossplot with the identified clusters colored by different neurons of Figure 3c and denotes where in the model the different patterns occur. It is clear from Figure 4 that even with two thin beds and the thickness of the sample interval (2 ms), the patterns are quite distinguishable. The specific winning neurons identifying the reflection coefficients in the model are labeled on the 2D color map. Therefore, the use of seismic attributes in a SOM analysis that are known to produce below tuning effects can be very powerful in the identification of thin beds.

SOM crossplot for seismic mutliattribute analysis

Figure 4. SOM crossplot of classified neurons as seen in Figure 3c. The location of the 2D color map neurons, associated clusters of points in the crossplot, and model reflection coefficients are designated by their associated neuron numbers. Note: Neuron numbering on the 2D color map runs left to right beginning at the bottom left corner.

Figures 3 and 4 demonstrate how only two seismic attributes display natural patterns or clusters that are easy to visualize in a simple model; however, producing compelling and accurate results from real data, which typically incorporates several hundred thousand to millions of data points, requires the combination of several attributes in a SOM analysis which cannot be visualized by a simple 2D or 3D crossplot. Even though humans cannot visualize more than three attributes in a crossplot, mathematically there is no problem in the analysis of these numerous attributes in multidimensional attribute space with SOM. In fact, in a multiattribute (e.g., 5–15 attributes) SOM analysis on real data where there is always a certain amount of noise, it is quite common to get clear distinctive patterns because the geologic features in multidimensional space often stand out above the noise.

What is evident in this analysis is that the conventional limit in resolution based on frequency and wavelets does not apply to a SOM analysis when appropriate seismic attributes are applied. The primary limiting resolution factor in a SOM analysis is sample interval and noise. The following two case studies exhibit the interpretation of thin beds from SOM that have been correlated with well and core results to corroborate the analyses.

Eagle Ford Shale facies case study

seismic attributes for unconventionals

Table 1. Seismic attributes employed for the Eagle Ford case study SOM analysis. These attributes were selected based on principal component analysis results.

The Eagle Ford Shale is a well-known unconventional resource play in Texas. Operators in this play must account for changing stratigraphy and facies to properly locate horizontal wells for optimum perforation intervals. The Eagle Ford stratigraphy is often associated with thin beds and facies well below conventional seismic resolution that change both vertically and laterally. This Eagle Ford case study contains 216 mi2 of enhanced 3D PSTM data processed to a 2 ms sample interval. Conventional vertical resolution (tuning thickness) is 100–150 ft. depending on the location in the Eagle Ford unit. More than 300 wells are available for correlation including 23 type logs, 249 horizontal borehole montages, nine vertical calibration wells with tops, logs, and time-depth corrections. Also available are five cores for which X-ray diffraction and saturation information was available. This well information was incorporated in the corroboration of the SOM results. Sixteen instantaneous seismic attributes were generated from the original amplitude volume. These attributes were input into a principal component analysis (PCA) where 10 attributes were selected for SOM analysis (see Table 1). Figure 5a is a northwest-southeast seismic amplitude line in color raster and wiggle-trace variable area formats across the location of well 6. The figure shows the Austin Chalk-Eagle Ford Group-Buda stratigraphic interval represented by roughly 2.5 peak/trough cycles. In the Eagle Ford, the amplitude appears continuous, yet any details are obscured because of the resolution limitations of the amplitude data where conventional tuning is 100–150 ft. Figure 5b displays in a 3D perspective the results of a SOM analysis that utilized the seismic attributes from Table 1. Sixty-four neurons were employed to identify 64 patterns in the data as seen on the associated 2D color map. A seismic interval from 10 ms below the Buda to 100 ms above the Buda or near the top of the Austin Chalk was chosen for the SOM analysis. Shown clearly is the resolution improvement provided by the SOM analysis compared to the associated seismic amplitude line. The results shown reveal nonlayer cake facies bands that include details in the Eagle Ford’s basal clay-rich shale, high-resistivity and low-resistivity Eagle Ford Shale objectives, the Eagle Ford ash, and the Upper Eagle Ford marl, which are overlain disconformably by the Austin Chalk. This roughly 28 ms (or 14 samples) uses some 26 of the 64 neurons to illuminate the various systems tracts within the Eagle Ford Group for this survey.

seismic display and SOM classification comparison

Figure 5. Resolution comparison between conventional seismic display and a Paradise multiattribute SOM classification. (a) Seismic amplitude profile through the 6 well and (b) the SOM results of the same profile identifying the Eagle Ford group that comprises 26 sample based neuron clusters, which are calibrated to facies and systems tracts. The 2D color map displays the associated neuron cluster colors. Seismic data owned by and provided courtesy of Seitel Inc.

The basal clay shale (BCS) in Figure 5b is resolved distinctly on top of the Buda generally by the #1 neuron in dark gray. Its lithologic and neural uniqueness is concomitant with its distal detrital or pelagic emplacement after deposition of the underlying Buda carbonates. The BCS is distinct also from the overlying regressive downlap of two red neurons (black arrow) and subsequent transgressive (white arrow) of the grayish-gold Eagle Ford high-resistivity organic rich facies.

A previously unresolved, encapsulated, and discontinuous core of rust-colored facies, now evident within the gold section of the lower Eagle Ford Shale is also well resolved in the pilot holes for wells 6 (Figure 5) and 8 (Figure 6). As seen in Figure 6, the logs in well 8 can be correlated directly to the classification and thicknesses of the various units in the Eagle Ford. The specific neurons that identify the various facies are noted in Figure 6b. The expanded display of the SOM results in Figure 7a denotes a low-resistivity gold thin bed that is identified by a single neuron (#55) and is one sample thick (2 ms). The areal distribution of the rust geobody is exhibited in Figure 8a where the SOM classification is displayed 12 ms below and parallel with the top Eagle Ford Shale horizon. Figure 8a shows where the rust facies intersects this horizon, and the associated fairway is relatively wide. Figure 8b denotes the same horizon 4 ms deeper where the fairway becomes narrower. The 2D color map associated with Figures 8a and 8b indicate this rust geobody is delineated by three neurons (circled on 2D color map) and has been calibrated to be the high-resistivity reservoir type. This SOM analysis identifies not only relatively thin facies but also their associated areal distribution.

Callibrating SOM results to well logs

Figure 6. Resolution calibration to well logs for well 8. (a) Profile through the well 8 location displays the SOM results correlated to specific units of this well; (b) well 8 logs identifying members of the Eagle Ford group and correlated with the SOM results of (a). The color code on logs is generalized after neuron clusters for facies tracts from top Buda to top Eagle Ford shale. These include grays that represent the basal clay shale (neuron 1), a rust color band over the high-resistivity laminated dark gold and encapsulated rust geobody target zone (neurons 53, 54, and 60), a thin portion of the lighter gold low resistivity clastic onlap facies (neuron 55), and the two lowresistivity red ashy infill beds (neuron 63 and 64).

The Eagle Ford ash (Figure 7a) lies above the gold and comprises a different red facies than the carbonate downlap; it is resolved as discontinuous and concave or low-seeking fill at the top of the Eagle Ford Shale. The Upper Eagle Ford marl, in purple and magenta colors in Figure 7a, rather than showing simple layers, exhibits updip and downdip facies changes and features that appear to calibrate to facies changes from a basal unit that is clay rich (lavender-neurons #15 and #16) to a slightly more quartz-enriched (magenta-neurons #6 and #8) zone. Organic content may also contribute to these variations. The white dashed line in Figure 7a, located above neuron #6 (magenta), represents the intricate unconformity between the Austin Chalk and the Eagle Ford Group.

Expanded SOM results in seismic interpretation software

Figure 7. Expanded view of SOM results correlated with well 8 logs. (a) Expanded view of SOM profile through well 8 and (b) well 8 logs with Eagle Ford units correlated with the SOM results of (a). Note that the resolution is down to the sample level of 10–12 feet and is illustrated by neuron 55 in (a). The dashed white line in (a) represents the top of the Eagle Ford and base of the Austin Chalk.

Over this survey area, 16 different neurons represent the various facies present in the Eagle Ford Shale over a 14 ms window (70–84 ft). The facies of the entire Eagle Ford Group, which includes the Basal Clay Shale, Eagle Ford Shale, and Eagle Ford marl, are defined by 26 different neurons over 28 ms (210–252 ft). Individual facies units are as thin as one sample interval of 2 ms (10–12 ft). In this Eagle Ford case study area with more than 300 wells (including results from five cores), an interpolation of thin beds between wells may have produced similar thickness results, however, the SOM analysis would have generated the same thin-bed facies results regardless of well control. The significant well information in this study area corroborated the accuracy of the SOM analysis.

Middle Frio onshore Texas case study

In this case study, a well was planned to be drilled offsetting a production unit well, which has produced slightly less than a half million barrels of oil. It was interpreted from well control that a middle Frio sand, locally called the Alibel, appeared to blanket the fault block. There also appeared to be a structural ridge-running strike to a large down-to-the-coast fault over which the Alibel was draped, providing a structural trap for the potential reservoir. To confirm the initial interpretation, eight square miles of seismic encompassing the majority of the fault block was purchased along with digital log curves for three wells from which synthetic seismograms were created to tie the wells accurately to the seismic.

SOM results and geobodies in the Eagle Ford

Figure 8. SOM areal distribution results parallel to the top of the Eagle Ford Shale, 12 and 16 ms below the Eagle Ford, left and right, respectively. The rust geobody is depicted by the same three neurons (60, 58, and 57), but now it can be seen in an areal context. At 12 ms (6 samples) from the top Eagle Ford shale, the uppermost part of the geobody is seen in a wide “fairway;” toward the base some 4 ms deeper, its “fairway” is narrower.

The key well, which is still producing, was perforated across a six-foot interval out of approximately 14 feet of sand at 10,820 feet. The synthetic tie to the data indicated that this perforated interval fell on a weak negative amplitude doublet just below the mapped Alibel horizon (Figure 9). The seismic reflectors are flat below this doublet but show signs of rollover, possibly indicating differential compaction over the perforated zone. The zone of interest produced such a weak seismic amplitude response it could not be mapped effectively, and interpretation of the sand distribution was questionable. Therefore, a SOM analysis was performed to resolve these issues.

Dip line of amplitdue and SOM results

Figure 9. Dip line of amplitude and SOM results through producing well. (a) Amplitude section in color raster format with wiggle-trace variable area overlay; and (b) SOM classification of the same dip line in color raster format with wiggle-trace variable area overlay of the amplitude data. The inset in (b) denotes the 6-foot perforated interval of the producing well correlated to the light to grayish blue neurons.

The SOM analysis incorporated a combination of seismic attributes determined from PCA analysis that would be conducive to find sand, porosity, and hydrocarbons in thin beds. The original enhanced PSTM 3D survey, from which the attributes were generated, was resampled from 4 ms to 2 ms for the purposes of the SOM analysis. These attributes (see Table 2) were input into a SOM analysis where an 8 × 8 topology (64 neurons) was employed. The SOM results were exported to the interpreter’s system where a 1D color bar was employed.

seismic attributes for the Frio

Table 2. Seismic attributes employed for the Frio case study SOM analysis. These attributes were selected based on principal component analysis results.

Figure 9a shows a PSTM amplitude dip line through the productive well with an amplitude overlay. The amplitude response at the pay interval is very weak and difficult to correlate laterally. Figure 9b displays the SOM classification results over the same dip line with an amplitude overlay. It is evident from this figure that the SOM results provide significantly more detail vertically. Only one to two light to grayish blue neurons identify the Alibel sand where six feet were perforated for production in this 14-foot sand. Figure 10 displays a strike line connecting key wells along the fault block. Four wells on the southwest portion of the geobody did not produce significant amounts of hydrocarbons and were thought to have been “mechanical failures.” However, after a review of the sand distribution based upon time slices through the interval (see Figure 11), it was determined that they were isolated in localized areas or on the fringes of the bar system and not connected to the main body of sand. Figure 10a is the PSTM amplitude strike line that exhibits very little amplitude or character in the Alibel interval. This same line in Figure 10b shows the SOM classification, which exhibits the detail and variability of the stratigraphy for this area.

strike line of amplitude and SOM results

Figure 10. Strike line of amplitude and SOM results through producing well and other wells in the area: (a) amplitude section in color raster format with wiggle-trace variable area overlay; and (b) SOM classification of the same strike line in color raster format with wiggle-trace variable area overlay of amplitude data. The neurons defining the Alibel perforated interval in the producing well is situated at the horizon 17 ms below the Alibel horizon.

The horizon mapped near the base of the Alibel sand (see Figures 9 and 10) was flattened, and the SOM classification results are shown in Figure 11. This northeast-southwest trending feature is quite clear, revealing the distribution of the sand. The location of dip and strike lines of Figures 9 and 10 are annotated. Figure 11 displays such detail that a tidal cut is interpreted in the northeast portion of the feature, which may have production implications in the future, related to compartmentalization. The detailed vertical and lateral definition of the Alibel sand by this SOM analysis exhibits thin-bed identification where the sample interval of 2 ms is approximately 10 feet.

SOM classification from flattened horizon

Figure 11. SOM classification from flattened horizon 17 ms below the Alibel horizon. The four wells drilled in the southwest portion of the area are interpreted to be out of or on the fringes of the sand bar. The location of the dip line in Figure 9 and strike line of Figure 10 are annotated.

Conclusions

The seismic interpretation of thin beds below tuning has always been a challenge in our industry. Essentially all of the methods employed for thin-bed delineation have been associated with scaling amplitude or inversion data, which may be inaccurate if rock properties and stratigraphy change below tuning. A multiattribute interpretation approach utilizing SOM analyzes numerous seismic attributes all at once to identify natural patterns in the data, some of which relate to thin beds. Many instantaneous attributes specifically display phenomena at or below tuning and are excellent choices to incorporate in a SOM analysis to define thin beds.

The traditional limitation in the interpretation of thin beds below tuning has been the frequency content of the seismic data involved. Seismic data can have improved processing for imaging, for improving lower S/N, and for processing to a smaller sample rate, but if the frequency content does not change, neither does the vertical resolution or tuning thickness. SOM analysis, however, is not limited by the frequency of the seismic data in a conventional sense.

Since the data points from instantaneous attributes at every sample measure many different components of the total energy in an acoustic wavefield, a SOM analysis utilizing these attributes does not have traditional resolution limitations. In fact, the primary limitation in determining resolution from a SOM analysis is sample interval and noise. Selection of the appropriate instantaneous attributes that have been shown to produce effects below tuning in a SOM analysis has been proven to resolve thin beds as thin as one sample thickness, as evidenced by the two case studies in this paper. It is because a SOM analysis is identifying thin beds and not just the scaling of amplitude or inversion values that the lateral distribution and stratigraphy of thin beds can now be mapped much more accurately. Actual thin-bed distributions from SOM analysis provide tremendous opportunities to improve facies distribution maps, lateral thickness measurements of reservoirs, and interpretation of stratigraphic variation of laterally changing geologic units and features never seen before.

Acknowledgments

The authors would like to thank the staff of Geophysical Insights for the research and development of the machine learning applications used in this paper.

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1 Geophysical Insights.
2 Auburn Energy.

Relating Seismic Interpretation to Reserve / Resource Calculations

Relating Seismic Interpretation to Reserve / Resource Calculations

By: Rocky Roden and Mike Forrest, Rose & Associates, Roger Holeywell, Marathon Oil Company
Published with permission: The Leading Edge
September 2012

In the process of quantifying resources/reserves, geoscientists attempt to employ all the available pertinent information to produce the most accurate results. The presence of direct hydrocarbon indicators (DHI) on seismic data can have a significant impact on the reserve/resource calculations not only for volumes, but also uncertainty levels. In 2001 a consortium of oil companies was organized in an attempt to understand seismic amplitude anomalies interpreted as DHIs and their impact on prospect risking and resource calculations (Roden et al., 2005; Forrest et al., 2010). The geologic setting, seismic and rock physics data quality, DHI characteristics, and calibration of drilling results are all incorporated into a database in a consistent and systematic process. From this process, the evaluation of 217 prospects and associated well results has enabled an in-depth understanding of the relevant key aspects of seismic amplitude anomalies and how they relate to drilling results.

DHI Consortium background

In the DHI Consortium, 41 oil companies have contributed to developing a seismic amplitude analysis process that systematically provides a probability of geologic success (Pg) values and associated implications for resource evaluations (Figure 1). The goals of the DHI consortium over the past 11 years have been and continue to be the following:

  1. Gain a better understanding of how seismic amplitudes impact prospect risking (i.e., predrill chance of geological success or Pg).
  2. Objectively characterize DHI observations using documented occurrences of recoverable hydrocarbons in the subsurface via prospect reviews and risk analysis discussions.
  3. Archive a statistically significant library of drilled prospect results.
  4. Use the prospect database to improve the predictability of Pg and range of uncertainty of hydrocarbon volumes.
  5. Improve the DHI work analysis process to help risk seismic amplitudes and provide an educational tool for interpreters.
  6. Discussion and review of pertinent technologies for amplitude interpretation.

These companies have supplied 217 prospects from around the world with approximately an equal number of successes, dry holes, and objective reservoirs ranging in age from Triassic to Pleistocene. The prospects range in size from less than 100 acres to greater than 10,000 acres and depths from 2000 to more than 20,000 ft. This database is predominantly made up of exploratory wells with only 14% coming from “development” or “extension to known reservoir” wells. The closure types include structural (59%), stratigraphic (26%), and combination structural/stratigraphic (15%) traps.

Critical in the evaluation of DHI characteristics is an understanding of the geologic setting. Because expected DHI responses vary depending on the geologic setting, the prospects in the database are categorized by AVO classes 1–4 as displayed in Figure 4 (Rutherford and Williams, 1989; Castagna et al., 1998). However, 76% of the prospects are class 3 and 22% are class 2, with only a few from class 1 and 4 settings.

A general description of the input information and quantified output for this database as is described in Table 1.

quanitifed workflow

Table 1. General input and quantified output from DHI analysis process. Note that the input parameters are not necessarily linked directly to the corresponding quantified output row.

Well locations for the DHI Consortium

Figure 2. Locations around the world of the 217 wells in DHI Consortium database

There are several ways the most important DHI characteristics can be determined from the well results. The following list is a result of when a DHI characteristic was rated to be a 4 or 5 on a scale of 1–5, with 1 being the worst and 5 the best. These results were then correlated with whether the wells were successful or dry holes. This approach eliminates any scaling or weighting effects and compares only whether the DHI characteristic is strongly correlated with the actual postdrill well results. The top five DHI characteristics have been determined for both class 2 and 3 AVO categories, which relate to different geologic settings, and for which there are a sufficient number of example prospects to be meaningful.

seismic interpretation prospect types 

Figure 3. Prospect types of wells in the DHI Consortium database.

A class 3 AVO setting relates to an unconsolidated gas sand with a gross interval velocity usually less than 8500 ft/s (2650 m/s). These reservoirs typically are low-impedance sands encased in relatively higher-impedance shales. Gas sands in this setting exhibit high amplitudes on the stack section as well as all offsets and angles. The seismic response from the top of these reservoirs produces high amplitudes with respect to background and increase (at times only slightly) with increasing offset/angle (Figure 4).

Top five class 3 DHI characteristics

    1. Amplitude downdip conformance (fit to closure) on stacked or far offset seismic data. This is the top-rated DHI characteristic in the database and describes how well the seismic amplitudes of the anomaly conform to the downdip structural contours of the reservoir. Key issues are whether the structural contours are in time or depth, potential velocity variations that can affect imaging, the accuracy of how the amplitudes were picked (top, bottom, or window) at the anomaly, and consideration of any stratigraphic variations. It is important to remember that this characteristic relates only to the downdip edge of the amplitude anomaly and how it compares to the interpreted structure. It does not relate to an amplitude’s conformance on the flanks of stratigraphic traps. This lateral conformance characteristic in stratigraphic traps depends on the geologic model and the strength of the independent evidence that supports that model.
    2. Phase or character change at the downdip edge of the anomaly. This DHI characteristic relates to a change in character such as phase or frequency at the downdip edge of the anomaly. As the model in Figure 6 displays, the transition from the hydrocarbon leg to the water leg in a sand can produce a seismic response indicative of the edge of a reservoir.
    3. Amplitude consistency in the mapped target area. The internal consistency of the seismic amplitude in a reservoir was found to be a significant DHI characteristic. This relates to the uniformity of the amplitude response within the mapped target area as interpreted from the stacked seismic data (Figure 7). When evaluating this characteristic, consideration should be given to possible faulting and stratigraphic changes that may modify internal consistency.

amplitude change with offset

Figure 4. (top) AVO classes based on amplitude change with offset from the top of gas sands (Rutherford and Williams, 1989; Castagna et al., 1998). (bottom) Angle gathers, zero-offset, and stack responses for typical class 2 and 3 AVO classes.

  1. Flat spots. This characteristic represents the seismic response at a hydrocarbon contact that presumably is relatively flat. This contact can be at the gas/oil contact, oil/ water contact, or gas/water contact. The gross reservoir thickness of the hydrocarbon unit must be greater than the tuning thickness (vertical resolution) to image a flat spot. Extreme care must be taken because the base or edge of channels, low-angle faults, diagenetic boundaries, or even processing artifacts are often misinterpreted as flat spots. Low-saturation gas in a reservoir can also produce a flat spot and this possibility is usually assessed by regional geologic studies.
  2. Excluding possible stacked pays, the AVO response is anomalous compared to events above and below. On prestack data, usually gathers, this characteristic refers to whether there are high-amplitude events above and below the targeted anomaly that look similar (Figure 8). The reasoning is that the anomaly is relatively unique suggesting a hydrocarbon-bearing reservoir.

A class 2 setting contains gas sands more consolidated than class 3 sands with gross interval velocities usually between 8500 ft/s and 12,000 ft/s (2650 m/s and 3650 m/s). The acoustic impedances of these gas sands and encasing shales are about equal. The intercept or near-offset amplitude can vary from a weak positive to a weak negative. The AVO effect can be strongly more negative with offset (large gradient) in these settings (Figure 4).

Top five class 2 DHI characteristics

  1. Amplitude downdip conformance (fit to closure) on far-offset seismic data. The principles behind this characteristic are the same as the top characteristic for class 3 settings, but for class 2 prospects this is usually evaluated on the far offset seismic data.
  2. Consistency in mapped target area (typically on gathers, faroffset/angle stacks, or windowed attributes). Internal consistency of the seismic amplitude identified on far-offset data was found to be an important DHI characteristic for class 2 rocks just as it is for class 3 rocks.
  3. AVO observations using gathers, far-offset/angle stacks, or windowed attributes. This characteristic relates to an interpreter’s confidence that the AVO response is proper for a class 2 setting. In other words, the near-offset is low in amplitude (small peak or trough) and the amplitude increases in negative amplitude with offset. (Figure 4) Noisy gathers, incorrect NMO corrections, multiples, insufficient offset during acquisition, and processing artifacts often complicate evaluation of this characteristic.
  4. The AVO event is anomalous compared to the same event outside the closure. This characteristic describes whether the class 2 AVO response is unique compared to the correlative event outside the closure. This characteristic is often interpreted from far-offset/angle stacks, as well as, intercept, gradient, intercept x-gradient, far-near, and (farnear) x-far displays.
  5. Change in AVO compared to model (wet versus hydrocarbon- filled). Modeling of the AVO response, usually applying Gassman’s equation, typically involves substitution modeling of gas, oil, and water responses. A comparison of the in-situ and modeled responses to actual gathers provides confidence that hydrocarbons are present or not.

amplitude conformance

Figure 5. Examples of amplitude conformance to the downdip structural contours from grade 1 (worst) to grade 5 (best).

Reasons for failure

reservoir hydrocarbon and water logs

Figure 6. Model of hydrocarbon and water legs of a reservoir and location of possible phase or frequency change at edge of fluid contact (courtesy Quantum Earth Corp).

The consortium defines successful wells as geologic successes (i.e., wells with flowable hydrocarbons). However, in this database all successes were determined to also be commercially successful except for a few wells. These exceptions contained from 50 to more than 100 ft of gas, but were determined to be noneconomic because of their location in the world and lack of infrastructure. Wells containing only low-saturation gas are considered by the consortium to be dry holes even though they may represent successful predictions of acoustic response.

Wet sands in the target interval accounted for 49% of the dry holes in the database. Nearly half of these wet sands were thick. In fact, these thick wet sands were greater than the predrill P10 estimates for reservoir thickness. Hard shale on top of a wet sand, blocky sands with higher than expected porosities, and tuning effects of wet sands all produced amplitude anomalies misinterpreted to be direct hydrocarbon effects.

Low-saturation gas (LSG) accounted for 23% for the failures in the database. This well known phenomenon is caused by a small percentage of residual gas in a reservoir (5–10%) producing an acoustic effect similar to commercial saturation and is usually associated with a breach or break in reservoir seal. All but one of the LSG dry holes were in class 3 rocks, with one in a class 2 setting. All but one of the LSG dry holes were in normally pressured sediment columns. Four of the LSG wells exhibited acoustic flat spots indicative of a paleo hydrocarbon contact. Low vertical effective stress caused by undercompaction accounted for several LSG results in deepwater settings, but was also found where a relatively young sediment column overlaid the amplitude anomaly. Even though not dry holes, several wells exhibited hydrocarbons, usually gas, overlying a LSG column in the sand.

No reservoir present was responsible for 17% of the dry holes. Amplitudes misinterpreted to be hydrocarbons included low-density shale, ash, coal beds, top of hard overpressures, seismic processing artifacts, mud volcano, and diagenetic boundary.

Tight reservoirs accounted for 11% of the dry holes in the database. Examples of these include oil-charged marl, gas-charged condensed section, low-impedance siltstone/mudstone and low-permeability reservoir.

DHI anomaly

Figure 7. Examples of amplitude consistency within a defined DHI anomaly from grade 1 (worst) to grade 5 (best).

Implications for resource calculations in exploration

The presence of direct hydrocarbon indicators can have a significant impact on methods to compute prospective resources, especially in exploration settings. How do DHIs change area determination for resources? How do DHIs impact thickness determinations in resource computations? How does a seismic-amplitude (DHI) defined area and thickness relate to a geologically defined area and thickness? The presence of true direct hydrocarbon indicators indicates that all geologic chance factors (e.g., source, migration, seal, reservoir, and trap) are working to form a petroleum system; therefore, the critical issue is determining the confidence level that the seismic is truly displaying a direct hydrocarbon indicator because not all anomalous amplitude events are DHIs.

AVO anomaly

Figure 8. An unmuted CDP gather displaying an AVO anomaly that appears prominent compared to the events above and below (Roden et al., 2005).The near vertical lines represent offset angles (purple = 10°, red = 20°, green = 30°).

DHI Index

Figure 9. Illustration of DHI Index method to define and weight geologic and amplitude areas for resource calculations

For area determinations, one approach is to consider two separate distribution estimates. One distribution is based on geologic evidence, independent of the amplitude as a DHI, while the other distribution is based on the amplitude-defined area (Figure 9). Each distribution can be defined by a P10 (largest reasonable) and a P90 (smallest reasonable) value. The variance or P10/P90 ratio of the area is often large for the geologic distribution, but much smaller for the amplitude-defined distribution because the anomaly usually has a sharp cutoff. From statistics based on the DHI consortium results, a “DHI Index” has been calculated that indicates the likelihood that the interpreted amplitude anomaly is truly a direct hydrocarbon indicator. This “DHI Index” can be used as a relative weighting factor for the two distribution estimates.

seismic interpretation software - DHI Index

Figure 10. DHI Index values color-coded by drilling outcomes. Note that above 20% almost all wells are successful. There are negative DHI Index values, usually associated with dry holes, where DHI characteristics should be present, but were absent.

The histograms of Figure 10 show that, when the DHI Index is more than 20%, essentially all wells are successful. To get a DHI Index of 20% or higher requires having numerous positive DHI characteristics. Therefore, using this logic, prospects with a DHI Index of 20% or larger employ the DHI-defined area, whereas, a DHI Index of 0% or less employ the geologically defined area (Figure 9). A DHI Index of 0% or less indicates there is no DHI element at all in the area determination. When a prospect has a DHI Index between 0% and 20%, the area is defined by a simple linear weighting (e.g., a DHI Index of 10% would have a 50/50 weighting of geology and amplitude-defined areas) between the two distribution estimates.

For thickness determinations, similar to the DHI Index approach for area, two thickness distributions can be estimated. One is defined geologically and one by the amplitude-de- fined thickness. The geologically defined distribution should be based on well control, sand studies, depositional trends, and usually involves constructing regional and local sand isopachs consistent with the depositional model in terms of reservoir shape and thickness. From the combination of sand isopachs, depositional environment determinations, models and stratigraphy studies, an estimate of the P10 and P90 average net pay thicknesses can be calculated.

For the amplitude-defined thickness, the interpreter first needs to determine whether the anomaly is above tuning, below tuning, or both. Above tuning, the thickness is a function of the time separation between the top and bottom of the anomaly. Gross pay maps (isochrons) can be generated for the P10 and P90 cases including any geometric factors. An estimated net-to-gross and interval velocity are applied to determine the final thickness distributions. Below tuning, in the ideal situation, the composite amplitude from the top and bottom of the bed decreases linearly with thickness starting from tuning thickness. The amplitude should be calibrated with well control and stratigraphy studies. Below tuning, a starting point P10 could be the tuning thickness and a P90 could be the detection limit (approximately 1/30 wavelength of the dominant frequency). Appropriate interval velocities may need to be applied for thickness conversions. Below tuning, the composite amplitude usually incorporates any net-to-gross effects. Once a geologically defined thickness distribution and a seismic amplitude-defined thickness are determined, the DHI Index can be used to determine the weighting of these two distributions. It can be more difficult to separate and differentiate between a geologically and amplitude-defined thickness as compared to interpreting distributions for the area. In this situation, it may be more appropriate to combine geologic and amplitude thickness information and use one distribution without weighting to compute resources. Spectral decomposition has been found to be helpful in certain situations to help determine thickness trends.

Employing the DHI Index method to determine area and thickness for resource calculations is one approach that gives credit to both geologically and amplitude defined distributions. However, it is not uncommon in strong amplitude driven plays to employ the amplitude-defined distributions only, especially for area. The logic here is that the presence of amplitudes is defining the reservoir and the geologic distributions do not apply. Care should be taken in that all amplitudes are not DHIs and not all geologic settings exhibit DHIs equally. In addition, even if there is evidence that the area can be narrowly estimated, there will often be significant remaining uncertainty in resource size because of the variance in average net pay thickness and recovery factor estimates.

Implications for reserve calculations

The U. S. Securities and Exchange Commission (SEC) defines “proved oil and gas reserves” in part as “those quantities of oil and gas, which, by analysis of geosciences and engineering data, can be estimated with reasonable certainty to be economically producible— from a given date forward, from known reservoirs, and under existing economic conditions, operating methods, and government regulations — prior to the time at which contracts providing the right to operate expire, unless evidence indicates that renewal is reasonably certain, regardless of whether deterministic or probabilistic methods are used for the estimation.” Unlike resource calculations in exploration, reserve calculations have had a well or wells drilled to confirm geologic conditions and to some degree seismic amplitude features. Therefore the interpretation of DHI features relates to the determination of “reliable technology” as defined by the SEC. As defined by the SEC, “reliable technology is a grouping of one or more technologies (including computational methods) that has been field tested and has been demonstrated to provide reasonably certain results with consistency and repeatability in the formation being evaluated or in an analogous formation.” Therefore for DHI characteristics to qualify as reliable technology requires the correlation of amplitude features with well control, reservoir engineering tests, known geologic trends and stratigraphy.

Even with well control and substantial geologic control of thickness and area extents, care should be taken in incorporating DHI information from seismic data. Considerations for amplitude anomalies and reserve determinations include:

  1. Strong amplitude is primarily at wedge where tuning occurs, weaker amplitude updip
  2. Stratigraphic trap where conventional downdip and updip limits do not apply
  3. Reservoir thins updip, bald structure
  4. Velocity tilt can affect conformance to closure interpretations
  5. Faults affecting amplitudes for area determinations
  6. Stratigraphic changes across field
  7. Masking of amplitudes from bright spots above prospect
  8. Amplitude effects from low-saturation gas downdip of proven pay in the same reservoir
  9. Beware of large column heights for gas reservoirs
  10. Data issues, such as prospect at edge of survey, complicate area estimates

In development, seismic technologies such as spectral decomposition, neural networks, statistical approaches, and advanced seismic inversion techniques are often applied to help determine reserves. The transition from exploration to development and the use of seismic inversion requires the incorporation of all available geologic, seismic, and engineering data in an attempt to discriminate specifically between lithology, porosity, and fluid effects and how these parameters relate to reserve calculations.

Conclusions

Over the past 11 years, 41 oil companies have contributed data for the evaluation of more than 200 drilled prospects (including assessment of the associated technical information and drilling results). The subsequent database developed from these assessments has enabled the identification of the pertinent aspects of direct hydrocarbon indicators and their impact on risking and resource/reserve determination. This database is composed primarily of exploratory wells (86%) and has direct relevance for computing resources in exploration. However, the lessons learned from this database are also pertinent for reserve assessment in development because an understanding of the most important DHI characteristics, reasons for failure, and potential pitfalls in DHI interpretation provide valuable information in the reserve assessment process. Critical in the evaluation of DHI characteristics is the pertinent technical approaches employed and their calibration to known drilling results. However, even in development settings with a certain amount of well control for calibration, DHI interpretation requires a consistent and systematic evaluation procedure, including recognition of potential pitfalls and an understanding of the cause of the DHIs and their significance in reserve assessment.

References

Castagna, J., H. Swan, and D. Foster, 1998, Framework for AVO gradient and intercept interpretation: Geophysics, 63, no. 3, 948– 956.

Forrest, M., R. Roden, and R. Holeywell, 2010, Risking seismic amplitude anomaly prospects based on database trends: The Leading Edge, 29, 936–940.

Roden, R., M. Forrest, and R. Holeywell, 2005, The impact of seismic amplitudes on prospect risk analysis: The Leading Edge, 24, no. 7, 706–711.

Roden, R., J. Castagna, and G. Jones, 2005, The impact of prestack data phase on the AVO interpretation workflow-A case study: The Leading Edge, 24, 890–895.

Rutherford, S. E. and R. H. Williams, 1989, Amplitude versus offset variation in gas sands: Geophysics, 54, no. 6, 680–688.

U. S. Securities and Exchange Commission, 2008, Modernization of oil and gas reporting, December 31.

Acknowledgments: The authors thank the member companies of the DHI Consortium for providing invaluable information necessary to develop the resulting interpretation process and prospect database.