Direct Hydrocarbon Indicators Archives

Future of Seismic Interpretation with Machine Learning and Deep Learning

13 February 2019 | Blog

By: Iván Marroquín, Ph.D. – Senior Research Geophysicist

I am very excited to participate as a speaker in the workshop on Big Data and Machine Learning organized by European Association of Geoscientists & Engineers. My presentation is about using machine learning and deep learning to advance seismic interpretation process for the benefit of hydrocarbons exploration and production.

Companies in the oil and gas industry invest millions of dollars in an effort to improve their understanding of their reservoir characteristics and predict their future behavior. An integral part of this effort consists of using traditional workflows for interpreting large volumes of seismic data. Geoscientists are required to manually define relationships between geological features and seismic patterns. As a result, the task of finding significative seismic responses to recognize reservoir characteristics can be overwhelming.

In this era of big data revolution, we are at the beginning of the next fundamental shift in seismic interpretation. Knowledge discovery, based on machine learning and deep learning, supports geoscientists in two ways. First, it interrogates volumes of seismic data without preconceptions. The objective is to automatically find key insights, hidden patterns, and correlations. So then, geoscientists gain visibility into complex relationships between geologic features and seismic data. To illustrate this point, Figure 1a shows a thin bed reservoir scenario from Texas (USA). In terms of seismic data, it is difficult to discern the presence of the seismic event associated with the producing zone at well location. The use of machine learning to derive a seismic classification output (Figure 1b) brought forward a much rich stratigraphic information. Upon closer examination using time slice views (Figure 1c), it is indicated that the reservoir is an offshore bar. Note how well oil production matches the extent of the reservoir body.

Figure 1. Seismic classification result using machine learning (result provided by Deborah Sacrey, senior geologist with Geophysical Insights).

Another way knowledge discovery can help geoscientists is to automate elements of seismic interpretation process. At the rate machine learning and deep learning can consume large amounts of seismic data, it makes possible to constantly review, modify, and take appropriate actions at the right time. With these possibilities, geoscientists are free to focus on other, more valuable tasks. The following example demonstrates that a deep learning model trained can be trained on seismic data or derived attributes (e.g., seismic classification, instantaneous, geometric, etc.) to identify desired outcomes, such as fault locations. In this case, a seismic classification volume (Figure 2a) was generated from seismic amplitude data (Taranaki Basin, west coast of New Zealand). Figure 2b shows the predicted faults displayed against the classification volume. To corroborate the quality of the prediction, the faults are also displayed against the seismic amplitude data (Figure 2c). It is important to note that the seismic classification volume provides an additional benefit to the process of seismic interpretation. It has the potential to expose stratigraphic information not readily apparent in seismic amplitude data.

Figure 2. Fault location predictions using deep learning (result provided by Dr. Tao Zhao, research geophysicist with Geophysical Insights).

Interpretation of DHI Characteristics with Machine Learning

15 May 2017 | Recommended, Technical Papers

By: Rocky Roden and ChingWen Chen, Ph.D.
Published with permission: First Break
Volume 35, May 2017

Introduction

In conventional geological settings, oil companies routinely evaluate prospects for their drilling portfolio where the process of interpreting seismic amplitude anomalies as Direct Hydrocarbon Indicators (DHIs) plays an important role. DHIs are an acoustic response owing to the presence of hydrocarbons and can have
a significant impact on prospect risking and determining well locations (Roden et al., 2005; Fahmy 2006; Forrest et al., 2010; Roden et al., 2012; Rudolph and Goulding, 2017). DHI anomalies are caused by changes in rock physics properties (P and S wave velocities, and density) typically of the hydrocarbon-filled
reservoir in relation to the encasing rock or the brine portion of the reservoir. Examples of DHIs include bright spots, flat spots, character/phase change at a projected oil or gas/water contact, amplitude conformance to structure, and an appropriate amplitude variation with offset on gathers. Many uncertainties should be considered and analyzed in the process of assigning a probability of success and resource estimate range before including a seismic amplitude anomaly prospect in an oil company’s prospect portfolio (Roden et al., 2012).

Seismic amplitude anomalies that are DHIs have played a major role in oil and gas exploration since the early 1970s (Hilterman, 2001). The technology and methods to identify and risk seismic amplitude anomalies have advanced considerably over the years, especially with the use of AVO (Amplitude vs. Offset) and improved acquisition and processing seismic technology (Roden et al., 2014). The proper evaluation of seismic direct hydrocarbon indicators for appropriate geologic settings has proven to have a significant impact on risking prospects. Rudolph and Goulding (2017) indicate from an ExxonMobil database of prospects that DHI-based prospects had over twice the success rate of non-DHI prospects on both a geologic and economic basis. In an industry-wide database of DHI prospects from around the world, Roden et al. (2012) indicate that when a prospect has a >20% DHI Index, a measure of the risk associated with DHI characteristics, almost all the wells were successful. Even with the use of advanced seismic technology and well-equipped interpretation workstations, the interpretation of DHI characteristics is not always easy or straightforward.

A key technology employed in evaluating potential DHI features is seismic attributes. Seismic attributes are any measurable property of seismic data including stacked or prestack data. Seismic attributes can be computed on a trace, multiple traces, on an entire volume, over isolated windows, on a horizon, and in either
time or depth. There are hundreds of seismic attributes generated in our industry (Brown, 2004; Chen and Sidney, 1997; Chopra and Marfurt, 2007; Taner, 2003) and can be generally categorized as instantaneous, geometric, AVO, seismic inversion, and spectral decomposition attributes. The instantaneous, AVO, and inversion attributes are typically utilized to highlight and identify DHI features. For example, amplitude envelope, average energy, and sweetness are good instantaneous attributes to display how amplitudes stand out above the background, potentially identifying a bright spot and a potential hydrocarbon accumulation. AVO attributes such as intercept times gradient, fluid factor, Lambda/Mu/Rho and far offset-minus near offset-times the far offset can help to identify hydrocarbon-bearing reservoirs (Roden et al., 2014). However, not all amplitude anomalies are DHIs and interpreting numerous seismic attributes can be complicated and at times confusing. In addition, it is almost impossible for geoscientists to understand how numerous seismic attributes (>3) interrelate.

Over the last few years, machine learning has evolved to help interpreters handle numerous and large volumes of data (e.g. seismic attributes) and help to understand how these different types of data relate to each other. Machine learning uses computer algorithms that iteratively learn from the data and independently adapt to produce reliable, repeatable results. We incorporate a machine learning workflow where principal component analysis (PCA) and self-organizing maps (SOM) are employed to analyze combinations of seismic attributes for meaningful patterns that correspond to direct hydrocarbon indicators. A machine learning multi-attribute approach with the proper input parameters can help interpreters to more efficiently and accurately evaluate DHIs and help reduce risk in prospects and projects.

Interpreting DHIs

Important DHI Characteristics

Table 1 Most important DHI characteristics for AVO classes 2 and 3 as determined by Forrest et al. (2010) and Roden et al. (2012)

DHI characteristics are usually associated with anomalous seismic responses in a trapping configuration: structural traps, stratigraphic traps, or a combination of both. These include bright spots, flat spots, amplitude conformance to structure, etc. DHI anomalies are also compared to other features such as models, similar events, background trends, proven productive anomalies, and geologic features. DHI indicators can also be located below presumed trapped hydrocarbons where shadow zones or velocity pull-down effects may be present. DHI effects can even be present dispersed in the sediment column in the form of gas chimneys or clouds. Forrest et al. (2010) and Roden et al. (2012) have documented the most important DHI characteristics based on well success rates in an industry-wide database of DHI prospects. Other than the amplitude strength above background (bright spots), Table 1 lists these DHI characteristics by AVO classes 2 and 3. These two AVO classes (Rutherford and Williams, 1989) relate to the amplitude with offset response from the top of gas sands which represent the specific geologic settings where most DHI characteristics are found. Therefore, the application of machine learning employing seismic multi-attribute analysis may help to clearly define DHI characteristics and assist the interpreter in making a more accurate assessment of prospect risk.

Class 3 DHI Characteristics

Table 2 Most important Class 3 DHI characteristics as denoted by Forrest et al. (2010) and Roden et al. (2012) and a designation of typical instantaneous attributes that identify these characteristics. Not all instantaneous attributes by themselves are conducive to identifying the top DHI characteristics.

Multi-attribute machine learning workflow

With the goal of identifying DHI characteristics, an interpreter must determine the specific attributes to employ in a machine learning workflow. A geoscientist can select appropriate attributes based on their previous knowledge and experience to define DHIs in a specific play or trend. Table 2 lists several common instantaneous attributes and the associated stacked seismic data DHI characteristics they tend to identify. These relationships are of course subjective and depend on the geologic setting and data quality. Class 3 DHIs are usually interpreted on full stack volumes and/or offset/angle volumes and their associated derivative products. Class 2 DHIs are typically interpreted on offset/angle volumes (especially far offset/angle volumes), gathers, and their associated derivative products including various types of crossplots. The relationships between attributes and DHI characteristics can be variable depending on the geologic setting and the seismic data quality. If it is unclear which attributes to employ, principal component analysis (PCA) can assist interpreters. PCA is a linear mathematical technique to reduce a large set of variables (seismic attributes) to a smaller set that still contains most of the variation of independent information in the larger set. In other words, PCA helps to determine the most meaningful seismic attributes.
The first principal component accounts for as much of the variability in the data as possible and each succeeding component (orthogonal to each preceding) accounts for as much of the remaining variability. Given a set of seismic attributes generated from the same original volume, PCA can identify combinations of attributes producing the largest variability in the data suggesting these combinations of attributes that will better identify specific geologic features of interest and in this case specific DHI characteristics. Even though the first principal component represents the largest linear attribute combinations that best represents the variability of the bulk of the data, it may not identify specific features of interest to the interpreter. The interpreter should also evaluate succeeding principal components because they may be associated with DHI characteristics not identified with the first principal component. In fact, the top contributing seismic attributes from the first few principal components, when combined, often produce the best results for DHI delineation. In other words, PCA is a tool that, employed in an interpretation workflow with a geoscientist’s knowledge of DHI related attributes, can give direction to meaningful seismic attributes and improve interpretation results. It is logical, therefore, that a PCA evaluation may provide important information on appropriate seismic attributes to take into a self-organizing map generation.

After appropriate seismic attributes have been selected, the next level of interpretation requires pattern recognition and classification of often subtle information embedded in the seismic attributes. Taking advantage of today’s computing technology, visualization techniques, and understanding of appropriate parameters, self-organizing maps (SOMs) efficiently distill multiple seismic attributes into classification and probability volumes (Smith and Taner, 2010; Roden et al., 2015). Developed by Kohonen in 1982 (Kohonen, 2001), SOM is a powerful non-linear cluster analysis and pattern recognition approach that helps interpreters to identify patterns in their data that can relate to geologic features and DHI characteristics. The samples for each of the selected seismic attributes from the desired window in a 3D survey are placed in attribute space where they are normalized or standardized to the same scale. Also in attribute space are neurons, which are points in space that start at random locations and train from the attribute data and mathematically hunt for natural clusters of information in the seismic data. After the SOM analysis, each neuron will have identified a natural cluster as a pattern in the data. These clusters reveal significant information about the classification structure of natural groups that are difficult to view any other way. In addition to the resultant classification volume, a probability volume is also generated which is a measure of the Euclidean distance from a data point to its associated winning neuron (Roden et al., 2015). The winning neuron is the one that is nearest to the data point in attribute space. It has been discovered that a low classification probability corresponds to areas that are quite anomalous as opposed to high probability zones that relate to regional and common events in the data.

To interpret the SOM classification results, each neuron is displayed in a 2D color map. Highlighting a neuron or combination of neurons in a 2D color map identifies their associated natural clusters or patterns in the survey because each seismic attribute data point retains its physical location in the 3D survey. The identification of these patterns in the data enables interpreters to define geology not easily interpreted from conventional seismic amplitude displays alone. These visual cues are facilitated by an interactive workstation environment.

Low probability anomalies

After the SOM process and natural clusters have been identified, Roden et al. (2015) describe the calculation of a classification probability. This probability estimates the probable certainty that a winning neuron classification is successful. The classification probability ranges from zero to 100% and is based on goodness of fit of the Euclidean distances between the multi-attribute data points and their associated winning neuron. Those areas in the survey where the classification probability is low correspond to areas where no winning neurons fit the data very well. In other words, anomalous regions in the survey are noted by low probability. DHI characteristics are often associated with low classification probabilities because they are anomalous features that are usually not widespread throughout the survey.

SOM analysis for Class 3 DHI characteristics

A class 3 geologic setting is associated with low acoustic impedance reservoirs that are relatively unconsolidated. These reservoirs typically have porosities greater than 25% and velocities less than 2700 m/sec. The following DHI characteristics are identified by multi-attribute SOM analyses in an offshore Gulf of Mexico class 3 setting. This location is associated with a shallow oil and gas field (approximately 1200 m) in a water depth of 140 m that displayed a high seismic amplitude response. Two producing wells with approximately 30 m of pay each were drilled in this field on the upthrown side of an east-west trending normal fault. Before these wells were drilled, operators had drilled seven unsuccessful wells in the area based on prominent seismic amplitudes that were either wet or low saturation gas. Therefore, the goal was to identify as many DHI characteristics as possible to reduce risk and accurately define the field and to develop SOM analysis approaches that can help to identify other possible prospective targets in the area.

Initially, 20 instantaneous seismic attributes were run through a PCA analysis in a zone 20ms above and 150 ms below the top of the mapped producing reservoir. Based on these PCA results, various combinations of attributes were employed in different SOM analyses with neuron counts from 3X3, 5X5, 8X8, 10X10, and 12X12 employed for each set of seismic attributes. It is from this machine learning multi-attribute interpretation workflow that the results defining different DHI characteristics were interpreted and described below. All of the figures associated with this example are from a SOM analysis with a 5X5 neuron count and employed the instantaneous attributes listed below.

Sweetness
Envelope
Instantaneous Frequency
Thin Bed
Relative Acoustic Impedance
Hilbert
Cosine of Instantaneous Phase
Final Raw Migration

SOM classification of a reservoir

Figure 1 From the top of the producing reservoir: a) time structure map in contours with an amplitude overlay in colour and b) SOM classification with low probability less than 1% denoted by white areas. The yellow line in b) represents with downdip edge of the high amplitude zone designated in a).

Figure 1a displays a time structure map as denoted by the contours with an amplitude overlay (color) from the mapped top of the reservoir in this field. The horizon at the top of the reservoir was picked on a trough (low impedance) on zero phase seismic data (SEG normal polarity). Figure 1a indicates that there is a relatively good amplitude conformance to structure based on the amplitude. Figure 1b is a display of classification probability from the SOM analysis at the top of the reservoir at the same scale as Figure 1a. This indicates that the top of this reservoir exhibits an anomalous response from the SOM analysis where any data points with a probability of less than 1% are displayed in the white areas. In comparing Figure 1a and 1b it is apparent that the low probability area corresponds closely to the amplitude conformance to structure as denoted by the yellow outline in Figure 1b. This confirms the identification of the productive area with low probability and proves the efficacy of this SOM approach. The consistency of the low probability SOM response in the field is another positive DHI indicator. In fact, the probabilities as low as .01% still produce a consistent response over the field indicating how the evaluation of low probability anomalies is critical in the interpretation of DHI characteristics.

This field contains oil with a gas cap and before drilling, there were hints of possible flat spots suggesting hydrocarbon contacts on the seismic data, but the evidence was inconsistent and not definitive. Figure 2 displays a north-south vertical inline profile through the middle of the field and its location is denoted in Figure 1. Figure 2a exhibits the initial stacked amplitude data with the location of the field annotated. Figure 2b denotes the SOM analysis results of this same vertical inline 9411 which incorporated the eight instantaneous attributes listed above in a 5X5 neuron matrix. The associated 2D color map in Figure 2b denotes the 25 natural patterns or clusters identified from the SOM process. It is apparent in this figure that the reservoir and portions of the gas/oil contact and the oil/water contact are easily identified. This is more easily seen in Figure 2c where the 2D color map indicates that the neurons highlighted in grey (20 and 25) are defining the hydrocarbon-bearing portions of the reservoir above the hydrocarbon contacts and the flat spots interpreted as hydrocarbon contacts are designated by the rust-colored neuron (15). The location of the reservoir and hydrocarbon contacts are corroborated by well control. The southern edge of the reservoir is revealed in the enlargements of the column displays on the right. Downdip of the field is another undrilled anomaly defined by the SOM analysis that exhibits similar DHI characteristics identified by the same neurons.

Figure 2 North-south vertical profile 9411 through the middle of the field: a) stacked seismic amplitude display with the field location designated, b) SOM classification with 25 neurons indicated by the 2D colour map over a 170 ms window, and c) three neurons highlighting the reservoir above the oil/water and gas/oil contacts and the hydrocarbon contacts (flat spots). The expanded insets denote the details from the SOM results at the downdip edge of the field.

Figure 3 West-east vertical profile 3183 through the field: a) stacked seismic amplitude display denoting tie with line 9411, b) SOM classification with 25 neurons indicated by the 2D colour map, and c) three neurons highlighting the gas/oil and oil/water contacts and the hydrocarbon contacts (flat spots). The expanded insets clearly display the edge of the field in the SOM classifications.

West to east crossline 3179 over the field is displayed in Figure 3 and with it the location designated in Figure 1. The stacked seismic amplitude display of Figure 3a indicates that its tie with inline 9411 is located in the updip portion of the reservoir where there is an apparent gas/oil contact. Figure 3b exhibits the SOM results of this west-east line utilizing 25 neurons as designated by the 2D color map. Similar to Figure 2b, Figure 3b indicates that the SOM analysis has clearly defined the reservoir by the grey neurons (20 and 25) and the hydrocarbon contacts in the rust-colored neuron (15). Towards the west, the rust-colored neuron (15) denotes the oil/water contact as defined by the flat spot on this crossline. Figure 3c displays only neurons 15, 20, and 25 to clearly define the reservoir, its relationship above the hydrocarbon contacts, and the contacts themselves. The three enlargements on the left are added for detail.

What is very evident from the SOM results in both Figures 2 and 3 is a clear character change and definition of the downdip edges of the reservoir. The downdip edge definition of an interpreted trap is an important DHI characteristic that is clearly defined by the SOM analysis in this field. The expanded insets in Figures 2 and 3 indicate that the SOM results are producing higher resolution results than the amplitude data alone and the edge terminations of the field are easily interpreted. These results substantiate that the SOM process with the appropriate set of seismic attributes can exhibit thin beds better than conventional amplitude data.

SOM analysis for Class 2 DHI characteristics

A class 2 geologic setting contains reservoirs more consolidated than class 3 and the acoustic impedance of the reservoirs are about equal to the encasing sediments. Typical porosities range from 15 to 25% and velocities 2700-3600 m/sec for these reservoirs. In class 2 settings, AVO attributes play a larger role in the evaluation of DHI characteristics than in class 3 (Roden et al., 2014). This example is located onshore Texas and targets Eocene sands at approximately 1830 m deep. The initial well B was drilled just downthrown on a small southwest-northeast regional fault, with a subsequent well drilled on the upthrown side (Well A). The reservoirs in the wells are approximately 5-m thick and composed of thinly laminated sands. The tops of these sands produce a class 2 AVO response with near zero amplitude on the near offsets and an increase in negative amplitude with offset (SEG normal polarity).

Figure 4 Time structure map at the top of the producing Eocene reservoir.

The goal of the multi-attribute analysis was to determine the full extent of the reservoirs revealed by any DHIs because both wells were performing much better than the size of the amplitude anomaly indicated from the stack and far offset seismic data. Figure 4 is a time-structure map from the top of the Eocene reservoir. This map indicates that both wells are located in stratigraphic traps with Well A situated on southeast dip and Well B located on the northwest dip that terminates into the regional fault. The defined anomaly conformance to downdip closure cannot be seen in the Well A reservoir because the areal extent of the reservoir is in a north-south channel and the downdip conformance location is
very narrow. In the Well B reservoir, the downdip edge of the reservoir actually terminates into the fault so an interpretation of the downip conformance cannot be determined. The updip portion of the reservoir at Well B actually thins out towards the south-east forming an updip seal for the stratigraphic trap. The Well B reservoir was interpreted to have a stacked data amplitude anomaly of approximately 70 acres and the Well A reservoir was determined to only have an amplitude anomaly of only about 34 acres (Figure 5a).

Figure 5 At the top of the Eocene reservoir: a) stacked seismic amplitude, b) SOM classification with 64 neurons, and c) same classification as the middle display with low probability of less than 30% designated by the white areas.

Figure 6 North-south arbitrary line through Wells A and B with the location designated in Figure 4: a) stacked seismic amplitude display, b) SOM classification with 64 neurons indicated by the 2D colour map, and c) SOM classification with only four neurons in grey highlighting both the reservoirs associated with the wells.

The gathers associated with the 3D PSTM survey over this area were conditioned and employed in the generation of very specific AVO attributes conducive to the identification of class 2 AVO anomalies in this geologic setting. The ten AVO attributes used for the SOM analysis were selected from a PCA analysis of 18 AVO attributes. The AVO attributes that were selected for the SOM analysis are listed below:

Far – Near
Shuey 2 term approximation – Intercept
Shuey 2 term approximation – Gradient
Shuey 2 term approximation – 1/2 (Intercept + Gradient)
Shuey 2 term approximation – 1/2 (Intercept – Gradient)
Shuey 3 term approximation – Intercept
Shuey 3 term approximation – Gradient
Shuey 3 term approximation – 1/2 (Intercept + Gradient)
Verm-Hilterman approximation – Normal Incident
Verm-Hilterman approximation – Poisson’s Reflectivity

Several different neuron counts were generated with these ten AVO attributes and the results in the associated figures are from the 8X8 (64 neurons) count. Figure 5b displays the SOM results from the top of the Eocene reservoirs. The associated 2D color map indicates that neurons 47, 58, 62, and 63 are defining the reservoirs drilled by the two wells. Comparing the areal distribution of the amplitude defined reservoirs in 5a to the SOM defined reservoirs in Figure 5b indicates that the later is larger. In fact, the Well A amplitude defined area of 34 acres is compared to approximately 95 acres as denoted by four neurons in Figure 5b. The Well B amplitude defined reservoir area was determined to be 70 acres, whereas, the SOM defined area was determined to be approximately 200 acres. The SOM defined areal distributions were determined to be consistent with engineering and pressure data in the two wells. The anomaly consistency in the mapped target area is evident in Figure 5b and is better in defining the extent of the producing reservoirs than amplitudes.

Figure 5c displays the SOM results of 5b. However, less than 30% of the low classification probability results are displayed in white. It denotes that the core of the reservoirs at each of the well locations reveal low probability. Low probability is defining anomalous zones based on the ten AVO attributes run in the SOM classification process.

Figure 7 Northeast-southwest inline 2109 through Well B with location designated in Figure 4: a) stacked seismic amplitude display, b) SOM classification with 64 neurons as denoted by the 2D colour map, and c) SOM classification with only four grey neurons highlighting the reservoir at Well B. The expanded insets display the updip edges of the reservoir with the SOM results clearly defining the updip seal edge of the field.

Figure 6 is a north-south arbitrary line running through both Wells A and B with its location denoted in Figure 4. Figure 6a is the conventional stacked seismic amplitude display of this line. Figure 6b displays the SOM results and the reservoirs at both wells defined by neurons 47, 58, 62, and 63. In Figure 6c only these four neurons are turned on defining the location of the reservoirs on this line. The four neurons are clearly defining the field and the southern downdip limits of the reservoir associated with Well A and the updip limits of the reservoir at Well B where the sands are thinning out to the south. Figure 7 is northwest-southeast inline 2109 with its location denoted in Figure 4. Figure 7a is the stacked amplitude display and Figure 7b displays the SOM results defining the limits of the Well B reservoir as it terminates at the fault to the northwest. Figure 7c with only the four neurons defining the reservoir displayed indicates the thinning out of the reservoir updip much more clearly than with amplitudes alone. The insets of Figures 7b and 7c illustrate the details in the updip portion of the reservoir defined by the SOM process. The SOM analysis incorporates ten AVO attributes and is not limited by conventional amplitude/frequency limitations of thickness and areal distribution. The AVO attributes selected for this SOM analysis are specifically designed to bring out the appropriate AVO observations for a class 2 setting. It is clear from these results that the AVO attributes in this SOM analysis are clearly distinguishing the anomalous areas associated with the producing reservoirs from the equivalent events and zones outside these stratigraphic traps.

Conclusions

For more than 40 years seismic amplitudes have been employed to interpret DHIs in an attempt to better define prospects and fields. There are dozens of DHI characteristics associated primarily with class 2 and 3 geologic settings. Hundreds of seismic attributes have been developed in an effort to derive more information from the original seismic amplitude data and further improve DHI interpretations. A machine learning workflow incorporating seismic attributes, PCA, and SOM, has been proven to produce excellent results in the interpretation of DHIs. This machine learning workflow was applied to data in class 2 and 3 reservoirs in an effort to interpret the most important DHI characteristics as defined by a worldwide industry database. The SOM analysis employing instantaneous attributes in a class 3 setting successfully identified the top DHI characteristics and especially those defining edge effects and hydrocarbon contacts (flat spots). AVO attributes conducive to providing information in class 2 settings incorporated in a SOM analysis allowed the interpretation of DHI characteristics that better defined the areal extent of the producing reservoirs than amplitudes by clearly denoting the stratigraphic trap edges.

Low SOM classification probabilities have been proven to help identify DHI characteristics. These low probabilities relate to data regions where the attributes are very different from the data points of all of the attributes in the SOM analysis and their associated winning neurons, which has defined a natural cluster or pattern in the data. Anomalous regions in the data, for example, DHI characteristics, are noted by low probability. This analytical approach of defining low probabilities proved to be helpful in identifying DHI characteristics in both class 2 and 3 settings.

An important observation in these two case studies is that the use of appropriate seismic attributes in a SOM analysis can not only identify DHI characteristics not initially interpreted but can also increase or decrease confidence in already identified characteristics. This multi-attribute machine learning workflow provides a methodology to produce more accurate identification of DHI characteristics and a better risk assessment of a geoscientist’s interpretation.

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. We would also like to thank the Rose & Associates DHI consortium, which has provided extremely valuable information on DHI characteristics. The seismic data in the offshore case study is courtesy of Petroleum Geo-Services. Thanks also go to Deborah Sacrey and Mike Dunn for reviewing the paper. Finally, we would like to thank Tom Smith for reviewing this paper and for the inspiration to push the boundaries of interpretation technology.

References

Brown, A.B, [2004]. Interpretation of three-dimensional seismic data. AAPG Memoir 42/SEG Investigations in Geophysics No. 9, sixth edition.

Chen, Q. and Sidney, S. [1997]. Seismic attribute technology for reservoir forecasting and monitoring. The Leading Edge, 16, 445-448. Chopra, S. and Marfurt, K. [2007]. Seismic attributes for prospect identification and reservoir characterization. SEG Geophysical Development Series No. 11.

Fahmy, W.A. [2006]. DHI/AVO best practices methodology and applications: a historical perspective. SEG/EAGE Distinguished Lecture presentation.

Forrest, M., Roden, R. and Holeywell, R. [2010]. Risking seismic amplitude anomaly prospects based on database trends. The Leading Edge, 5, 936-940.

Hilterman, F.J. [2001]. Seismic amplitude interpretation. Distinguished instructor short course, SEG/EAGE. Kohonen, T. [2001]. Self Organizing Maps. Third extended addition, Springer Series in Information Services, Vol. 30.

Roden, R., Forrest, M. and Holeywell, R. [2005]. The impact of seismic amplitudes on prospect risk analysis. The Leading Edge, 7, 706-711.

Roden, R., Forrest, M. and Holeywell, R. [2012]. Relating seismic interpretation to reserve/resource calculations: Insights from a DHI consortium. The Leading Edge, 9, 1066-1074.

Roden, R., Forrest, M., Holeywell, R., Carr, M. and Alexander, P.A. [2014]. The role of AVO in prospect risk assessment. Interpretation, 2, SC61-SC76.

Roden, R., Smith, T. and Sacrey, D. [2015]. Geologic pattern recognition from seismic attributes: Principal component analysis and self-organizing maps. Interpretation, 3, SAE59-SAE83.

Rudolph, K.W. and Goulding, F.J. [2017]. Benchmarking exploration predictions and performance using 20+ yr of drilling results: One company’s experience. AAPG Bulletin, 101, 161-176.

Rutherford, S.R. and Williams, R.H. [1989]. Amplitude-versus-offset variations in gas sands: Geophysics, 54, 680-688.

Smith, T. and Taner, M.T. [2010]. Natural clusters in multi-attribute seismics found with self-organizing maps. Extended Abstracts, Robinson-Treitel Spring Symposium by GSH/SEG, March 10-11, 2010, Houston, Tx.

Taner, M.T. [2003]. Attributes revisited. http://www.rocksolidimages.com/pdf/attrib_revisited.htm, accessed 13 August 2013.

ROCKY RODEN owns his own consulting company, Rocky Ridge Resources Inc., and works with several oil companies on technical and prospect evaluation issues. He also is a principal in the Rose and Associates DHI Risk Analysis Consortium and was Chief Consulting Geophysicist with Seismic Micro-technology. He is a proven oil finder (36 years in the industry) with extensive knowledge of modern geoscience technical approaches (past Chairman – The Leading Edge Editorial Board). As Chief Geophysicist and Director of Applied Technology for Repsol-YPF, his role comprised advising corporate officers, geoscientists, and managers on interpretation, strategy and technical analysis for exploration and development in offices in the U.S., Argentina, Spain, Egypt, Bolivia, Ecuador, Peru, Brazil, Venezuela, Malaysia, and Indonesia. He has been involved in the technical and economic evaluation of Gulf of Mexico lease sales, farmouts worldwide, and bid rounds in South America, Europe, and the Far East. Previous work experience includes exploration and development at Maxus Energy, Pogo Producing, Decca Survey, and Texaco. He holds a BS in Oceanographic Technology-Geology from Lamar University and a MS in Geological and Geophysical Oceanography from Texas A&M University. Rocky is a member of SEG, AAPG, HGS, GSH, EAGE, and SIPES.

CHINGWEN CHEN, PH.D. received an M.S. (2007) and a Ph.D. (2011) in Geophysics from the University of Houston, studying global seismology. After graduation, she joined the industry as a geophysicist with Noble Energy where she supported both exploration and development projects. Dr. Chen has a great passion for quantitative seismic interpretation, and more specifically rock physics, seismic imaging and multi-seismic attribute analysis. She later joined Geophysical Insights as a Senior Geophysicist, where the application of machine learning techniques became a focus of her work. Since 2015, her primary interest has been in increasing the efficiency of seismic interpretation.

Seismic Interpretation Below Tuning with Multiattribute Analysis

12 April 2017 | Recommended, Technical Papers

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.

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.).

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.

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.

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

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.

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.

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.

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.

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.

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.

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.

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.

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.

Using Self Organizing Maps to Expose Direct Hydrocarbon Indicators

1 March 2017 | Case Studies

Analysis of offshore Gulf of Mexico – Class 3 AVO setting

The case study highlighted below is an offshore oil/gas field in 470 feet of water on the continental shelf of the Gulf of Mexico. The eld has two producing wells that were drilled on the upthrown side of a normal fault and into an amplitude anomaly. The normally-pressured reservoir is approximately 100 feet thick. The hydrocarbon-filled sandstone reservoir has low impedance compared to the encasing shales, indicative of a Class 3 AVO environment.

Applying both traditional interpretation methods and the Self-Organizing Maps (SOM’s) capability within Paradise, Mr. Rocky Roden, a Sr. Geophysical Consultant with Geophysical Insights, conducted an analysis on the 3D volume over this field. A group of seismic attributes were selected that would best expose Direct Hydrocarbon Indicators (DHIs).

Using known conditions, including AVO Class, reservoir characteristics, well logs, and seismic response data, the Rose and Associates DHI Consortium has developed a methodology for quantifying the risk of DHI-related prospects. The Consortium has compiled a database of several hundred wells and has been able to identify the most important DHI Characteristics.

Data quality, bed resolution, or seismic attribute algorithm may or may not allow an interpreter to nd any of the following examples, even if they are physically present in an ideal natural setting. The existence and/or variance in any of these DHI characteristics, as expressed in the data, enable the extent of risk to be better understood, and more so in a group of prospects, to be far better appreciated and quantified. According to the DHI Consortium, some of the most important DHI characteristics are:

Amplitude conformance to downdip structural closure
Internal consistency of amplitude
Phase, frequency, or character changes at edge of reservoir
Flat spots
Attenuation

A SOM probability display using flat spot attributes

Results – Reduced risk through greater insights

The key findings of the study are as follows:

The SOM analyses of the seismic attributes associated with Flat Spots helped define two levels of hydrocarbon contacts in this dataset.
The SOM analyses on Attributes for Attenuation amplified apparent attenuation features, especially within the reservoir sand.
The SOM analyses of the seismic attributes named Ten Attributes 10% was especially helpful in defining amplitude conformance to downdip closure, and provided confidence in the internal consistency of the reservoir.
All three combinations of seismic attributes analyzed by SOM analysis revealed phase and character changes near the edge of the hydrocarbon reservoir.

SOM Analysis proved to complement and enhance the conventional interpretation by providing a second, completely independent method of exposing DHIs. This application of the SOM method increased confidence that insightful DHI characteristics are truly evidenced in the appraisal area. The results of this case study demonstrate that applying the DHI methodology with the SOM analysis engine in Paradise on selected seismic attributes can dramatically reduce uncertainty in the interpretation, thereby decreasing exploration risks in this geological setting.

A SOM classification of attenuation attributes

Seismic data provided courtesy of Petroleum Geo-Services (PGS)

Using Self-Organizing Maps to Explore the Yegua in the Texas Gulf Coast

15 January 2016 | Case Studies

Analysis of Middle Texas Gulf Coast 3D to explore for shallow Yegua Formation Potential

In this case study of the use of Self-Organizing-Maps (SOM analysis), the gathers were used to generate AVO volumes such as Far-Near (used on the angle stacks, where nears were 0-15 degrees and fars were 31-45 degrees), (Far-Near)xFar, Gradient (B), Intercept (A) x Gradient (B), ½(Intercept + Gradient) and Poissons’ Reflectivity (PR).

Conventional amplitude interpretation identified a potential area of hydrocarbon accumulation, downthrown on a down-to-coast fault. Figure 1 is the amplitude extraction from the PSTM-raw volume.

In addition to the created AVO attributes, volumes of Spectral Decomposition, curvature, similarity and other frequency-related attributes were created. Conventional interpretation of the reservoir area indicated the anomaly covered approximately 70 acres.

A SOM of the above mentioned AVO attributes, plus Sweetness and Average Energy was run to more closely identify the anomaly and the aerial extent. Figure 2 shows the results of this analysis.

A detailed engineering study of the production indicates that the results of the SOM analysis concur with the aerial extent of the sand deposition to be more in line with almost 400 acres of drainage rather than the initial 70 acres first identified. The SOM identified in this time slice shows a network of sand deposition not seen in conventional mapping.

Figure 3 shows an arbitrary line going through a second, upthrown Yegua anomaly identified by the SOM analysis, and now drilled, confirming the economic presence of hydrocarbons.

The conclusion drawn from this study is that SOM analysis proved to complement and enhance the conventional interpretation by providing a second, completely independent method of exposing direct hydrocarbon indicators.

Self-organizing maps to explore the Yegua

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