Solving Interpretation Problems using Machine Learning on Multi-Attribute, Sample-Based Seismic Data

Solving Interpretation Problems using Machine Learning on Multi-Attribute, Sample-Based Seismic Data

Solving Interpretation Problems using Machine Learning on Multi-Attribute, Sample-Based Seismic Data
Presented by Deborah Sacrey, Owner of Auburn Energy
Challenges addressed in this webinar include:

  • Reducing risk in drilling marginal or dry holes
  • Interpretation of thin bedded reservoirs far below conventional seismic tuning
  • How to better understand reservoir characteristics
  • Interpretation of reservoirs in deep, pressured environments
  • Using the classification process to help with correlations in difficult stratigraphic or structural environments

The webinar is open to those interested in learning more about how the application of machine learning is key to seismic interpretation.

Deborah Sacrey

Deborah Sacrey


Auburn Energy

Deborah Sacrey is a geologist/geophysicist with 41 years of oil and gas exploration experience in the Texas, Louisiana Gulf Coast, and Mid-Continent areas of the US. Deborah specializes in 2D and 3D interpretation for clients in the US and internationally.

She received her degree in Geology from the University of Oklahoma in 1976 and began her career with Gulf Oil in Oklahoma City. She started Auburn Energy in 1990 and built her first geophysical workstation using the Kingdom software in 1996. Deborah then worked closely with SMT (now part of IHS) for 18 years developing and testing Kingdom. For the past eight years, she has been part of a team to study and bring the power of multi-attribute neural analysis of seismic data to the geoscience community, guided by Dr. Tom Smith, founder of SMT. Deborah has become an expert in the use of the Paradise® software and has over five discoveries for clients using the technology.

Deborah is very active in the geological community. She is past national President of SIPES (Society of Independent Professional Earth Scientists), past President of the Division of Professional Affairs of AAPG (American Association of Petroleum Geologists), Past Treasurer of AAPG and Past President of the Houston Geological Society. She is currently the incoming President of the Gulf Coast Association of Geological Societies (GCAGS) and is a member of the GCAGS representation on the AAPG Advisory Council. Deborah is also a DPA Certified Petroleum Geologist #4014 and DPA Certified Petroleum Geophysicist #2. She is active in the Houston Geological Society, South Texas Geological Society and the Oklahoma City Geological Society (OCGS).

Thin Beds and Anomaly Resolution in the Niobrara

Thin Beds and Anomaly Resolution in the Niobrara

By Rocky Roden
March 2018

Identifying thin beds using machine learning

In March of 2018, a study was conducted of the Niobrara using machine learning for multi-attribute analysis. Geophysical Insights obtained 100 square miles of seismic data covering the Niobrara in northeast Colorado from the Geophysical Pursuit and Geokinetics multi-client seismic library.  Using Paradise®, a multi-attribute learning application, geoscientists generated and classified seismic volumes, the results revealing thin beds below seismic tuning and anomaly resolution not practical with traditional interpretation methods. In total, the results of that classification demonstrated dramatically improved stratigraphic resolution and anomaly isolation within the Niobrara Formation and associated reservoir and source rock units.


Outline of Niobrar Phase 5


Figure 1: Outline of Niobrara Phase 5. 100 square miles within this survey were selected for analysis.

 Seismic Volume Classification Results

The result set, or classification in multi-colors, shows the continuity of facies and the continuity/discontinuity of anomalies in the greater Niobrara section. Each voxel represents 1 millisecond in the volume or approximately 15 feet. The white dashed correlation line shows anomalies on the B bench and the brackets indicate anomalies at the Codell level. Two horizontal boreholes are shown, however, neither seems an optimal penetration. The results demonstrate that machine learning in Paradise enables a sample-based thin bed analysis.

Classification - Instantaneous Attributes

Figure 2a: Classification based on eight Instantaneous attributes.

Figure 2b: The original amplitude data.

Figure 3: Self-Organizing Map (SOM) classification with low probability (<10%) anomalies (white) in the greater Niobrara section. The results may indicate concentrations of hydrocarbons in organic-rich shales. Anomalies also may show migration up section along faults from the Niobrara into the Sharon Springs. Niobrara B anomalies are best developed in elevated areas. Additional well data can corroborate anomaly interpretations.

Interactive 2D Map

Figure 4: Interactive 2D Colormaps use transparency (left and see inset) to highlight and extract anomalous behavior or particular facies. Note that the wellbore intersects a fault (white arrow) and misses the bracketed anomaly.

Principal Component Analysis (PCA) was used to identify and quantify the key attributes in the seismic volumes. The SOM process was then applied on these attributes to learn and classify the data. The neural topology, shown in the Paradise 2D Colormap, establishes the number of classes in the resulting seismic volume. The classification volumes show geologic features and anomalies that can aid in well location and development planning.


Top Niobrara Map - Structures and Associated Fractures

Figure 5: Map of structural features identified in this Niobrara volume. Near vertical graben-style faulting predominates in the upper Niobrara..

Using Self Organizing Maps to Expose Direct Hydrocarbon Indicators

Using Self Organizing Maps to Expose Direct Hydrocarbon Indicators

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:

  1. Amplitude conformance to downdip structural closure
  2. Internal consistency of amplitude
  3. Phase, frequency, or character changes at edge of reservoir
  4. Flat spots
  5. 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)

First Steps in the Sub-Seismic Resolution of the Eagle Ford, Part I

First Steps in the Sub-Seismic Resolution of the Eagle Ford, Part I

This and three companion Application Briefs (EF2, EF-3, and EF-4) present details of an analysis and results from the application of Paradise® on a large 3D seismic volume from two counties in Texas focused on the Eagle Ford trend and its bounding formations: the underlying Buda and the overlying Austin Chalk. The results characterize remarkable resolution of stratigraphic and structural details in these three formations. Dramatically, these Application Briefs will show resolution of
features below seismic resolution, a product of the analysis taking full advantage of multiple attributes simultaneously.

While this and related Application Briefs are set in unconventional geologies, the principles outlined herein are applicable to both conventional and unconventional resource plays.

The analysis began by applying Principal Component Analysis (PCA) on 16 Instantaneous attributes. Instantaneous attributes calculate a value at each sample and inherently return higher frequency information. From the PCA analysis, nine attributes were run in Self-Organizing Maps (SOMs) and, of these, five were found to be most common in the Eagle Ford results. A brief description of the five types of Instantaneous attributes is as follows:

  • Instantaneous Phase for the continuity/ discontinuity enhancement;
  • Normalized Amplitude aka Cosine of Instantaneous Phase returns the energy distinctly from peaks versus troughs;
  • Relative Acoustic Impedance helps to resolve geobodies;
  • Envelope or Total Energy of the entire reflected waveform, including Real Part of the reflected seismic that is measurable and the Imaginary Part which is not.
  • Separately, Trace Envelope was found to be applicable in one unusual setting and facies tract for geobody 2, and suggests another distinction in rock or reservoir type.

Figure 1

below tuning in the eagle ford

In addition to the above, Envelope 2nd Derivative, Instantaneous Frequency, and Thin Bed Indicator rounded out the nine suggested by the PCA. However, these three were less evident in the area investigated or were possibly in the background.

The original PCA and SOM were run over a 1.5 to 3.2 sec. interval and a specific range of inlines and crosslines to capture the Eagle Ford’s complete updip to downdip occurrence. Results were first viewed by use of the default Interactive 2D Colormap (Figure 1a), which is unique to Paradise. Note that the Eagle Ford is resolved but not uniquely distinguished until a few colors that were not specific to the Eagle Ford were changed in the Interactive 2D Colormap. Figure 1b shows the result that helped confirm the near-uniqueness of the Eagle Ford facies in the stratigraphy of the area.

The transparency function of the interactive 2D Colormap was then used to remove all neuron colors except those that represent the Eagle Ford shale and the overlying interface with the Upper Eagle Ford marl. This technique exposed other similar and sizeable objectives in the overall stratigraphic section. Figure 2 reveals that filled scour structures carved into the top of the Georgetown and an uncalibrated zone, possibly Pearsall, share the facies characteristics of the Eagle Ford, which suggest these other formations also likely include similar organic-rich shale facies.

Figure 2


All seismic data owned and provided courtesy of Seitel, Inc.

Stratigraphic and Structural Resolution Using Instantaneous Attributes on Spectral Decomp Sub-Bands, Buda and Austin Chalk Formations, Part 4

Stratigraphic and Structural Resolution Using Instantaneous Attributes on Spectral Decomp Sub-Bands, Buda and Austin Chalk Formations, Part 4

Spectral Decomposition (SD) is regarded as a useful tool for below-resolution seismic interpretation, reservoir thickness interpretation, and depositional structure enhancement. Amplitude components
using Normalized Instantaneous attributes help quantify thickness variability more reliably.
Phase components detect lateral discontinuities both stratigraphic and structural and also contribute to the segregation of various facies tracts. However, going beyond the visualization of one, two, or even three attributes at a time, this Application Brief describes the simultaneous analysis of multiple SD attributes using machine learning processes in Paradise®.

Initial steps were to take 20 sub bands from 8 to 85Hz. Run over the time interval of 1.5 to 3.2 seconds, the first three Eigenvectors yielded relatively low values for sub-bands 48.5 to 68.8Hz, moderate values for sub–bands 24.2 through 32.3Hz, and higher values for sub-bands 8 to 16.1Hz respectively. These results suggested a further look at the Linear/Octave Trace/envelope sub-bands from 12-50Hz. From these analyses, the Linear sub-band 24.7Hz and the Octave sub-band 26.5Hz stood out (Figure 1). The selections were based on the best resolution of the disconformity between the lower Austin Chalk and the Eagle Ford.

seismic interpretation in the eagle ford - 01

Instantaneous Principal Component Analysis (PCAs) and Self-Organizing Maps (SOMs) were applied using each of the two selected linear sub-bands as the base survey. When the data is delimited by area and by horizons (see Paradise Application Brief EF2), only one Eigenvector is dominant and the top two sub-bands, 24.2 and 28.3Hz, are those that encompass the aforementioned result. The SOM results from Linear 24.7Hz (Figure 2a) and 26.5Hz (Figure 2b) were then ghosted onto the Austin Chalk top for comparison. A subtle SW–NE trending fault encountered in the #2 well, which had not been seen using traditional methods, is resolved in Figure 2a; yet is a bit more subtle in Figure 2b.

seismic interpretation in the eagle ford 02

In Figure 3, the Instantaneous SOM result for the Linear 24.7Hz is displayed in SW to NE crosslines through two neighbor wells (see inset). It can be seen that stratal variations are rapid and subtle. In the Eagle Ford, turning off green neurons 1 and 2 blank out continuous bands in the upper Eagle Ford at Well 3, and at Well 4 only a smattering of pixels are gone. Also in the right view, two additional semi-continuous greens 9 and 17 in the upper part of the Eagle Ford shale are present. Both views share the basal green bands of neuron 25 and 26.

seismic interpretation in the eagle ford -03

seismic interpretation in the eagle ford - 04

The purple band in these views is the unique lithology of the Basal Clay shale (BCS), a presumed pelagic deposit. In the underlying Buda, scour shapes in neuron 57 and 59 (red) on the left contrast starkly with the continuous bands of both facies in the vicinity of Well 4. Neuron “facies” 51 and 58 at well 3, not present on the line over Well 4, have been turned off to enhance the appearance of the scours. The overall thickness of the Buda shown is only 10ms.

A time slice (Figure 4) in the area just downdip of the last figure shows detailed stacking variations across the upper Buda along its northern edge. Yellow neuron 49 facies come in above red 59 and underneath orange 58 of last figure, before the latter then the former laps out to the NE. A compressional fault is distinct in the time slice and is apparent throughout the vertical section in nearby crossline (circle). Probable karst features are apparent to the SW and NE in the uppermost Austin Chalk in both views.

At the dip position of wells 6 and 8 on the Instantaneous Spectral Decomp (Figure 5), the
Upper Eagle Ford marl varies little in neuron sequence. With neurons 54, 62, 63, and 64 turned off across the #6 boreholes, the scour at the base of the Austin Chalk outlined by a white dashed line can be seen to carve into marl neuron facies 46 and 54. In this dip position, the Basal Clay Shale (BCS) is lowest olive color.

seismic interpretation in the eagle ford - 05

Similar features of the angular unconformity at the base of the Austin Chalk and phenomenal karsts can be seen on the Instantaneous SOM result for the Linear 26.5Hz result (Figure 6a, b, c) and are enhanced by the use of transparency. Corresponding neurons are turned off in the 2D Colormap in the upper left for the Upper Eagle Ford above the Eagle Ford shale and in the upper right for measures below the Eagle Ford shale. Note the absence of faults or any of the key stratigraphic features on conventional seismic display.

seismic interpretation in the eagle ford - 06seismic interpretation in the eagle ford 07

All Seismic data owned and provided courtesy of Seitel, Inc.