Geophysical Insights at SEG 2017 in Houston, Booth #301
Using Attributes to Interpret the Environment of Deposition - A 1 day course. Taught by Kurt Marfurt, Rocky Roden, and ChingWen Chen
Dr. Kurt Marfurt and Dr. Tom Smith featured in the July edition of AOGR on Machine Learning and Multi-Attribute Analysis
Rocky Roden and Ching Wen Chen in May edition of First Break - Interpretation of DHI Characteristics using Machine Learning
Seismic interpretation and machine learning by Rocky Roden and Deborah Sacrey, GeoExPro, December 2016

Multi-Attribute Seismic Analysis in Paradise

By Deborah Sacrey and Rocky Roden

A compendium of case studies showing Paradise applying multi-attribute analysis for conventional and unconventional resources to highlight anomalies and other siesmic features. Machine learning in Paradise used for sesimic interpretation to analyze pore pressure, facies, sweet spots and other anomalous features in different geology.

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This paper is an assembly of four case studies on the application machine learning processes for multt-attribute seismic analysis. Specifically, the paper describes how Self-Organizing Maps (SOM’s), a form machine learning, and Principal Component Analysis (PCA), an advanced pattern-recognition method, are applied to seismic attributes in various geologic settings, including onshore - conventional and unconventional - and offshore. A brief ‘working’ definition of these two advanced pattern recognition methods is provided below for the convenience of the reader. The flowchart shown on the cover illustrates how the SOM and PCA processes are used together to refine and ‘de-risk’ interpretation. Straightforward workflows that guide geoscientists through the application of these methods are embodied within the Paradise® advanced geoscience analysis platform by Geophysical Insights. A brief summary of the capabilities of Paradise is provided in the last section of this document and more information on the Paradise platform is available at 

Attribute Analysis in Unconventional Resource Plays Using Unsupervised Neural Networks

Analysis of Unconventional Resource using Inversion attributes and seismic attributes:

 Key elements in understanding unconventional resource plays encompass the following categories:

Reservoir Geology: thickness, lateral extent, stratigraphy, mineralogy, porosity and permeability

Geochemistry: Total Organic Content (TOC), maturity (Ro-heat), and kerogen% (richness)

Geomechanics: acoustic impedance inversion, Young’s modulus, Poisson’s ratio (Vp/Vs) and pressures

Faults, Fractures, and Stress Regimes: coherency (similarity), curvature, fault volumes, velocity anisotrophy (azimuthal distribution) and stress maps.

This case study involved a newly acquired 3D seismic volume in a fringe area of the Eagle Ford Shale Trend. The 3D is approximately 10 square miles and four wells had been drilled to date on 2D data previously interpreted. Two wells targeted the Eagle Ford Shale Formation, and another two wells were drilled for the Austin Chalk and the Buda Lime Formations. All four wells were drilled in normal pressured reservoirs with mixed results when it came to quality shows and commercial production.  

After processing the 3D volume and initial interpretation was completed, well results and logs were incorporated by the client to create critical inversion attributes known to assist in the assessment of the shale’s productivity. Attributes contributed by the client to the analysis were: Final Density, Lambda Rho, MuRho, Poisson’s Brittleness, Poisson’s Ratio, Shear Impedance, Brittleness Coefficient, and P-impedance. Additional attributes run for the analysis were: Spectral Decomposition volumes, curvature and similarity volumes, Instantaneous attributes and Amplitude-related volumes (Average Energy and Sweetness).

The zone of study was confined to roughly the Top Austin Chalk to the Top of the Edwards Lime, encompassing the Austin Chalk, Eagle Ford Shale and Buda Limestone, which was approximately from 1.2 to 1.6 seconds.

In addition to the PSTM volume, the generated plus client-provided attributes used to highlight sweet spots included:

  • Attenuation
  • Bandwidth
  • Envelope Slope
  • Insantaneous Q
  • MuRho
  • S-Impendance
  • Trace Envelope
  • Young’s Brittleness

A 12 x 6 topology was used for the analysis, so there were 72 neurons training on the attribute information. Figure 1 is a time slice showing the interpreted “sweet spots” in the Eagle Ford Shale on the 3D from the SOM Analysis.

Two wells had been drilled, targeting the Eagle Ford Shale Formation. One was drilled prior to the acquisition of the data, and had few shows. It was plugged as a non-commercial well. The second well had good shows in the horizontal section of the hole, but encountered mechanical difficulties during drilling and had to be temporarily plugged.

In conclusion, SOM analysis proved to be complementary to the interpretation of the data. The company who owns this 3D is now planning on targeting the area with five additional wells in the coming year. The application of using SOM analysis using selected seismic attributes can dramatically reduce uncertainty and thus decrease exploration risk in unconventional reservoirs.

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

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 3 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 exent. Figure 4 shows the results of this analysis.

A detailed engineering study of the production indicates that the results of the SOM analysis concurs 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 5 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.

Exposing DHI's Using Self-Organizing Maps

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 field 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, an analysis was conducted 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, and the seismic attribute algorithms employed may or may not allow an interpreter to identify DHI characteristics, 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

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 (See Figure 6).
  • The SOM analyses on Attributes for Attenuation amplified apparent attenuation features, especially within the reservoir sand (See Figure 7).
  • The SOM analyses of one set of seismic attributes 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.

Seismic Facies - a Pore Pressure Analog - and Self-Organizing Maps

Offshore South America Seismic Facies Analysis

This analysis and application of Paradise involved the evaluation of a 3D volume offshore South America. A well had been drilled and encountered unexpected high pressures which prevented drilling to the desired deeper target. Traditional methods to identify pressure did not readily reveal a high pressure zone before drilling. The challenge was to identify the high pressure zone employing Self-Organizing-Maps (SOM’s).

Initial evaluation of the 3D seismic volume suggested there may be facies and stratigraphic variations in the high pressure zone. After an interpretation of the local geology and putting this into a regional context, the anomalous high pressure area appeared to be associated with a slope facies as interpreted from the conventional stacked seismic data.

Therefore, five different combinations of seismic attributes were applied in a SOM analysis to help define the seismic facies in the zone of interest. One specific combination of six seismic attributes clearly defined the seismic slope facies and associated high pressure region. With the use of 2D colorbars in the 3D Viewer in the Paradise software, the highlighting of specific neurons enabled the visualization of the high pressured seismic facies (See Figure 8).

Analysis Results – Exposing High Pore Pressure Region

  • Based on pressure readings from a single well, the increase in pressure seems to be associated with a hummocky, wavy, and at times chaotic seismic reflection character.
  • This reflection character is typically associated with a slope setting where there are rapid facies changes, discontinuous siltstone and mudstone beds and at times channelized sands with interchannel mudstones.
  • Dozens of seismic attributes were generated to help define this seismic facies associated with pressure in the well.
  • Five different sets of seismic attributes were selected for SOM analysis to define this pressure associated seismic facies.
  • All of the SOM Classification volumes and to some degree Probability volumes defined components of this seismic facies (e.g., top, bottom, internal seismic reflection character, reflection character above and below, etc.).
  • A specific set of seismic attributes effectively isolated the pressure zone through a SOM analysis (See Figure 9).

Welcome to Paradise

Paradise is a dramatic new geoscience analysis platform featuring the use of advanced pattern recognition methods applied to oil and gas exploration, including Self-Organizing Maps (SOMs) and Principal Component Analysis (PCA). The core capabilities below enable geoscientists to rapidly...

  • Scan large volumes to reveal anomalies for further analysis
  • Discriminate the presence of hydrocarbons and DHI’s
  • Reveal geologic and stratigraphic features
  • Identify changes in pore pressure

The unique software technology, a scalable, client-server architecture, enables geoscientists to work independently or collaboratively across an oil & gas enterprise. The product suite offers guided workflows, 3D imaging, and 2D color mapping.

The software executes and manages workflows based on advanced pattern recognition methods, including SOM and PCA. Paradise guides geoscientists in applying these next generation processes to reveal new insights in the data. The product is powerful yet easy to use, enabling analysis of different sets of attributes and engineering data more rapidly.

About Geophysical Insights

Geophysical Insights ( applies advanced analytic methods and technology with deep experience in seismic interpretation to reduce the time and risk of exploration. Founded by Dr. Tom Smith in 2009, Geophysical Insights is taking the interpretation process to deeper levels of understanding. We are applying advanced geophysics and new interpretation methods to address four important areas of geologic analysis, including identifying rock lithologies and fluid contents. Our consultants leverage advanced technologies with extensive experience to deliver practical solutions to difficult interpretation challenges.

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