Seismic Interpretation with Machine Learning

Seismic Interpretation with Machine Learning

By: Rocky Roden, Geophysical Insights, and Deborah Sacrey, Auburn Energy
Published with permission: GeoExPro Magazine
December 2016

Today’s seismic interpreters must deal with enormous amounts of information, or ‘Big Data’, including seismic gathers, regional 3D surveys with numerous processing versions, large populations of wells and associated data, and dozens if not hundreds of seismic attributes that routinely produce terabytes of data. Machine learning has evolved to handle Big Data. This incorporates the use of computer algorithms that iteratively learn from the data and independently adapt to produce reliable, repeatable results. Multi-attribute analyses employing principal component analysis (PCA) and self-organizing maps are components of a machine-learning interpretation workflow (Figure 1) that involves the selection of appropriate seismic attributes and the application of these attributes in an unsupervised neural network analysis, also known as a self-organizing map, or SOM. This identifies the natural clustering and patterns in the data and has been beneficial in defining stratigraphy, seismic facies, DHI features, sweet spots for shale plays, and thin beds, to name just a few successes. Employing these approaches and visualizing SOM results utilizing 2D color maps reveal geologic features not previously identified or easily interpreted from conventional seismic data.

Steps 1 and 2: Defining Geologic Problems and Multiple Attributes

Seismic attributes are any measurable property of seismic data and are produced to help enhance or quantify features of interpretation interest. There are hundreds of types of seismic attributes and interpreters routinely wrestle with evaluating these volumes efficiently and strive to understand how they relate to each other.

The first step in a multi-attribute machine-learning interpretation workflow is the identification of the problem to resolve by the geoscientist. This is important because depending on the interpretation objective (facies, stratigraphy, bed thickness, DHIs, etc.), the appropriate set of attributes must be chosen. If it is unclear which attributes to select, a principal component analysis (PCA) may be beneficial. This 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 dataset. In other words, PCA helps determine the most meaningful seismic attributes.

seismic interpretation workflow

Figure 1: Multi-attribute machine learning interpretation workflow with principal component analysis (PCA) and self-organizing maps (SOM).

Figure 2 is a PCA analysis from Paradise® software by Geophysical Insights, where 12 instantaneous attributes were input over a window encompassing a reservoir of interest. The following figures also include images of results from Paradise. Each bar in Figure 2a denotes the highest eigenvalue on the inlines in this survey. An eigenvalue is a value showing how much variance there is in its associated eigenvector and an eigenvector is a direction showing a principal spread of attribute variance in the data. The PCA results from the selected red bar in Figure 2a are denoted in Figures 2b and 2c. Figure 2b shows the principal components from the selected inline over the zone of interest with the highest eigenvalue (first principal component) indicating the seismic attributes contributing to this largest variation in the data. The percentage contribution of each attribute to the first principal component is designated. In this case the top four seismic attributes represent over 94% of the variance of all the attributes employed. These four attributes are good candidates to be employed in a SOM analysis. Figure 2c displays the percentage contribution of the attributes for the second principal component. The top three attributes contribute over 68% to the second principal component. PCA is a measure of the variance of the data, but it is up to the interpreter to determine and evaluate how the results and associated contributing attributes relate to the geology and the problem to be resolved.

principal component analysis for seismic interpretation

Figure 2: Principal Component Analysis (PCA) results from 12 seismic attributes: (a) bar chart with each bar denoting the highest eigenvalue for its associated inline over the displayed portion of the seismic 3D volume. The red bar designates the inline with the results shown in 2b and c; (b) first principal component designated win orange and associated seismic attribute contribution to the right; and (c) second principal component in orange with the seismic contributions to the right. The highest contributing attributes for each principal component are possible candidates for a SOM analysis, depending on the interpretation goal.

Steps 3 and 4: SOM Analysis and Interpretation

The next step in the multi-attribute interpretation process requires pattern recognition and classification of the 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, developed by Teuvo Kohonen in 1982, efficiently distill multiple seismic attributes into classification and probability volumes. SOM is a powerful non-linear cluster analysis and pattern recognition approach that helps interpreters identify patterns in their data, some of which can relate to desired geologic characteristics. The tremendous amount of samples from numerous seismic attributes exhibit significant organizational structure. SOM analysis identifies these natural organizational structures in the form of natural attribute clusters. These clusters reveal significant information about the classification structure of natural groups that is difficult to view any other way.

Figure 3 describes the SOM process used to identify geologic features in a multi-attribute machine-learning methodology. In this case, 10 attributes were selected to run in a SOM analysis over a specific 3D survey, which means that 10 volumes of different attributes are input into the process. All the values from every sample from the survey are input into attribute space where the values are normalized or standardized to the same scale. The interpreter selects the number of patterns or clusters to be delineated. In the example in Figure 3, 64 patterns are to be determined and are designated by 64 neurons. After the SOM analysis, the results are nonlinearly mapped to a 2D color map which shows 64 neurons.

SOM workflow process

Figure 3: How SOM works (10 seismic attributes)

At this point, the interpreter evaluates which neurons and associated patterns in 3D space define features of interest. Figure 4 displays the SOM results, where four neurons have highlighted not only a channel system but details within that channel. The next step is to refine the interpretation and perhaps use different combinations of attributes and/or use different neuron counts. For example, in Figure 4, to better define details in the channel system may require increasing the neuron count to 100 or more neurons to produce much more detail. The scale of the geologic feature of interest is related to the number of neurons employed; low neuron counts will reveal larger scale features, whereas a high neuron count defines much more detail.

SOM seismic interpretation analysis

Figure 4: SOM analysis interpretation of channel feature with 2D color map

Workflow Examples

Figure 5 shows the SOM classification from an offshore Class 3 AVO setting where direct hydrocarbon indicators (DHIs) should be prevalent. The four attributes listed for this SOM run were selected from the second principal component in a PCA analysis. This SOM analysis clearly identified flat spots associated with a gas/oil and an oil/water contact. Figure 5 displays a line through the middle of a field where the SOM classification identified these contacts, which were verified by well control. The upper profile indicates that 25 neurons were employed to identify 25 patterns in the data. The lower profile indicates that only two neurons are identifying the patterns associated with the hydrocarbon contacts (flat spots). These hydrocarbon contacts were difficult to interpret with conventional amplitude data.

SOM results defining hydrocarbon contacts

Figure 5: SOM results defining hydrocarbon contacts on a seismic line through a field. Attributes chosen for the identification of flat spots were 1. instantaneous frequency; 2. thin bed indicator; 3. acceleration of phase; 4. dominant frequency

The profile in Figure 6 displays a SOM classification where the colors represent individual neurons with a wiggle-trace variable area overlay of the conventional amplitude data. This play relates to a series of thin strandline sand deposits. These sands are located in a very weak trough on the conventional amplitude data and essentially have no amplitude expression. The SOM classification employed seven seismic attributes which were determined from the PCA analysis. A 10x10 matrix of neurons or 100 neurons were employed for this SOM classification. The downdip well produced gas from a 6’ thick sand that confirmed the anomaly associated with a dark brown neuron from the SOM analysis. The inset for this sand indicates that the SOM analysis has identified this thin sand down to a single sample size which is 1 ms (5’) for this data. The updip well on the profile in Figure 6 shows a thin oil sand (~6’ thick) that is associated with a lighter brown neuron with another possible strandline sand slightly downdip. This SOM classification defines very thin beds and employs several instantaneous seismic attributes that are measuring energy in time and space outside the realm of conventional amplitude data.

SOM results showing thin beds

Figure 6: SOM results showing thin beds in a strandline setting

Geology Defined

The implementation of a multi-attribute machine-learning analysis is not restricted to any geologic environment or setting. SOM classifications have been employed successfully both onshore and offshore, in hard rocks and soft rocks, in shales, sands, and carbonates, and as demonstrated above, for DHIs and thin beds. The major limitations are the seismic attributes selected and their inherent data quality. SOM is a non-linear classifier and takes advantage of finely sampled data and is not burdened by typical amplitude resolution limitations. This machine learning seismic interpretation approach has been very successful in distilling numerous attributes to identify geologic objectives and has provided the interpreter with a methodology to deal with Big Data.

Rocky RodenROCKY 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.
Deborah SacreyDEBORAH SACREY  is a geologist/geophysicist with 39 years of oil and gas exploration experience in the Texas and Louisiana Gulf Coast, and Mid-Continent areas. For the past three years, she has been part of a Geophysical Insights team working to bring the power of multiattribute neural analysis of seismic data to the geoscience public. Sacrey received a degree in geology from the University of Oklahoma in 1976, and immediately started working for Gulf Oil. She started her own company, Auburn Energy, in 1990, and built her first geophysical workstation using
Kingdom software in 1995. She specializes in 2-D and 3-D interpretation
for clients in the United States and internationally. Sacrey is a DPA certified
petroleum geologist and DPA certified petroleum geophysicist.

 

Delighting in Geophysics

Delighting in Geophysics

 
Q&A with Dr. Thomas A. Smith
Published with permission: GeoExPro September 2014, Volume 11, No 4

Why did you launch Geophysical Insights after the sale of SMT? Wasn’t it time for a break?

The work at SMT was thoroughly enjoyable, particularly generating new ideas and developing new technology, so after the sale of SMT, it seemed quite natural to continue. I jumped into geophysical research with delight. Geophysical Insights was launched to develop the next generation of interpretation technologies, a result of some of that research. We recognized that there was an opportunity to make a contribution to the industry. Response has been good, with a substantial number of people expressing a great interest in these new ways to conduct seismic interpretation. Here’s why: today we have more data, a greater variety of play concepts, and often less time for interpreters to analyze prospects. In particular, the number of seismic attributes available is now in the hundreds. Witnessing this growing body of information, several years ago M. Turhan Tanner (see GEO ExPro Vol. 3, No. 4), Sven Treitel and I began collaborating on the premise that greater insight may be extracted from the seismic response by analyzing multiple attributes simultaneously. We recognized that advanced pattern recognition methods were being used in many applications outside geoscience that could be adopted to address what we saw as an opportunity to advance the geoscience for exploration and production. Our thoughts on the opportunity were put forward at a 2009 SEG workshop entitled ‘What’s New in Seismic Interpretation’ in a presentation called ‘Self Organizing Maps of Multiple Attribute 3D Seismic Reflections’.

Tell us about the advanced geoscience analysis software platform, Paradise.

Paradise is an off-the-shelf analysis platform that enables interpreters to use advanced pattern recognition methods like Self-Organizing Maps and Principal Component Analysis through guided workflows. In 2009 we organized a team of interpretation software specialists, geoscientists, and marketing professionals to develop an advanced geoscience platform that would take full advantage of modern computing architecture, including large-scale parallel processing. Today, Paradise distills a variety of information from many attributes simultaneously at full seismic resolution, i.e. operating on every piece of data in a volume. This is one of the many differences in the application of machine learning and pattern recognition methods available in Paradise.

What is your perspective on the interpretation needs of unconventional compared to conventional resources? 

Both types of plays have their respective challenges, of course. Our work at Geophysical Insights is evenly divided between conventional and unconventional resources; however, there is growth in the use of seismic among E&P companies in unconventional plays. Systematic drilling programs are now being augmented more often by seismic interpretation, which is reducing field development costs by optimizing drilling and development. There is also growing recognition of what is termed ‘complex conventionals’, like carbonates – a geologic setting that requires advanced analysis for the characterization of carbonate reservoir rocks.

Where do you see the next big advances in geophysics? 

While traditional interpretation tools have made extensive use of improvements in interpretation imagery, their analysis has been largely qualitative – an interpretation of visual imagery on a screen. Certainly, qualitative interpretation is important and will always have a place in the interpretation process. We see the next generation of technologies producing quantitative results that will guide and inform an interpretation, thereby complementing qualitative analysis. Improvements in quantitative analysis will help interpretation add forecasting to prediction.

Do you think these advances will come from industry or academia?

Bright people and ideas are everywhere, and we must be open to solutions from a variety of sources. Technology breakthroughs are often an application of existing concepts from multiple disciplines applied in a whole new way. I believe that fluid, inter-disciplinary teams, enabled by advanced technology, offer an excellent organizational model for addressing the complex challenges of monetizing hydrocarbons in challenging geologic settings.

Where will these advances originate?

While the U.S. has emerged as a leader in O&G production due in large part to the development of unconventional resources and the application of new technologies, regions outside of the U.S. are beginning to develop these too. It is reasonable to expect that universities and companies in these regions will generate many new technologies, which will be essential to supply the growing demand for hydrocarbons worldwide. I applaud the next generation of geoscientists and hope that they enjoy the work of our industry as much as we do.