Solving Problems in Seismic Interpretation with Machine Learning
Deborah K. Sacrey, Auburn Energy
The process of statistically analyzing multiple seismic attributes using a SOM (Self-Organized Map) algorithm has been around for several decades. However, advances in computing power, coupled with the many new attributes developed in the last 30 years, has made this type of analysis extremely powerful.
In the past, SOM has been used on only one attribute at a time, and using the seismic wavelet as the basis for the neural analysis. The approach in this presentation is using SOM on multiple seismic attributes at one time, and in a sample-based, not wavelet, format.
Multi-attribute machine learning using SOM (which is an unsupervised learning process) can be shown to reveal details in the data not previously identified and which can be interpreted to be lithologic in nature. The detail comes with the statistical analysis of the data based on information on each sample on each trace in the data. The result of this sample-based statistical analysis is that one can interpret thin-bed resolution well below conventional wavelet tuning. This in turn, helps with highly accurate reservoir prediction when one ties the information to existing production or in the estimation of new reserves in exploration plays.
The key to the presentation is showing examples of problems in the every-day interpretation of data which can be solved by the neural analysis (Classification) of multiple seismic attributes. These problems could be reservoir delineation, exploration and exploitation for new reserves, interpretation of complicated stratigraphic sequences, or basic interpretation when the data is less than optimal. Highlighted will be several case histories where the use of SOM has helped with mapping carbonate-on-carbonate sequences, finding reservoirs in poor data quality, thin-bed definition of reservoirs, and subtle structural features not seen in regular amplitude data. Examples are also shown in using classification to tie to mud logs as well as electric logs.
Bio for Deborah King Sacrey
Deborah is a geologist/geophysicist with 45 years of oil and gas exploration experience in Texas, Louisiana Gulf Coast and Mid-Continent areas of the US. She received her degree in Geology from the University of Oklahoma in 1976 and immediately started working for Gulf Oil in their Oklahoma City offices.
She started her own company, Auburn Energy, in 1990 and built her first geophysical workstation using Kingdom software in 1996. She helped SMT/IHS for 18 years in developing and testing the Kingdom Software. She specializes in 2D and 3D interpretation for clients in the US and internationally. For the past 10 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 public, guided by Dr. Tom Smith, founder of SMT. She has become an expert in the use of Paradise software and has seven discoveries for clients using multi-attribute neural analysis.
Deborah has been very involved in the geological community, having served as President of the Houston Geological Society, President of the Division of Professional Affairs of AAPG and President of the Society of Independent Professional Earth Scientists (SIPES). She has also served as Treasurer of the AAPG, and has been on the Advisory Council as a Gulf Coast representative from Houston.
