Abstract:
Finding Oil and Gas Using the Power of Machine Learning
Deborah King Sacrey
Since the late 1970’s the explosion of various kinds of seismic attributes derived from the acquired seismic signal has been the boon and the bane of the interpreter. Now the interpretation of reflection data requires powerful computers and advanced visualization software packages, but the interpreter is always looking for ways of distilling vast amounts of data down to essential volumes necessary to make prudent choices for reducing risk in picking drilling locations.
Seismic attributes are considered to be any measurable properties of the seismic signal. They can be measured at one instance in time or depth or a window of time or depth. They can be single trace measurements, multiple traces or even on a surface interpreted in the data. Common categories of seismic attributes would include the instantaneous (frequency, phase, Q), geometric (coherence, curvature), amplitude enhancing (sweetness, relative acoustic impedance, average energy), AVO (fluid factor, intercept gradient), spectral decomposition (either envelope based or wavelet based) and inversion (Poisson’s ratio, density, brittleness and more).
The use of Principal Component Analysis (PCA), which is a linear quantitative process designed to understand which seismic attributes have interpretative significance by analyzing the variations in the data, has proven to be an excellent approach to sorting through vast amounts of data. PCA used in the interpretation workflow can help determine meaningful seismic attributes, and in turn, these attributes can be used as input into neural analysis in the creation of Self-Organized Maps (SOM). SOM analysis is a pattern recognition process using unsupervised neural networks, and can reveal the natural clustering and patters in the data which often are distinct geological features not easily identifiable using singular seismic attributes.
Several case histories using PCA and SOM in both conventional and unconventional reservoirs will be reviewed to show the importance of these time-saving tools when added to the interpretation workflow.
Bio:
Deborah King Sacrey
Deborah is a geologist/geophysicist with 40 years of oil and gas exploration experience in the Texas and 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 five 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.
Deborah has been 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 also a DPA Certified Petroleum Geologist #4014 and DPA Certified Petroleum Geophysicist #2. She belongs to AAPG, SEG, PESA (Australia), SIPES, Houston Geological Society, South Texas Geological Society and the Oklahoma City Geological Society (OCGS).
