Date/Time
Date(s) - 06/11/2020
12:00 pm - 1:00 pm
Location
Via Webinar
Categories
The Unconventional Revolution in Exploration Geophysics –Machine Learning Applications and Economic Implications
Speaker- Nancy House
It is well known that geophysics, particularly the reflection seismic method, significantly changed the probability of economic success for conventional hydrocarbon deposits over the past 100 years. 3D seismic imaging revolutionized hydrocarbon exploration providing a robust picture of the subsurface and with modern acquisition techniques; further leveraged to provide insight into the properties of the reservoir fluids and recently the rock itself.
As unconventional resource plays took off, many North American Basins have been carpeted with high quality 3D seismic that is routinely be exploited to reduce drilling risks, improve completion efficiency and high-grade acreage based on seismic indicators of Total Organic Content (TOC), brittleness, or ‘fracability’. Recent advances in 3D seismic allow interpreters to map areas of higher productivity and identify bypassed reserves. MicroSeismic mapping has made completion more efficient and safer. Integration of Microseismic, drilling, production, and completion information can radically improve the outcome of an unconventional development project. Lately the use of high-quality 3D seismic integrated with reservoir, completion and production data is used to lower drilling risks, high grade acreage and quantify the extent of unconventional resources. (Stephen Rassenfoss, JPT Emerging Technology Senior Editor, 2015). Geophysical data is now an accepted early development tool of successful oil and gas companies.
Amplitude vs. Offset or Angle (AVO & AVA) recognized in the 1990’s to separate fluids in seismic amplitude anomalies; was extended to analyze changes in amplitude and travel time delays relative to azimuth to extract fracture and or stress orientation in formations.
By extending AVO to the pre-stack domain, it’s possible to simultaneously invert for Vp, Vs and density. Armed with these three fundamental rock properties that dictate elastic and inelastic rock response, researchers were able to combine those properties to tie directly to how well a rock will respond to hydraulic fracturing, or which rocks contain a higher TOC, or other rock properties that control how a rock responds to seismic waves or hydraulic fracturing.
Currently hundreds of different seismic attributes that are generated from 3D seismic data are used to identify the highest productive areas and how to develop them. Machine Learning applications help the interpreter to sift through hundreds of attributes to identify the key indicators of fluids, rock properties and the most productive areas. Estimated Ultimate Recovery (EUR) figures for undrilled wells can calculated when the right seismic attributes are correlated with production figures. However, it is critical that the data going into the Machine Learning (ML) algorithms has been acquired so that it is sensitive to those properties that are hardest to extract. Technical objectives need to be incorporated into the design, acquisition, processing and interpretation to enable the extraction of advanced properties from 3D seismic data.
The technical requirements of gathering and processing the seismic data that allow the extraction of reservoir and rock properties from tight rocks and shale reservoirs are high and consequently very costly. Proprietary acquisition underwritten by a single company is, as in the past, restricted to companies with large geologic and geophysical budgets. Speculative data acquisition and licensing is employed more and more to reduce the cost of 3D seismic to individual companies.
The balance between development and the market value of the gas or oil is critical. Through economic analysis incorporating the cost of geophysical information the long-term benefits of the investment. We propose a simple method to evaluate the investment in seismic using simple decision analysis coupled with expressing the cost in ways that are applicable to the full economic benefit of the data.
Nancy House, SEG President 2017/2018, has been a member of SEG for over 40 years, joining in 1978 as a graduate
student at CSM, has worked as a geophysicist for multinational corporations and
small independent oil companies primarily as an interpreter on and offshore US,
South America and Africa (West and East), and other areas. She is second-
generation geoscientist, growing up in South America and Singapore. She has a BA
in Geology/Geophysics from the University of Wyoming, (1976), an MSc in
Geophysics from Colorado School of Mines (1979), and did additional postgraduate
work at Colorado School of Mines in Reservoir Characterization, Economics and
Geophysics (2000-2002).
Nancy has served on numerous committees including, Global Affairs Committee,
Women’s Network Committee, Finance Committee, Membership Committees in SEG.
She served as Denver Geophysical Society President/Past President from 2008-
2010, General Chairman for SEG AM 2010, and Secretary -Treasurer 2011-2012,
Chairman of SEG Women’s Network Committee 2012-2013, and the Finance
Committee 2012-2014. She has been a regular contributor to TLE, presenter at
meetings (Best Poster 1995), a reviewer for Geophysics and a session chair for
various meetings. She also served on several task forces to understand critical
business issues around SEGs global activities. She has been a member of AAPG,
Dallas GS, Den GS, RMAG, DEG (Division Environmental Geology of AAPG), AGU,
AWG, and EAEG.
As SEG President 2017-2018 she focused on increasing diversity and inclusion in
the profession of geophysics and continue strategies implemented by Rd. Bradford
and Bill Abriel, to recognize the social contribution of Geophysics and applied
geophysics in areas such as pollution mitigation, groundwater location and mineral
exploration. As the industry evolves with the gradual replacement of oil and gas by
renewables, she hopes to expose the critical application of geophysics to effective
and efficient production of minerals needed for renewable energy resources.
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