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Geophysical Insights Announces Paradise Scripting Language

For Immediate Release

Geophysical Insights Announces Paradise Scripting Language

Society of Exploration Geophysicists, San Antonio, Texas – 16 September 2019

Geophysical Insights chose the Society of Exploration Geophysicists Annual Meeting in San Antonio to announce the launch of the Paradise Scripting Suite. Those universities in the Paradise University Program will be the first recipients of the new programming language that is specifically for geoscience application development.

The Paradise Scripting Language (PSL) is a powerful procedural language for geophysical signal and neural network analysis. PSL commands are written using the Paradise Scripting Editor (PSE), which allows for editing, building, and debugging with ease. The Paradise Script Processor (PSP), which runs the scripts, is a language processor constructed with Intel Fortran, Parallel Studio XE 2016 64-bit in Windows Studio Ultimate.

The PSL programming environment offers over 600 geoscience specific commands for analyzing geoscience data. Using the PSL geophysical toolbox, researchers can write custom commands that include unsupervised and supervised neural network analysis. PSL comes with pre-built scripts for seismic data conditioning, resampling, ranging, clipping, balancing, whitening, and others. Students and researchers can also develop new and custom functionality and share the PSL scripts with a growing Paradise development community via a PSL forum on the Paradise Customer Portal. Scripts can also be shared via personal GitHub accounts.

“By opening up our scripting suite to researchers inside and outside the academic community, we are empowering the next generation of geoscientists and petroleum engineers to create new geophysical data procedures, and leverage our machine learning and deep learning knowledge,” says Dr. Tom Smith, President & CEO of Geophysical Insights. “PSL and the supporting suite of tools will save investigators substantial time versus programming in general-purpose languages by providing a rich set of commands built for geoscience. Those writing routines in PSL will also have the benefit of using the Paradise® AI workbench to load, visualize, and operate on the output of their code, leveraging the off-the-shelf machine learning and deep learning capabilities of Paradise. PSL expands the Paradise ecosystem to data scientists and researchers who want to develop and apply AI and advanced data analytics”.

PSL and related suite of tools are only available currently to the Paradise University Program participants; however, accredited universities are welcome to apply to the program, which is free of charge. Complementing the launch of PSL, Geophysical Insights has introduced a new e-course by Dr. Tom Smith: Machine Learning Essentials for Seismic Interpretation.

About Geophysical Insights

Geophysical Insights was founded in 2008 by Dr. Tom Smith with the vision of applying machine learning methods to geologic interpretation to reduce the risk of exploration and the cost of field development. The mission of the business is to develop the next generation of interpretation tools in an intuitive, elegant interface that can be used by all interpreters. Over three years in development, Paradise had its commercial debut at the Society of Exploration Geophysicists (SEG) Annual Convention in 2013 and has seen steady worldwide adoption since its launch.

Geophysical Insights continues to build artificial intelligence capabilities in the Paradise workbench, which is proving to reveal deeper insights into the seismic response. Built for large sets of data, Paradise is a ‘Big Data’ solution that easily scales from a single workstation to an enterprise. With adaptive algorithms that learn from the data, interpreters can extract more information from seismic and well data than would otherwise be possible from traditional interpretation tools.

Media Contact:
Hal Green
Geophysical Insights
713.480.2260
[email protected]

Geophysical Insights Announces Call for Abstracts – University Challenge

Geophysical Insights Announces Call for Abstracts – University Challenge

Geophysical Insights – University Challenge Topics

Call for Abstracts

The following “Challenge Topics” are offered to universities who are part of the Paradise University Program. Those universities are encouraged to consider pursuing one or more of the topics below in their research work with Paradise® and related interpretation technologies. Students interested in researching and publishing on one or more of these topics are welcome to submit an abstract to Geophysical Insights, including an explanation of their interest in the topic. The management of Geophysical Insights will select the best abstract per Challenge Topic and provide a grant of $1,000 to each student upon the completion of the research work. Student(s) who undertake the research may count on additional forms of support from Geophysical Insights, including:

    • • Potential job interview after graduation
    • • Special recognition at the Geophysical Insights booth at a future SEG
    • • Occasional collaboration via web meeting, email, or phone with a senior geoscientist
    • • Inclusion in invitations to webinars hosted by Geophysical Insights on geoscience topics

Challenge Research Topics

Develop a geophysical basis for the identification of thin beds below classic seismic tuning

The research on this topic will investigate applications of new levels of seismic resolution afforded by multi-attribute Self-Organizing Maps (SOM), the unsupervised machine learning process in the Paradise software. The mathematical basis of detecting events below classical seismic tuning through simultaneous multi-attribute analysis – using machine learning – has been reported by Smith (2017) in an abstract submitted to SEG 2018. (Subsequently, the abstract has been placed online as a white paper resource). Examples of thin-bed resolution have been documented in a Frio onshore Texas reservoir, and in the Texas Eagle Ford Shale by Roden, et al., (2017). Therefore, the researcher is challenged to develop a better understanding of the physical basis for the resolution of events below seismic tuning vs. results from wavelet-based methods. Additional empirical results of the detection of thin beds are also welcomed. This approach has wide potential for both exploration and development in the interpretation of facies and stratigraphy and impact on reserve/resource calculations.  For unconventional plays, thin bed delineation will have a significant influence on directional drilling programs.

Determine the effectiveness of ‘machine learning’ determined geobodies in estimating reserves/resources and reservoir properties

The Paradise software has the capability of isolating and quantifying geobodies that result from a SOM machine learning process. Initial studies conducted with the technology suggest that the estimated reservoir volume is approximately what is being realized through the life of the field. This Challenge is to apply the geobody tool in Paradise along with other reservoir modeling techniques and field data to determine the effectiveness of geobodies in estimating reserves. If this proves to be correct, the estimating of reserves from geobodies could be done early in the lifecycle of the field, saving engineering time while reducing risk.

Corroborate SOM classification results to well logs or lithofacies

A challenge to cluster-based classification techniques is corroborating well log curves to lithofacies. Up to this point, such corroboration has been an iterative process of running different neural configurations and visually comparing each classification result to “ground truth”. Some geoscientists (results yet to be published) have used bivariate statistical analysis from petrophysical well logs in combination with the SOM classification results to develop a representation of the static reservoir properties, including reservoir distribution and storage capacity. The challenge is to develop a methodology incorporating SOM seismic results with lithofacies determination from well logs.

Explore the significance of SOM low-probability anomalies (DHIs, anomalous features, etc.)

In addition to a standard classification volume resulting from a SOM analysis, Paradise also produces a “Probability” volume that is composed of a probability value at each voxel for a given neural class (neuron). This technique is a gauge of the consistency of a feature to the surrounding region. Direct Hydrocarbon Indicators (DHIs) tend to be identified in the Paradise software as “low probability” or “anomalous” events because their properties are often inconsistent with the region. These SOM low probability features have been documented by Roden et al. (2015) and Roden and Chen (2017).  However, the Probability volume changes with the size of the region analyzed, and with respect to DHIs and anomalous features. This Challenge will determine the effectiveness of using the probability measure from a SOM result as a valid gauge of DHIs and set out the relationships among the optimum neural configuration, the size of the region, and extent of the DHIs.

Map detailed facies distribution from SOM results

SOM results have proven to provide detailed information in the delineation and distribution of facies in essentially any geologic setting (Roden et al., 2015; Roden and Santogrossi, 2017; Santogrossi, 2017). Due to the high-resolution output of appropriate SOM analysis, individual facies units can often be defined in much more detail than conventional interpretation approaches. Research topics should be related to determining facies distribution in different geological environments utilizing the SOM process, available well log curves, and regional knowledge of stratigraphy.

For more information on Paradise or the University Challenge Program, please contact:

Hal Green
Email: [email protected]
Mobile:  713.480.2260

Paradise 3.2 Press Release

For Immediate Release Media

Geophysical Insights Launches Paradise 3.2, adding Geobody Identification Through Machine Learning and Attribute Generation using High-Performance Computing

Houston, 2 August 2018 — Geophysical Insights announces the launch of Paradise 3.2, which includes the isolation of geobodies through machine learning, and the generation of attributes on a LINUX High-Performance Computing (HPC) facility. Paradise is a multi-attribute seismic analysis workbench that uses machine learning to extract more information from both seismic and well data. The new geobody analysis ThoughtFlow™ in Paradise derives geobodies from the Self-Organizing Map (SOM) machine learning process.  This new technology in Paradise is the first instance in the industry to generate geobodies automatically using machine-learning classification results.  The new geobody capability allows interpreters to isolate areas of interest based on a common neuron or set of neurons. Upon generation, interpreters can edit – prune or fill – geobodies at the individual seismic sample level. Given velocities alone, the geobody analysis tool will estimate volumetrics.  Geobodies can also be filtered for size and neural classes. Along with geobodies, the newest version of Paradise enables companies that have an HPC cluster to generate AASPI geometric and spectral decomposition attributes in a fraction of the time it would take on even a large, multi-core server. The HPC capability also supports both SLURM and LSF Scheduler formats to ensure that resources are managed effectively.  Paradise 3.2 offers other performance improvements, including faster SEGY data loading and a connector to Petrel 2017. “The goal of both these new tools, and of Paradise itself, is to increase the efficiency of the interpreter and leverage machine learning technology states Dr. Tom Smith, President/CEO of Geophysical Insights. “The ability to automatically generate geobodies using machine learning takes the industry to a new level of interpretation. And, including HPC capabilities in Paradise was a logical extension of the Paradise platform due to the increasing use of geometric and spectral decomposition attributes in large seismic volumes. Our goal is to enable every interpreter to leverage machine learning technology in ways that will provide more time for analysis and focus on what’s important – finding oil and gas. We are delighted to offer a dramatic new Paradise version that applies new machine learning processes and reduces the time to results.” About Geophysical Insights Geophysical Insights was founded in 2008 by Dr. Tom Smith with the vision of applying machine learning methods to geologic interpretation to reduce the risk of exploration and the cost of field development.  The mission of the business is to develop the next generation of interpretation tools in an intuitive interface that uses guided ThoughtFlows, which can be used by all interpreters.  Over three years in development, Paradise was released at the Society of Exploration Geophysicists (SEG) Annual Convention in 2013 and has been adopted by the International Oil Companies (IOCs) and National Oil Companies (NOCs) and worldwide since its launch. Geophysical Insights continues to build machine learning capabilities in the Paradise workbench, which is proving to reveal deeper insights into the seismic response. Built for large sets of data, Paradise is a ‘Big Data’ solution that easily scales from a single workstation to an enterprise. With adaptive algorithms that learn from the data that are enabled by high-performance computing, interpreters can extract more information from seismic and well data than would otherwise be possible from traditional interpretation tools.      

Contact: Hal Green, Director of Marketing, Geophysical Insights

713.480.2260

[email protected]

Paradise 3.1 Press Release

For Immediate Release

Media Contact:
Hal Green
Geophysical Insights
713.480.2260
[email protected]

Geophysical Insights Launches the Attribute Generator with an Expanded Library of Advanced Seismic Attributes

Houston, Texas, 24 April 2017
Geophysical Insights announced the launch of the new Attribute Generator in the latest release of Paradise® – version 3.1.  Paradise is a multi-attribute analysis platform that uses machine learning to extract more information from seismic and well data.  The new Attribute Generator features a library of over 100 attributes, including instantaneous, geometric, and spectral decomposition.  A key component of the release is inclusion of a library of spectral decomposition and geometric attributes by the AASPI  (Attribute Assisted Seismic Processing and Interpretation) Consortium at The University of Oklahoma.  In October 2016, Geophysical Insights announced an agreement with The University of Oklahoma to license the AASPI algorithms and make them available through guided Thought-Flows™ in the Paradise platform.  With the launch of the new Paradise Attribute Generator, these advanced attributes can now be used by more interpreters across the oil and gas industry, and in any geologic setting.  Version 3.1 of Paradise also includes RGB Blending and a “drag and drop” connector to Petrel. 

“We reviewed various sources of seismic advanced attributes, including developing our own, and found those from the AASPI Consortium represent the best in the industry,” according to Dr. Tom Smith, President and CEO of Geophysical Insights.  “Under Dr. Kurt Marfurt’s leadership, researchers at the AASPI Consortium are at the frontier of seismic attribute research, and we are proud to leverage AASPI technology.  The addition of the Attribute Generator advances our strategy of making Paradise the destination for attributes, machine learning, and multi-attribute analysis.”  

About the AASPI Consortium at the University of Oklahoma (OU)
The AASPI Consortium, which is led by Dr. Kurt Marfurt with the University of Oklahoma’s Mewbourne College of Earth and Energy, is a world-class research organization whose members include companies across many sectors of the oil and gas industry.  Their goal is to serve as a research component for independent and intermediate-sized oil and gas companies, domestic North American business units of large companies and national oil companies dealing with imaging subtle structures in land data and marine environments. In exchange for geologic insight, well control, 3D seismic data and consortium funding from sponsor companies, they generate and evaluate emerging technologies that can influence costly drilling and completion decisions.

Housing the AASPI Consortium, the Mewbourne College of Earth and Energy at the University of Oklahoma consists of the Mewbourne School of Petroleum and Geological Engineering, the ConocoPhillips School of Geology and Geophysics and the Oklahoma Geological Survey. The schools are consistently rated among the top five petroleum engineering, petroleum geology and geophysics programs in the country.

About Geophysical Insights
Geophysical Insights was founded in 2008 by Dr. Tom Smith with the vision of applying machine learning methods to geologic interpretation to reduce the risk of exploration and the cost of field development.  The mission of the business is to develop the next generation of interpretation tools in an intuitive, elegant interface that can be used by all interpreters.  Over three years in development, Paradise was commercially launched at the Society of Exploration Geophysicists (SEG) Annual Convention in 2013 and has seen steady adoption in the industry since its launch. 

Geophysical Insights continues to build capabilities in the Paradise platform, which is proving to reveal deeper insights in the seismic response through multi-attribute analysis. Built for large sets of data, Paradise is a ‘Big Data’ solution that easily scales from a single workstation to an enterprise. With adaptive algorithms that learn from the data that are enabled by low-cost, high performance computing, interpreters are able to extract more information from seismic and well data than would otherwise be possible from traditional interpretation tools.

Dr. Tom Smith receives the Natural Science & Mathematics Distinguished Alumni Award from the University of Houston

Distinguished Alumni Awards are bestowed upon alumni of the College of Natural Sciences and Mathematics for exceptional achievement in their professional field, involvement in the community, and demonstrated innovative change to improve the lives of others through their work.

See more on the event here: http://www.uh.edu/nsm/people/distinguished-alumni/index

Dr. Tom Smith

President/CEO | Geophysical Insights

Dr. Tom Smith received a BS and MS degree in Geology from Iowa State University. His graduate research focused on a shallow refraction investigation of the Manson astrobleme. In 1971, he joined Chevron Geophysical as a processing geophysicist but resigned in 1980 to complete his doctoral studies in 3D modeling and migration at the Seismic Acoustics Lab at the University of Houston. Upon graduation with the Ph.D. in Geophysics in 1981, he started a geophysical consulting practice and taught seminars in seismic interpretation, seismic acquisition and seismic processing. Dr. Smith founded Seismic Micro-Technology in 1984 to develop PC software to support training workshops which subsequently led to development of the KINGDOM Software Suite for integrated geoscience interpretation with world-wide success.

The Society of Exploration Geologists (SEG) recognized Dr. Smith’s work with the SEG Enterprise Award in 2000, and in 2010, the Geophysical Society of Houston (GSH) awarded him an Honorary Membership. Iowa State University (ISU) has recognized Dr. Smith throughout his career with the Distinguished Alumnus Lecturer Award in 1996, the Citation of Merit for National and International Recognition in 2002, and the highest alumni honor in 2015, the Distinguished Alumni Award.  The University of Houston College of Natural Sciences and Mathematics recognized Dr. Smith with the 2017 Distinguished Alumni Award.

In 2009, Dr. Smith founded Geophysical Insights, where he leads a team of geophysicists, geologists and computer scientists in developing advanced technologies for fundamental geophysical problems.  The company launched the Paradise® multi-attribute analysis software in 2013, which uses Machine Learning and pattern recognition to extract greater information from seismic data.

Dr. Smith has been a member of the SEG since 1967 and is a professional member of SEG, GSH, HGS, EAGE, SIPES, AAPG, Sigma XI, SSA and AGU. Dr. Smith served as Chairman of the SEG Foundation from 2010 to 2013.  On January 25, 2016, he was recognized by the Houston Geological Society (HGS) as a geophysicist who has made significant contributions to the field of geology.  He currently serves on the SEG President-Elect’s Strategy and Planning Committee and the ISU Foundation Campaign Committee for Forever True, For Iowa State.