DMG2

Workshop on Data Mining for Geophysics and Geology
When May 03, 2018 11:40 to
May 05, 2018 11:40
Where San Diego, USA
Add event to calendar vCal
iCal

Data Mining for Geophysics and Geology SDM 2018

Modern geosciences have to deal with large quantities and a wide variety of data, including 2-D, 3-D and 4-D seismic surveys, well logs generated by sensors, detailed lithological records, satellite images and meteorological records. These data serve important industries, such as the exploration of mineral deposits and the production of energy (Oil and Gas, Geothermal, Wind, Hydroelectric), are important in the study of the earth crust to reduce the impact of earthquakes, in land use planning, and have a fundamental role in sustainability.

The volume of raw data being stored by different earth science archives today makes it impossible to rely on manual examination by scientists. The data volumes resultant of different sources, from terrestrial or aerial to satellite surveys, will reach a terabyte per day by the time all the planned satellites are flown. In particular, the oil industry has been using large quantities of data for quite a long time. Although there are published works in this area since the 70s, these days, the ubiquity of computing and sensor devices enables the collection of higher resolution data in real time, giving a new life to a mature industrial field. Understanding and finding value in this data has an impact on the efficiency of the operations in the oil and gas production chain. Efficiency gains are particularly important since the steep fall in oil prices in 2014, and represent an important opportunity for data mining and data science.

After the inaugural Workshop on Data Mining for Oil and Gas (DM4OG - SDM 2017) the scope of the meeting opened to the Geosciences. This workshop aims at bringing together researchers and practitioners from data science, data mining, forecasting, geosciences, petrochemistry, marine and petroleum geology, applied mathematics, and other disciplines, to explore the utilization of data mining techniques to develop intelligent solutions in the area of the geosciences with a special focus in the oil and gas exploration.

Topics of Interest:

Oil and gas exploration and production
Mineral deposit/reservoir identification and characterization
Exploration of well-log data
Earth crust analysis and understanding
Sensor data exploration
Remote sensing
Novel data mining problems in the geosciences
Visualization of big data in the geosciences
Geoscience data fusion for enhancing data mining solutions
Data streams analysis in geoscience
Feature extraction and data transformation from geoscientific data

Organizing Committee

Following up from the Workshop on Data Mining for Oil and Gas (SDM 2017), the organizing Committee joins researchers and practitioners from academia and industry:

Youzuo Lin
Youzuo Lin ylin@lanl.gov

Youzuo Lin is a staff scientist at Earth and Environmental Sciences Division, Los Alamos National Laboratory (LANL). He specializes in computational and applied mathematics with a focus on computational methods for inverse problems, machine learning, and image analysis. He has the integrated expertise in subsurface characterization and scientific computation needed for this problem. While at LANL, Lin has developed various computational techniques for several major research projects among multiple groups and teams. The project topics include geothermal exploration, CO2 storage, groundwater modeling, global seismology, remote sensing image analysis, and computational methods for large-scale machine learning. Prior to working at LANL, Lin received his Ph.D. in 2010 in Applied Mathematics from Arizona State University.

Weichang Li
Weichang Li weichang.lis@gmail.com

Weichang Li’s current research is focused on statistical signal processing and machine learning with applications in exploration geophysics and geology, production monitoring, industrial process control and material characterizations. Examples of his recent work at Aramco Research Center - Houston include robust seismic data denoising, improved seismic imaging, geological pattern recognition, acoustic tomography of multiphase flow and multi-modal characterization of geological samples using machine learning methods. He was the Data Analytics team lead at ExxonMobil Corporate Strategic Research from 2011-2013, responsible for developing research projects applying machine learning techniques to upstream, downstream and chemical business applications, and leading both internal research and university collaborations. Weichang obtained his Ph.D. (2006) in Electrical and Oceanographic Engineering, and a dual MS (2002) in EECS and Ocean Engineering, all from MIT. He is an active reviewer for IEEE Trans. Signal Processing, ICASSP, SEG, Geophysics, Applied Optics, Journal of Acoustical Society of America. He has chaired sessions in ASA annual meetings and IEEE Oceans Conferences.

Alípio Jorge
Alipio Jorge amjorge@fc.up.pt

Alípio Jorge is an associate professor at the Department of Computer Science of the Faculty of Science of the University of Porto (UP) and the coordinator of LIAAD , the Artificial Intelligence and Decision Support Lab of INESC-TEC. He holds a PhD in Computer Science by UP, and a MSc. on Foundations of Advanced Information Technology by the Imperial College. His research interests are Data Mining and Machine Learning, in particular association rules, web intelligence and data mining for decision support. His past research also includes Inductive Logic Programming and Collaborative Data Mining, and he lead research projects on data mining and web intelligence. He was Vice-President of APPIA the Portuguese Association for Artificial Intelligence. He co-chaired international conferences (Discovery Science 2009, ECML/PKDD 05 and EPIA 01), workshops, and seminars in data mining and artificial intelligence.

Rui L. Lopes
Rui L. Lopes rui.l.lopes@inesctec.pt

Rui L. Lopes is a researcher at the Institute for Computer and Systems Engineering, Science and Technology (INESC-TEC). He holds a M.Sc. in Informatics Engineering (2007) and a Ph.D. in Information Science and Technology (2015), from the University of Coimbra (Portugal). His research interests focus on Biologically Inspired Computing, Complex Systems, and Machine Learning, applied to diverse disciplines such as Operations Research, Earth Sciences, and Social Sciences. He has contributed to several conferences both as an author and as a member of the program committee. He co-organized three workshops in the Oil&Gas field, including a one-day event on educational challenges, and an intensive course on stratigraphic interpretation.

German Larrazabal
German Larrazabal german.larrazabal@repsol.com

German Larrazabal is a Senior R&D Geophysicist at Repsol USA. He holds a M.Sc. degree in Computer Science from Universidad Central de Venezuela, and a PhD in Computer Sciences, Suma Cum Laude award, from the University Polytechnic of Catalonia (UPC), Bercelona, Spain. He has been a Professor and Researcher at the University of Carabobo, Venezuela, at San Diego State University (USA), and at the University of Texas, El Paso (USA). He has been working on Research and Development projects related to the Oil&Gas industry. These projects targeted different areas, such as Reservoir Simulation, Geophysics, Geothermal, Gas, and simulation of water bodies.

Pablo Guillen
Pablo Guillen pgrondon@uh.edu

Pablo Guillen is a Research Assistant Professor/Faculty at the Center for Advanced Computing and Data Systems, University of Houston, Houston, TX, USA. He holds a B. Sc. degree in Mathematics and a M. Sc. degree in Applied Mathematics from Universidad de Los Andes, Merida, Venezuela, a PhD in Biomedical Engineering, Cum Laude award, from the Polytechnic University of Catalonia (UPC), Barcelona, Spain, and a Postdoc in Computational Science from University of Texas at El Paso, TX, USA. During the last 18 years he has been working on Research and Development projects related to Oil, Gas, and Biomedical Sciences. These projects have been in different areas such as: Artificial Intelligence, Data Mining, Data Reconciliation and Fault Diagnosis, Reservoir Simulation, Geophysics, Geothermal, Gas, Processing of Signals and Visualization. These projects were funded by Universities, Ministry of Science and Innovation and Companies. He has been a keynote speaker in several Conferences, and he has over 100 papers published in Journals and Conferences.

Program Committee

Paulo Azevedo

University of Minho, Braga, Portugal

Robertas Damaševičius

Kaunas University of Technology, Lithuania

Pablo Guillen

University of Houston, Texas, USA

Lei Huang

Curtin University, Sarawak, Malaysia

Alípio Jorge

University of Porto, Portugal

Weichang Li

ExxonMobil Corporate Strategic Research , USA

Youzou Lin

Los Alamos National Laboratory, New Mexico, USA

Hui Liu

University of Calgary, Alberta, Canada

Rui L. Lopes

INESCTEC, Porto, Portugal

Debotyam Maity

Gas Technology Institute, Illinois, USA

Ali Mohebbi

Shahid Bahonar University of Kerman

Carlos Monroy

Rice University, Houston, Texas, USA

Mirco Nanni

KDD-Lab ISTI-CNR Pisa, Italy

Eirini Ntoutsi

Gottfried Wilhelm Leibniz Universität Hannover, Germany

João Pedro Pedroso

University of Porto, Portugal

Omair Shafiq

Carleton University, Canada

Alireza Shahkarami

Saint Francis University, Pennsylvania, USA

Program

13:30 Opening session
Shahab D. Mohaghegh West Virginia University & Intelligent Solutions, Inc.

Shale Analytics: Contribution of Artificial Intelligence and Machine Learning to Unconventional Resources

14:30 Session 1
Chair: Youzuo Lin

“DeepDetect: A Deep Densely Connected Neural Network to Detect Seismic Events”

“SFA-GTM: Seismic Facies Analysis Based on Generative Topographic Map and RBF”

15:30 Coffee Break
15:45 Session 2
Chair: Weichang Li

“Earthquake Detection in 1-D Time Series Data with Feature Selection and Dictionary Learning”

“Support vector machine application for multiphase flow patterns prediction”

17:15 Closing Session Organizing committee.

Important Dates Updated

Deadline: February 1st, 2018

Notifications: March 1st, 2018

Conference Early-Registration: April 3rd, 2018

SDM 2018: May 3-5, 2018

Submissions 

All submitted papers will go through a rigorous double-blind peer review process, and the workshop proceedings will be published in electronic format, with CEUR-WS (indexed by DBLP, as well as Scopus). Alternatively, if CEUR's requirements are not met, the proceedings will be published using arXiv.


Full Papers (5 to 15 pages):

A full research paper should provide detail original research contributions. They must report new research results that represent a contribution to the field; sufficient details and support for the results and conclusions should also be provided. The work presented in regular papers is expected to be at a stage of maturity that with additional work can be published as journal papers.


Extended Abstracts (2 to 4 pages):

The extended abstracts should be 2-4 pages long, including figures and explanations. They should provide overall research methodologies with some results. The work presented in short papers are expected to be at a stage of maturity that with some additional work can be published as regular papers.


Workshop Presentations:

Accepted full papers will be given approximately 20 minutes for presentation, plus time for discussion. Selected extended abstracts will have approximately 10 minutes for presentation, plus time for discussion.


Manuscripts:

In order to produce the PDF, we provide a LaTex template, together with an example with author guidelines (download author guidelines). 


Instructions:

The full papers/extend abstracts must be submitted through the DMG2 submission site hosted at EasyChair.

Proceedings

The proceedings may be found at arXiv, by following the link bellow.

DMG2 Proceedings (arXiv)


INESC TEC - Laboratório Associado