Training of Crisis Mappers and Map Production from Multi-sensor Data: Vernazza Case Study (Cinque Terre National Park, Italy)

This aim of paper is to presents the development of a multidisciplinary project carried out by the cooperation between Politecnico di Torino and ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action). The goal of the project was the training in geospatial data acquiring and processing for students attending Architecture and Engineering Courses, in order to start up a team of "volunteer mappers". Indeed, the project is aimed to document the environmental and built heritage subject to disaster; the purpose is to improve the capabilities of the actors involved in the activities connected in geospatial data collection, integration and sharing. The proposed area for testing the training activities is the Cinque Terre National Park, registered in the World Heritage List since 1997. The area was affected by flood on the 25th of October 2011. According to other international experiences, the group is expected to be active after emergencies in order to upgrade maps, using data acquired by typical geomatic methods and techniques such as terrestrial and aerial Lidar, close-range and aerial photogrammetry, topographic and GNSS instruments etc.; or by non conventional systems and instruments such us UAV, mobile mapping etc. The ultimate goal is to implement a WebGIS platform to share all the data collected with local authorities and the Civil Protectio

A Review on the Application of Natural Computing in Environmental Informatics

Natural computing offers new opportunities to understand, model and analyze the complexity of the physical and human-created environment. This paper examines the application of natural computing in environmental informatics, by investigating related work in this research field. Various nature-inspired techniques are presented, which have been employed to solve different relevant problems. Advantages and disadvantages of these techniques are discussed, together with analysis of how natural computing is generally used in environmental research.Comment: Proc. of EnviroInfo 201

Stratigraphic interpretation of Well-Log data of the Athabasca Oil Sands of Alberta Canada through Pattern recognition and Artificial Intelligence

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.Automatic Stratigraphic Interpretation of Oil Sand wells from well logs datasets typically involve recognizing the patterns of the well logs. This is done through classification of the well log response into relatively homogenous subgroups based on eletrofacies and lithofacies. The electrofacies based classification involves identifying clusters in the well log response that reflect ‘similar’ minerals and lithofacies within the logged interval. The identification of lithofacies relies on core data analysis which can be expensive and time consuming as against the electrofacies which are straight forward and inexpensive. To date, challenges of interpreting as well as correlating well log data has been on the increase especially when it involves numerous wellbore that manual analysis is almost impossible. This thesis investigates the possibilities for an automatic stratigraphic interpretation of an Oil Sand through statistical pattern recognition and rule-based (Artificial Intelligence) method. The idea involves seeking high density clusters in the multivariate space log data, in order to define classes of similar log responses. A hierarchical clustering algorithm was implemented in each of the wellbores and these clusters and classifies the wells in four classes that represent the lithologic information of the wells. These classes known as electrofacies are calibrated using a developed decision rules which identify four lithology -Sand, Sand-shale, Shale-sand and Shale in the gamma ray log data. These form the basis of correlation to generate a subsurface model

Case-Based Reasoning of Man-Made Geohazards Induced by Rainfall on Transportation Systems

Due to global warming and environmental change, disastrous natural events have increased in scale and impact, e.g., Typhoon Morakot, in 2009 and 2011 Tōhoku earthquake and resulting tsunami in Japan. Hazard management is becoming increasingly important, making it a necessity to manage risk and fully understand critical scenarios. For example, the National Infrastructure Protection Plan of the United States emphasizes on lessons learned from past disasters. In this chapter, several selected cases of accidents caused by man-made geohazards in Taiwan are studied

Data Driven Approach To Saltwater Disposal (SWD) Well Location Optimization In North Dakota

The sharp increase in oil and gas production in the Williston Basin of North Dakota since 2006 has resulted in a significant increase in produced water volumes. Primary mechanism for disposal of produced water is by injection into underground Inyan Kara formation through Class-II Saltwater Disposal (SWD) wells. With number of SWD wells anticipated to increase from 900 to over 1400 by 2035, localized pressurization and other potential issues that could affect performance of future oil and SWD wells, there was a need for a reliable model to select locations of future SWD wells for optimum performance. Since it is uncommon to develop traditional geological and simulation models for SWD wells, this research focused on developing data-driven proxy models based on the CRISP-Data Mining pipeline for understanding SWD well performance and optimizing future well locations. NDIC’s oil and gas division was identified as the primary data source. Significant efforts went towards identifying other secondary data sources, extracting required data from primary and secondary data sources using web scraping, integrating different data types including spatial data and creating the final data set. Orange visual programming application and Python programming language were used to carry out the required data mining activities. Exploratory Data Analysis and clustering analysis were used to gain a good understanding of the features in the data set and their relationships. Graph Data Science techniques such as Knowledge Graphs and graph-based clustering were used to gain further insights. Machine Learning regression algorithms such as Multi-Linear Regression, k-Nearest Neighbors and Random Forest were used to train machine learning models to predict average monthly barrels of saltwater disposed in a well. Model performance was optimized using the RMSE metric and the Random Forest model was selected as the final model for deployment to predict performance of a planned SWD well. A multi-target regression model was trained using deep neural network to predict water production in oil and gas wells drilled in the McKenzie county of North Dakota

Using 3D printing for the instruction of petrophysical properties

textWith the recent increase in natural gas production, the demand for college educated petroleum engineers has increased. A greater number of high school graduates are now applying to petroleum engineering degree programs, however, the admission requirements to petroleum engineering schools are becoming increasingly stricter. Secondary educators have a greater challenge to better prepare students to compete for these positions and there is a need to introduce petrophysical concepts to students in the most effective manner. One petrophysical concept is porosity of rock. In this report, background information on rock formation and porosity of rocks is provided along with a brief summary on how 3D printers operate. But primarily, a lesson plan is presented to teach rock porosity in a novel way using 3D printed enlargements of porous rock from x-ray microtomography images of packed sand. The hypothesis was that students will gain greater understanding of petrophysical properties when using 3D prints of rocks. The porosity lesson with a lab using the 3D printed rocks was taught to a treatment group of 20 upcoming 6th graders. A porosity lesson without the use of 3D printed rocks was didactically taught to a control group of 14 additional 6th graders. Because of time limitations, not all of the students from the treatment group were able to experience all elements of the lab. However, every student in the control group received instruction and practice on how to calculate porosity of rock. The treatment group showed greater gain in learning the abstract concept about porosity that rocks of similar structure will have equivalent porosity regardless of grain size. However, the control group indicated greater gain learning the fundamental concepts of the definition of porosity, how to calculate porosity, and at being able to transfer their knowledge of percent porosity to a general problem about percentages. Despite the limited sample size and other sources of error, using 3D printed enlargements of rock was found to enhance students’ abilities to visualize abstract petrophysical properties. However, benefits from didactic instruction of fundamental concepts of petrophysical properties were found as well.Science, Technology, Engineering, and Mathematics Educatio

Regional analysis of Residual Oil Zone potential in the Permian Basin

textThis study provides independent analysis of Residual Oil Zones (ROZs) in the Permian Basin from a regional perspective, focusing on the formation mechanism and present ROZ locations. Results demonstrate widespread potential for ROZs, defined here as thick volumes of reservoir rock containing near-residual saturations of predominantly immobile oil formed by natural imbibition and displacement of oil by dynamic buoyant or hydrodynamic forces. Previous work suggests hydrodynamic forces generated by regional tectonic uplift drove widespread oil remobilization and ROZ creation. To test the hypothesis, uplift and tilting are quantified and the resulting peak regional potentiometric gradient used as a physical constraint to compute and compare predicted ROZ thicknesses from hydrodynamics for several ROZ-bearing San Andres fields with known ROZ thicknesses. Late-Albian Edwards Group geologic contacts, which are interpreted to have been deposited near sea level prior to uplift, are used as a regional datum. Approximate elevations determined for the present datum show ~1800 m of differential uplift since Edwards deposition, with an average regional slope of ~0.128˚. This post-Edwards tilting increased the pre-existing regional structural gradient of the San Andres Formation to ~0.289˚. Using the calculated post-Edwards gradient results in to prediction of ROZ thicknesses from hydrodynamics that is consistent with measured ROZ thicknesses at several fields. When compared with countervailing buoyancy forces, hydrodynamics is calculated to be the more dominant driving force of oil movement for reservoirs with structural dips less than 1.5˚, which is the common dip for San Andres Formation platform deposits where ROZs have been identified. To predict the location of ROZs, ROZ-related oil field properties were identified and analyzed for over 2,800 Permian Basin reservoirs. A strong basin-wide correlation between API and crude sulfur content is consistent with the expected outcome of oil degradation driven by oil-water interaction, and supports the use of API and sulfur content as proxies for ROZ potential in the Permian Basin. Spatial analysis of sulfur data shows that the highest probability for ROZ existence exists in Leonardian through Guadalupian-age reservoirs, distributed primarily in shelf and platform areas of Permian structures. Combined, these results support the widespread potential for ROZs across the Permian Basin generated primarily by regional scale tilting and resultant hydrodynamic forces.Geological Science

Training of Crisis Mappers and Map Production from Multi-sensor Data: Vernazza Case Study (Cinque Terre National Park, Italy)

This aim of paper is to presents the development of a multidisciplinary project carried out by the cooperation between Politecnico di Torino and ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action). The goal of the project was the training in geospatial data acquiring and processing for students attending Architecture and Engineering Courses, in order to start up a team of “volunteer mappers”. Indeed, the project is aimed to document the environmental and built heritage subject to disaster; the purpose is to improve the capabilities of the actors involved in the activities connected in geospatial data collection, integration and sharing. The proposed area for testing the training activities is the Cinque Terre National Park, registered in the World Heritage List since 1997. The area was affected by flood on the 25th of October 2011. According to other international experiences, the group is expected to be active after emergencies in order to upgrade maps, using data acquired by typical geomatic methods and techniques such as terrestrial and aerial Lidar, close-range and aerial photogrammetry, topographic and GNSS instruments etc.; or by non conventional systems and instruments such us UAV, mobile mapping etc. The ultimate goal is to implement a WebGIS platform to share all the data collected with local authorities and the Civil Protection