194 research outputs found

    Visual Techniques for Geological Fieldwork Using Mobile Devices

    Get PDF
    Visual techniques in general and 3D visualisation in particular have seen considerable adoption within the last 30 years in the geosciences and geology. Techniques such as volume visualisation, for analysing subsurface processes, and photo-coloured LiDAR point-based rendering, to digitally explore rock exposures at the earth’s surface, were applied within geology as one of the first adopting branches of science. A large amount of digital, geological surface- and volume data is nowadays available to desktop-based workflows for geological applications such as hydrocarbon reservoir exploration, groundwater modelling, CO2 sequestration and, in the future, geothermal energy planning. On the other hand, the analysis and data collection during fieldwork has yet to embrace this ”digital revolution”: sedimentary logs, geological maps and stratigraphic sketches are still captured in each geologist’s individual fieldbook, and physical rocks samples are still transported to the lab for subsequent analysis. Is this still necessary, or are there extended digital means of data collection and exploration in the field ? Are modern digital interpretation techniques accurate and intuitive enough to relevantly support fieldwork in geology and other geoscience disciplines ? This dissertation aims to address these questions and, by doing so, close the technological gap between geological fieldwork and office workflows in geology. The emergence of mobile devices and their vast array of physical sensors, combined with touch-based user interfaces, high-resolution screens and digital cameras provide a possible digital platform that can be used by field geologists. Their ubiquitous availability increases the chances to adopt digital workflows in the field without additional, expensive equipment. The use of 3D data on mobile devices in the field is furthered by the availability of 3D digital outcrop models and the increasing ease of their acquisition. This dissertation assesses the prospects of adopting 3D visual techniques and mobile devices within field geology. The research of this dissertation uses previously acquired and processed digital outcrop models in the form of textured surfaces from optical remote sensing and photogrammetry. The scientific papers in this thesis present visual techniques and algorithms to map outcrop photographs in the field directly onto the surface models. Automatic mapping allows the projection of photo interpretations of stratigraphy and sedimentary facies on the 3D textured surface while providing the domain expert with simple-touse, intuitive tools for the photo interpretation itself. The developed visual approach, combining insight from all across the computer sciences dealing with visual information, merits into the mobile device Geological Registration and Interpretation Toolset (GRIT) app, which is assessed on an outcrop analogue study of the Saltwick Formation exposed at Whitby, North Yorkshire, UK. Although being applicable to a diversity of study scenarios within petroleum geology and the geosciences, the particular target application of the visual techniques is to easily provide field-based outcrop interpretations for subsequent construction of training images for multiple point statistics reservoir modelling, as envisaged within the VOM2MPS project. Despite the success and applicability of the visual approach, numerous drawbacks and probable future extensions are discussed in the thesis based on the conducted studies. Apart from elaborating on more obvious limitations originating from the use of mobile devices and their limited computing capabilities and sensor accuracies, a major contribution of this thesis is the careful analysis of conceptual drawbacks of established procedures in modelling, representing, constructing and disseminating the available surface geometry. A more mathematically-accurate geometric description of the underlying algebraic surfaces yields improvements and future applications unaddressed within the literature of geology and the computational geosciences to this date. Also, future extensions to the visual techniques proposed in this thesis allow for expanded analysis, 3D exploration and improved geological subsurface modelling in general.publishedVersio

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

    Get PDF
    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

    Ontology based data warehousing for mining of heterogeneous and multidimensional data sources

    Get PDF
    Heterogeneous and multidimensional big-data sources are virtually prevalent in all business environments. System and data analysts are unable to fast-track and access big-data sources. A robust and versatile data warehousing system is developed, integrating domain ontologies from multidimensional data sources. For example, petroleum digital ecosystems and digital oil field solutions, derived from big-data petroleum (information) systems, are in increasing demand in multibillion dollar resource businesses worldwide. This work is recognized by Industrial Electronic Society of IEEE and appeared in more than 50 international conference proceedings and journals

    RELATING STRATIGRAPHY, LITHOLOGIC FACIES, AND 3D SEISMIC ATTRIBUTES TO OIL PRODUCTION IN BAKKEN FORMATION WELLS, RED SKY AREA, WILLISTON BASIN, NORTH DAKOTA

    Get PDF
    The Bakken Formation is considered the most important hydrocarbon-bearing rock unit in the Williston Basin of North Dakota and is currently one of the most prolific unconventional resource plays in North America with a 2015, basin-wide production of approximately 1.1 million barrels of oil per day. Most production from the Bakken Formation is from wells drilled horizontally and completed using hydraulic fracturing in the relatively thin Bakken formation that varies in thickness from 0 to 160 feet (0 to 49 meters). Studies by previous workers in the Williston basin have identified six, field-scale “sweet spots” characterized by higher production relative to other areas of the Williston Basin. Previous workers have also recognized that significant variation in hydrocarbon production exists even at scales of 1000’s of feet to a few miles between individual wells within these known sweet spots. The objective of this thesis is to understand the causes of these localized, field-scale, production variations and how to best optimize future wells and completions. Unlike previous studies that focused on regional-scale sweet spots, the work presented here focuses on the field level, which is scale for drilling and completion decisions. I present an integrated interpretation of the geology, geophysics, and drilling and completion designs of wells targeting the Bakken Formation in the Red Sky area, Mountrail County, North Dakota, in order to explain localized variations in well productivity using my compilation of historical production statistics. Using a high-quality, time-migrated, 3D seismic survey combined with well logs, core data, well files, seismic attributes, and production statistics, I have identified the productive reservoir unit and its natural fracture patterns over an area of approximately 730 square miles. Using ArcGIS spatial analysis and TIBCO Spotfire analytical software, I present a simple method to quantify variations in historical production statistics and how these variations reflect geologic controls including facies and fracture patterns. Geologically, the most significant control on the “sweet spot” are thick sand bodies of the Middle Bakken Formation deposited in a tidal dominated barrier bar system. Structurally, the most significant control on sweet spots are areas of the least number of natural fractures as mapped using seismic attributes.Earth and Atmospheric Sciences, Department o

    Quantitative seismic interpretation and machine learning applications for subsurface characterization and modeling

    Get PDF
    Quantitative seismic interpretation and geostatistical modeling methods have been widely used for subsurface reservoir characterization. However, the task becomes challenging due to the reservoir complexity and limited well control. To address these challenges, this research explores workflows that combine supervised machine learning, quantitative seismic interpretation, and seismic-constraining reservoir modeling methods to effectively reduce uncertainty in predicting multiscale subsurface heterogeneity. These workflows help mitigate the risks and uncertainties of exploring and developing potential reservoirs for hydrocarbon exploration and production or subsurface carbon sequestration. Techniques applied in this study integrate multiple sources of data to characterize complex reservoirs across different fields in north America. This dissertation presents three case studies combining new and traditional subsurface characterization techniques at different scales. The research starts with supervised machine learning, 3D seismic data, and well-log information to map the seismic scale diagenetic imprint and its corresponding reservoir quality on a Permian Basin reservoir. Then, I present a workflow that integrates core-derived petrophysical measurements, well logs, and pre-stack seismic data through supervised machine learning to map the seismic-scale spatial variability of petrophysically significant facies of a carbonate reservoir targeted for carbon geosequestration. Lastly, I present a seismic-constrained reservoir modeling and simulation workflow that combines the seismic-scale petrophysically defined facies information with well log and core data to map small-scale stratigraphic variability of petrophysical properties, CO2 storage capacity, and subsurface fluid flow behavior for long-term carbon sequestration. The illustrated workflows showed that the subsurface properties, such as lithology and petrofacies information, could be extracted on a seismic scale with the help of supervised machine learning. Additionally, this information can be used to better constrain reservoir models and reduce uncertainty where the well control is sparse

    Woodford Shale enclosed mini-basin fill on the Hunton Paleo Shelf. A depositional model for unconventional resource shales

    Get PDF
    The exploration of unconventional hydrocarbon resources of the Woodford Shale in Oklahoma (USA) has focused on characterizing this formation as an entirely open marine deposit. The impact of recognizing the enclosed mini-basin fill settings remains under-explored. To better understand these effects, I propose a detailed integrated study to highlight how these depositional variations occur. It is necessary to perform a workflow that involves multidisciplinary integration of geological, geochemical (both organic and inorganic) and geophysical characterizations to identify the characteristics of these deposits, how they vary vertically in the stratigraphic section of the Woodford Shale (internal variations in organic matter content and type; variability of the major heavy elements; and differences in mineralogy), and how they are laterally dissimilar by analyzing and comparing different Woodford locations in the Oklahoman petroleum provinces. The enclosed mini-basin fill settings occur locally in areas of thicker (gross thickness greater than 200 ft) and more organic-rich Woodford Shale (greater than 5.5 % on average of total organic carbon TOC). By understanding the context of regional sea-level fluctuations in the Upper Devonian time, it is observed that the Woodford Shale is deposited upon a pre-existent carbonate platform, where this platform was previously eroded by karstification or incised valley development during regional sea level drops at the pre-Woodford time. These karst/incised valley-forming processes formed a regional erosional unconformity, which allowed the development of sinkholes, pockets, and pods with more accommodation space for Woodford Shale sediment deposition in enclosed mini-basin fill settings. These erosional unconformities can be identified in outcrops, cores, well logs, and on 3D seismic data sets. I propose that the localized and discontinuous enclosed mini-basin fills settings represented silled constricted oceanic circulation with higher bottom-water euxinia (high free sulfur), which had better conditions for accumulation and preservation of clay and organic matter particles than did the well-circulated, open marine settings. I interpret that these depositional differences provide recognizable patterns in bed thickness and organic matter variations inside the Woodford Shale. I propose that areas in Oklahoma with thicker Woodford enclosed mini-basin fill settings are stratigraphical variations that could economically produce more oil and gas than other areas deposited under more open marine conditions or thinner enclosed mini-basin fill intervals. I capture these intervals by determining which ones contain more organic matter, more hydrogen, lower oxygen, more amorphous organic matter (more oil-prone than gas prone), the differences in paleo water chemistry (water column stratification, higher water salinity, higher levels of anoxia and euxinia). I recognize that these enclosed mini-basin fill geochemical characteristics are combined with the identification of enrichments in detrital quartz and relatively high depletions in the clay content of the lithofacies. The enclosed mini-basin fill deposits not only accumulate more organic matter but present different petrophysical and mechanical characteristics that, when modeled, simulated and compared with reported production, recover higher volumes of hydrocarbons under the standard unconventional petroleum industry operational practices
    corecore