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

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

Generative adversarial networks review in earthquake-related engineering fields

Within seismology, geology, civil and structural engineering, deep learning (DL), especially via generative adversarial networks (GANs), represents an innovative, engaging, and advantageous way to generate reliable synthetic data that represent actual samples' characteristics, providing a handy data augmentation tool. Indeed, in many practical applications, obtaining a significant number of high-quality information is demanding. Data augmentation is generally based on artificial intelligence (AI) and machine learning data-driven models. The DL GAN-based data augmentation approach for generating synthetic seismic signals revolutionized the current data augmentation paradigm. This study delivers a critical state-of-art review, explaining recent research into AI-based GAN synthetic generation of ground motion signals or seismic events, and also with a comprehensive insight into seismic-related geophysical studies. This study may be relevant, especially for the earth and planetary science, geology and seismology, oil and gas exploration, and on the other hand for assessing the seismic response of buildings and infrastructures, seismic detection tasks, and general structural and civil engineering applications. Furthermore, highlighting the strengths and limitations of the current studies on adversarial learning applied to seismology may help to guide research efforts in the next future toward the most promising directions

Development of an unsupervised remote sensing methodology of detect surface leakage from terrestrial CO2 storage sites

Imperial Users onl

Enhancing the information content of geophysical data for nuclear site characterisation

Our knowledge and understanding to the heterogeneous structure and processes occurring in the Earth’s subsurface is limited and uncertain. The above is true even for the upper 100m of the subsurface, yet many processes occur within it (e.g. migration of solutes, landslides, crop water uptake, etc.) are important to human activities. Geophysical methods such as electrical resistivity tomography (ERT) greatly improve our ability to observe the subsurface due to their higher sampling frequency (especially with autonomous time-lapse systems), larger spatial coverage and less invasive operation, in addition to being more cost-effective than traditional point-based sampling. However, the process of using geophysical data for inference is prone to uncertainty. There is a need to better understand the uncertainties embedded in geophysical data and how they translate themselves when they are subsequently used, for example, for hydrological or site management interpretations and decisions. This understanding is critical to maximize the extraction of information in geophysical data. To this end, in this thesis, I examine various aspects of uncertainty in ERT and develop new methods to better use geophysical data quantitatively. The core of the thesis is based on two literature reviews and three papers. In the first review, I provide a comprehensive overview of the use of geophysical data for nuclear site characterization, especially in the context of site clean-up and leak detection. In the second review, I survey the various sources of uncertainties in ERT studies and the existing work to better quantify or reduce them. I propose that the various steps in the general workflow of an ERT study can be viewed as a pipeline for information and uncertainty propagation and suggested some areas have been understudied. One of these areas is measurement errors. In paper 1, I compare various methods to estimate and model ERT measurement errors using two long-term ERT monitoring datasets. I also develop a new error model that considers the fact that each electrode is used to make multiple measurements. In paper 2, I discuss the development and implementation of a new method for geoelectrical leak detection. While existing methods rely on obtaining resistivity images through inversion of ERT data first, the approach described here estimates leak parameters directly from raw ERT data. This is achieved by constructing hydrological models from prior site information and couple it with an ERT forward model, and then update the leak (and other hydrological) parameters through data assimilation. The approach shows promising results and is applied to data from a controlled injection experiment in Yorkshire, UK. The approach complements ERT imaging and provides a new way to utilize ERT data to inform site characterisation. In addition to leak detection, ERT is also commonly used for monitoring soil moisture in the vadose zone, and increasingly so in a quantitative manner. Though both the petrophysical relationships (i.e., choices of appropriate model and parameterization) and the derived moisture content are known to be subject to uncertainty, they are commonly treated as exact and error‐free. In paper 3, I examine the impact of uncertain petrophysical relationships on the moisture content estimates derived from electrical geophysics. Data from a collection of core samples show that the variability in such relationships can be large, and they in turn can lead to high uncertainty in moisture content estimates, and they appear to be the dominating source of uncertainty in many cases. In the closing chapters, I discuss and synthesize the findings in the thesis within the larger context of enhancing the information content of geophysical data, and provide an outlook on further research in this topic

9th Annual Jackson School of Geosciences Student Research Symposium, February 15, 2020

ConocoPhillipsGeological Science

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

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

Tracing back the source of contamination

From the time a contaminant is detected in an observation well, the question of where and when the contaminant was introduced in the aquifer needs an answer. Many techniques have been proposed to answer this question, but virtually all of them assume that the aquifer and its dynamics are perfectly known. This work discusses a new approach for the simultaneous identification of the contaminant source location and the spatial variability of hydraulic conductivity in an aquifer which has been validated on synthetic and laboratory experiments and which is in the process of being validated on a real aquifer