Machine learning applications for geoscience problems

Geoscientists have used machine learning for at least three decades and the applications spam many fields, from seismic processing and interpretation, to remote sensing classification, to analysis of well log data, among many others. More popular in some fields (e.g. seismic interpretation, remote sensing analysis) than others (e.g. paleontology), machine learning tools can leverage research in different areas of geoscience. Although machine learning is becoming more popular in different fields of geoscience, some concepts of more modern applications, convolutional neural networks in particular, are still vaguely understood by non-practitioners. I present some of the key concepts of machine learning with more details on the foundations of convolutional neural networks and some techniques that can help better understand convolutional neural networks behavior. I then present five case studies, mostly using convolutional neural networks and transfer learning. Transfer learning is a methodology that allow us to repurpose filters created by convolutional neural networks on a primary task to perform a secondary task. The five case studies start with a broader application of convolutional neural networks for different geoscience images, including thin-sections and core photographs. Then I present a how to perform core classification using convolutional neural networks. Next, how microfossils can be classified by the same methodology. I present a more detailed analysis of transfer learning using different remote sensing datasets. In the final case study, I show applications of supervised learning techniques to help forecast Megaelectron-Volt electrons inside Earth’s outer radiation belt. I conclude the dissertation with a summary and comments on the expectation of future research

Machine Learning for Seismic Exploration: where are we and how far are we from the Holy Grail?

Machine Learning (ML) applications in seismic exploration are growing faster than applications in other industry fields, mainly due to the large amount of acquired data for the exploration industry. The ML algorithms are constantly being implemented to almost all the steps involved in seismic processing and interpretation workflow, mainly for automation, processing time reduction, efficiency and in some cases for improving the results. We carried out a literature-based analysis of existing ML-based seismic processing and interpretation published in SEG and EAGE literature repositories and derived a detailed overview of the main ML thrusts in different seismic applications. For each publication, we extracted various metadata about ML implementations and performances. The data indicate that current ML implementations in seismic exploration are focused on individual tasks rather than a disruptive change in processing and interpretation workflows. The metadata shows that the main targets of ML applications for seismic processing are denoising, velocity model building and first break picking, whereas for seismic interpretation, they are fault detection, lithofacies classification and geo-body identification. Through the metadata available in publications, we obtained indices related to computational power efficiency, data preparation simplicity, real data test rate of the ML model, diversity of ML methods, etc. and we used them to approximate the level of efficiency, effectivity and applicability of the current ML-based seismic processing and interpretation tasks. The indices of ML-based processing tasks show that current ML-based denoising and frequency extrapolation have higher efficiency, whereas ML-based QC is more effective and applicable compared to other processing tasks. Among the interpretation tasks, ML-based impedance inversion shows high efficiency, whereas high effectivity is depicted for fault detection. ML-based Lithofacies classification, stratigraphic sequence identification and petro/rock properties inversion exhibit high applicability among other interpretation tasks

Shale lithofacies modeling of the Bakken Formation in the Williston basin, North Dakota

The Bakken petroleum system (Devonian-Mississippian) in the Williston basin of North Dakota and Montana in the United States, and Saskatchewan and Manitoba in Canada is one of the largest unconventional oil plays in North America. The Bakken Formation consists of three members: upper, middle, and lower. Both upper and lower members are shale (source rocks), whereas the middle member (reservoir rock) is composed of mixed lithologies, including sandstone, dolostone, and limestone. Underlying the lower Bakken shale member, the Three Forks Formation is another target for hydrocarbon exploration.;Although the middle Bakken member along with the Three Forks Formation have been the targets for horizontal drilling and hydraulic stimulation throughout the basin, several uncertainties remain, including facies variation due to depositional and diagenetic controls on mineral composition and organic matter content in the Bakken shale members, which could play a significant role in hydrocarbon generation and production. Although the Bakken shale members may look homogeneous in the appearance, they are significantly heterogeneous and complex mixture of quartz, smectite, illite, carbonate, pyrite, and kerogen in varying proportions. Improved characterization of the Bakken shale lithofacies is important to better understand depositional environment, lithofacies distribution, and their potential influence on hydrocarbon production.;The main objective of this work is to investigate vertical and lateral heterogeneities of the Bakken shale lithofacies, based on mineralogy and organic matter richness. Secondly, if the Bakken shale members are composed of different lithofacies, can they be associated with different depositional and/or diagenetic conditions, which could influence source, transportation, and preservation of organic matter and sediment in the Williston basin.;Core data (such as X-ray diffraction, X-ray fluorescence, and Total Organic Carbon content), conventional borehole geophysical logs (such as gamma, resistivity, bulk density, neutron porosity, and photo-electric factor), and advanced petrophysical logs (such as Spectral Gamma and Pulsed Neutron Spectroscopy) are used and integrated together to classify the Bakken shale lithofacies and build models of lithofacies distribution at multiple scales. Usually there are minimal core data, scattered advanced well logs, and ubiquitous conventional well log suites in a petroliferous basin, which hinders lithofacies analysis and petrophysical modeling. Therefore, a significant effort of this work is geared towards developing and applying cost-effective mathematical algorithms (such as Support Vector Machine and Artificial Neural Network etc.) and geostatistical techniques (such as Sequential Indicator Simulation) to classify, predict, and interpolate shale lithofacies with high accuracy, using conventional well log-derived petrophysical parameters from several wells.;The results show that both upper and lower Bakken shale members are vertically and laterally heterogeneous at core, well, and regional scales. Bakken shale members can be classified as five different lithofacies, in terms of mineralogy and organic matter content. Organic-rich shale lithofacies are more dominant than organic-poor shale lithofacies. It appears several factors (such as source of minerals, paleo-redox conditions, organic matter productivity, and preservation etc.) controlled the Bakken shale lithofacies distribution pattern. Silica in the Organic Siliceous Shale (OSS) lithofacies near the basin center is hypothesized to be related to the presence of biogenic silica (e.g. radiolaria), whereas the portion of OSS lithofacies near the basin margin is believed to be associated with eolian action. High organic matter content in the Organic Mudstone (OMD) lithofacies near the basin margin could be interpreted due to the presence of algal matter. The borehole geophysical, petrophysical approaches, and the 3D lithofacies modeling techniques developed in this study can be applied to detailed studies of complex shale formations and exploration of hydrocarbon resources worldwide

Machine assisted quantitative seismic interpretation

During the past decades, the size of 3D seismic data volumes and the number of seismic attributes have increased to the extent that it is difficult, if not impossible, for interpreters to examine every seismic line and time slice. Reducing the labor associated with seismic interpretation while increasing the reliability of the interpreted result has been an on going challenge that becomes increasingly more difficult with the amount of data available to interpreters. To address this issue, geoscientists often adopt concepts and algorithms from fields such as image processing, signal processing, and statistics, with much of the focus on auto-picking and automatic seismic facies analysis. I focus my research on adapting and improving machine learning and pattern recognition methods for automatic seismic facies analysis. Being an emerging and rapid developing topic, there is an endless list of machine learning and pattern recognition techniques available to scientific researchers. More often, the obstacle that prevents geoscientists from using such techniques is the “black box” nature of such techniques. Interpreters may not know the assumptions and limitations of a given technique, resulting in subsequent choices that may be suboptimum. In this dissertation, I provide a review of the more commonly used seismic facies analysis algorithms. My goal is to assist seismic interpreters in choosing the best method for a specific problem. Moreover, because all these methods are just generic mathematic tools that solve highly abstract, analytical problems, we have to tailor them to fit seismic interpretation problems. Self-organizing map (SOM) is a popular unsupervised learning technique that interpreters use to explore seismic facies using multiple seismic attributes as input. It projects the high dimensional seismic attribute data onto a lower dimensional (usually 2D) space in which interpreters are able to identify clusters of seismic facies. In this dissertation, using SOM as an example, I provide three improvements on the traditional algorithm, in order to present the information residing in the seismic attributes more adequately, and therefore reducing the uncertainly in the generated seismic facies map

Extreme Learning Machine combined with a Differential Evolution algorithm for lithology identification

Lithology identification, obtained through the analysis of several geophysical properties, has an important role in the process of characterization of oil reservoirs. The identification can be accomplished by direct and indirect methods, but these methods are not always feasible because of the cost or imprecision of the results generated. Consequently, there is a need to automate the procedure of reservoir characterization and, in this context, computational intelligence techniques appear as an alternative to lithology identification. However, to acquire proper performance, usually some parameters should be adjusted and this can become a hard task depending on the complexity of the underlying problem. This paper aims to apply an Extreme Learning Machine (ELM) adjusted with a Differential Evolution (DE) to classify data from the South Provence Basin, using a previously published paper as a baseline reference. The paper contributions include the use of an evolutionary algorithm as a tool for search on the hyperparameters of the ELM. In addition, an  activation function recently proposed in the literature is implemented and tested. The  computational approach developed here has the potential to assist in petrographic data classification and helps to improve the process of reservoir characterization and the production development planning