Forecasting: theory and practice

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases

Machine Learning for Subsurface Data Analysis: Applications in Outlier Detection, Signal Synthesis and Core & Completion Data Analysis

Application of machine learning has become prominent in many fields and has captured the imaginations of various industries. The development of data driven algorithms and the ongoing digitization of subsurface geological measurements provide a world of opportunities to maximize the exploration and production of resources such as oil, gas, coal and geothermal energy. The current proliferation of data, democratization of state-of- the-art processing technology and computation power provide an avenue for both large and small industry players to maximize the use of their data to run more economic and efficient operations. The aim of this thesis is to discuss the development of robust data- driven methods and their effectiveness in providing insightful information about subsurface properties. The study opens with a brief overview of the current literature regarding application of data driven methods in the oil and gas industry. Outlier detection can be a strenuous task when data preprocessing for purposes of data- driven modeling. The thesis presents the efficacy of unsupervised outlier detection algorithms for various practical cases by comparing the performance of four outlier detection algorithms using appropriate metrics. Three case were created simulating: noisy measurements, measurements from washout formation and measurements from formations with several thin shale layers. It was observed that the Isolation Forest based model is efficient in detecting a wide range of outlier types with a balanced accuracy score of 0.88, 0.93 and 0.96 for the respective cases, while the DBSCAN based model was effective at detecting outliers due to noisy measurement with balanced accuracy score 0f 0.93. NMR measurements provide a wealth of geological information for petrophysical analysis and can be key in accurately characterizing a reservoir, however they are expensive and technically challenging to deploy, it has been shown in research that machine learning models can be effective in synthesizing some log data. However, predicting an NMR distribution where each depth is represented by several bins poses a different challenge. In this study, a Random Forest model was used for predicting the NMR T1 distribution in a well using relatively inexpensive and readily available well logs with an r2 score and corrected Mean absolute percentage error of 0.14 and 0.84. The predictions fall within the margin of error and an index was proposed to evaluate the reliability of each prediction based on a quantile regression forest to provide the user more information on the accuracy of the prediction when no data is available to test the model as will be the case in real world application. Using this method engineers and geologist can obtain NMR derived information from a well when no NMR tool has been run with a measure of reliability for each predicted sample/depth. Identifying sweet spots in unconventional formations can be the difference between an economically viable well and a money pit, in this study clustering techniques in conjunction with feature extraction methods were used to identify potential sweet spots in the Sycamore formation, elemental analysis of the clusters identified the carbonate concentration in sycamore siltstones as the key marker for porosity. This provided information as to why some layers had more production potential than the others. Machine learning algorithms were also used to identify key parameters that affect the productivity of an unconventional well using data from a simulation software. 11 completion parameters (lateral spacing, area (areal spacing), total vertical depth, lateral length, stages, perforation cluster, sand intensity, fluid intensity, pay thickness, fracture ½ length and fracture conductivity lateral length) were used to predict the EUR and IP90 using a random forest model and the normalized mean decrease in impurity was used to identify the key parameter. The lateral length was identified as the key parameter for estimated ultimate recovery and perforation clusters the key parameter for higher IP90 with a normalized mean decrease in impurity of 0.73 and 0.88 respectively. Machine learning methods can be integrated to optimize numerous industry workflows and therefore has huge potential in the oil and gas industry. It has found wide applications in automating mundane tasks like outlier detection, synthesizing pseudo-data when true data is not available and providing more information on technical operation for sound decision making

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

Embracing Analytics in the Drinking Water Industry

Analytics can support numerous aspects of water industry planning, management, and operations. Given this wide range of touchpoints and applications, it is becoming increasingly imperative that the championship and capability of broad-based analytics needs to be developed and practically integrated to address the current and transitional challenges facing the drinking water industry. Analytics will contribute substantially to future efforts to provide innovative solutions that make the water industry more sustainable and resilient. The purpose of this book is to introduce analytics to practicing water engineers so they can deploy the covered subjects, approaches, and detailed techniques in their daily operations, management, and decision-making processes. Also, undergraduate students as well as early graduate students who are in the water concentrations will be exposed to established analytical techniques, along with many methods that are currently considered to be new or emerging/maturing. This book covers a broad spectrum of water industry analytics topics in an easy-to-follow manner. The overall background and contexts are motivated by (and directly drawn from) actual water utility projects that the authors have worked on numerous recent years. The authors strongly believe that the water industry should embrace and integrate data-driven fundamentals and methods into their daily operations and decision-making process(es) to replace established ìrule-of-thumbî and weak heuristic approaches ñ and an analytics viewpoint, approach, and culture is key to this industry transformation