Advances in Theoretical and Computational Energy Optimization Processes

Industry, construction and transport are the three sectors that traditionally lead to the highest energy requirements. This is why, over the past few years, all the involved stakeholders have widely expressed the necessity to introduce a new approach to the analysis and management of those energy processes characterizing the aforementioned sectors. The objective is to guide production and energy processes to an approach aimed at energy savings and a decrease in environmental impact. Indeed, all of the ecosystems are stressed by obsolete production schemes deriving from an unsustainable paradigm of constant growth and related to the hypothesis of an environment able to absorb and accept all of the anthropogenic changes

Advances in Theoretical and Computational Energy Optimization Processes

The paradigm in the design of all human activity that requires energy for its development must change from the past. We must change the processes of product manufacturing and functional services. This is necessary in order to mitigate the ecological footprint of man on the Earth, which cannot be considered as a resource with infinite capacities. To do this, every single process must be analyzed and modified, with the aim of decarbonising each production sector. This collection of articles has been assembled to provide ideas and new broad-spectrum contributions for these purposes

Methane emission inventory and forecasting in Malaysia

The increase in global surface temperature by 0.74 ± 0.18 oC between 1901 and 2000 as a result of global warming has become a serious threat. It is caused by the emission of greenhouse gases into the atmosphere due to human activities. The major greenhouse gases are carbon dioxide, methane and nitrous oxide. Records show that only carbon dioxide received detailed investigation but not methane, hence the motive behind this study. This study examined the emission of methane from six main sources in Malaysia. Data for the inventories of the production of these six sources were taken from 1980 – 2011 and were used to forecast emissions from 2012 – 2020. The data were sourced from Ministries, Departments and International Agencies. Six categories of animals were studied under livestock with their corresponding methane emissions from 1980 – 2011 computed as follows: cattle: 1993Gg (6.13%), buffaloes: 341Gg (10.8%), sheep: 24Gg (0.8%), goats: 55Gg (1.8%), horses: 3Gg (0.1%), poultry: 161Gg (5.1%), and pigs: 579Gg (18.3%). Methane emissions from the other sources from 1980 to 2011 are rice production: 1617Gg (0.02%), crude oil production: 8016636Gg (99.8%), Wastewater (POME): 11362Gg (0.14%), municipal solid waste landfills: 3294Gg (0.04%), coal mining: 14Gg (0.0002%). Forecasting of methane emissions from 2012 to 2020 were carried out using the Box-Jenkins ARIMA method. There were close similarities between the observed and forecast values. In the year 2020 predicted methane emissions will be cattle: 113Gg (72.2%), buffaloes: 8.0Gg (5.1%), sheep: 1.2Gg (0.8%), goats: 4.2 Gg (2.7%), horses: 0.2Gg (0.1%), pigs: 13.2Gg (8.4%), and poultry: 16.8Gg (10.7%) for the livestock sector. For other sectors the forecast will be wastewater: 836Gg for wastewater, 4.7 Gg for coal production, 503,208 Gg for crude oil production, 50.6 Gg for rice production, and 167 Gg from municipal solid waste landfills. Population and GDP will rise to 33.26 million and 329US $ billion by 2020, respectively. Optimisation was carried out after running a linear regression to determine the significant parameters. The equation developed was a nonlinear programming problem and was solved using sequential quadratic programming (SQL) and implemented on MATLAB environment. Sensitivity analysis carried out on the constraints showed the need to maintain the present livestock and rice production levels. The amount of meat protein currently available far exceeds the dietary protein requirement by more than five times. Several mitigation measures aimed towards reducing future methane emissions in Malaysia were also suggested for the various sources. These are in line with the country’s commitment to reduce greenhouse gas emissions by 40% over the 2005 level by 2020. The use of renewable energy in the energy mix was suggested in line with the government’s five fuel policy and increase in the number of vehicles using gas was also proposed

Methane concentration forward prediction using machine learning from measurements in underground mines

Unmanaged gases inside the mine airways are hazards to health and explosions, mainly methane (CH4) in coal mines. Temperature rise caused by heat release from the strata and machinery is another factor that may harm the health and safety of workers in underground mines. Control of methane and other gas components and the high temperature near a working face require overall localized ventilation management and adequate mine cooling systems. Continuously monitoring the in-situ atmospheric conditions and the number of contaminant gases, especially methane, are important factors for predicting the necessary actions for keeping the mine a safe and healthy place for workers. Studies are reported for predicting methane concentration variations inside underground mines using a long-short-term memory (LSTM) artificial recurrent neural network. Results will be compared to a simple time-series regression predictor (time-series filter). Different combinations of the variables and techniques are tested in the LSTM model to find the best results for accuracy and applicability. Forward time step variations are tested to explore the best prediction outcome. The results show that the LSTM model is limited to one-step-ahead prediction for reasonable accuracy. Furthermore, increasing the number of variables or the training window size does not seem to increase the accuracy of the LSTM predictions. Comparing the results using artificial data and the measured data from the mine, it is observed that the LSTM performs better if the data has a specific pattern and is as smooth as possible

Forecasting CO2 Sequestration with Enhanced Oil Recovery

The aim of carbon capture, utilization, and storage (CCUS) is to reduce the amount of CO2 released into the atmosphere and to mitigate its effects on climate change. Over the years, naturally occurring CO2 sources have been utilized in enhanced oil recovery (EOR) projects in the United States. This has presented an opportunity to supplement and gradually replace the high demand for natural CO2 sources with anthropogenic sources. There also exist incentives for operators to become involved in the storage of anthropogenic CO2 within partially depleted reservoirs, in addition to the incremental production oil revenues. These incentives include a wider availability of anthropogenic sources, the reduction of emissions to meet regulatory requirements, tax incentives in some jurisdictions, and favorable public relations. The United States Department of Energy has sponsored several Regional Carbon Sequestration Partnerships (RCSPs) through its Carbon Storage program which have conducted field demonstrations for both EOR and saline aquifer storage. Various research efforts have been made in the area of reservoir characterization, monitoring, verification and accounting, simulation, and risk assessment to ascertain long-term storage potential within the subject storage complex. This book is a collection of lessons learned through the RCSP program within the Southwest Region of the United States. The scope of the book includes site characterization, storage modeling, monitoring verification reporting (MRV), risk assessment and international case studies

Selected Papers from the 8th Annual Conference of Energy Economics and Management

This collection represents successful invited submissions from the papers presented at the 8th Annual Conference of Energy Economics and Management held in Beijing, China, 22–24 September 2017. With over 500 participants, the conference was co-hosted by the Management Science Department of National Natural Science Foundation of China, the Chinese Society of Energy Economics and Management, and Renmin University of China on the subject area of “Energy Transition of China: Opportunities and Challenges”. The major strategies to transform the energy system of China to a sustainable model include energy/economic structure adjustment, resource conservation, and technology innovation. Accordingly, the conference and its associated publications encourage research to address the major issues faced in supporting the energy transition of China. Papers published in this collection cover the broad spectrum of energy economics issues, including building energy efficiency, industrial energy demand, public policies to promote new energy technologies, power system control technology, emission reduction policies in energy-intensive industries, emission measurements of cities, energy price movement, and the impact of new energy vehicle

Assisted history matching using pattern recognition technology

Reservoir simulation and modeling is utilized throughout field development in different capacities. Sensitivity analysis, history matching, operations optimization and uncertainty assessment are the conventional analyses in full field model studies. Realistic modeling of the complexities of a reservoir requires a large number of grid blocks. As the complexity of a reservoir increases and consequently the number of grid blocks, so does the time required to accomplish the abovementioned tasks.;This study aims to examine the application of pattern recognition technologies to improve the time and efforts required for completing successful history matching projects. The pattern recognition capabilities of Artificial Intelligence and Data Mining (AI&DM;) techniques are used to develop a Surrogate Reservoir Model (SRM) and use it as the engine to drive the history matching process. SRM is a prototype of the full field reservoir simulation model that runs in fractions of a second. SRM is built using a small number of geological realizations.;To accomplish the objectives of this work, a three step process was envisioned:;• Part one, a proof of concept study: The goal of first step was to prove that SRM is able to substitute the reservoir simulation model in a history matching project. In this part, the history match was accomplished by tuning only one property (permeability) throughout the reservoir.;• Part two, a feasibility study: This step aimed to study the feasibility of SRM as an effective tool to solve a more complicated history matching problem, particularly when the degrees of uncertainty in the reservoir increase. Therefore, the number of uncertain reservoir properties increased to three properties (permeability, porosity, and thickness). The SRM was trained, calibrated, and validated using a few geological realizations of the base reservoir model. In order to complete an automated history matching workflow, the SRM was coupled with a global optimization algorithm called Differential Evolution (DE). DE optimization method is considered as a novel and robust optimization algorithm from the class of evolutionary algorithm methods.;• Part three, a real-life challenge: The final step was to apply the lessons learned in order to achieve the history match of a real-life problem. The goal of this part was to challenge the strength of SRM in a more complicated case study. Thus, a standard test reservoir model, known as PUNQ-S3 reservoir model in the petroleum engineering literature, was selected. The PUNQ-S3 reservoir model represents a small size industrial reservoir engineering model. This model has been formulated to test the ability of various methods in the history matching and uncertainty quantification. The surrogate reservoir model was developed using ten geological realizations of the model. The uncertain properties in this model are distributions of porosity, horizontal, and vertical permeability. Similar to the second part of this study, the DE optimization method was connected to the SRM to form an automated workflow in order to perform the history matching. This automated workflow is able to produce multiple realizations of the reservoir which match the past performance. The successful matches were utilized to quantify the uncertainty in the prediction of cumulative oil production.;The results of this study prove the ability of the surrogate reservoir models, as a fast and accurate tool, to address the practical issues of reservoir simulation models in the history matching workflow. Nevertheless, the achievements of this dissertation are not only aimed at the history matching procedure, but also benefit the other time-consuming operations in the reservoir management workflow (such as sensitivity analysis, production optimization, and uncertainty assessment)

Green Technologies for Production Processes

This book focuses on original research works about Green Technologies for Production Processes, including discrete production processes and process production processes, from various aspects that tackle product, process, and system issues in production. The aim is to report the state-of-the-art on relevant research topics and highlight the barriers, challenges, and opportunities we are facing. This book includes 22 research papers and involves energy-saving and waste reduction in production processes, design and manufacturing of green products, low carbon manufacturing and remanufacturing, management and policy for sustainable production, technologies of mitigating CO2 emissions, and other green technologies

Population-based algorithms for improved history matching and uncertainty quantification of Petroleum reservoirs

In modern field management practices, there are two important steps that shed light on a multimillion dollar investment. The first step is history matching where the simulation model is calibrated to reproduce the historical observations from the field. In this inverse problem, different geological and petrophysical properties may provide equally good history matches. Such diverse models are likely to show different production behaviors in future. This ties the history matching with the second step, uncertainty quantification of predictions. Multiple history matched models are essential for a realistic uncertainty estimate of the future field behavior. These two steps facilitate decision making and have a direct impact on technical and financial performance of oil and gas companies. Population-based optimization algorithms have been recently enjoyed growing popularity for solving engineering problems. Population-based systems work with a group of individuals that cooperate and communicate to accomplish a task that is normally beyond the capabilities of each individual. These individuals are deployed with the aim to solve the problem with maximum efficiency. This thesis introduces the application of two novel population-based algorithms for history matching and uncertainty quantification of petroleum reservoir models. Ant colony optimization and differential evolution algorithms are used to search the space of parameters to find multiple history matched models and, using a Bayesian framework, the posterior probability of the models are evaluated for prediction of reservoir performance. It is demonstrated that by bringing latest developments in computer science such as ant colony, differential evolution and multiobjective optimization, we can improve the history matching and uncertainty quantification frameworks. This thesis provides insights into performance of these algorithms in history matching and prediction and develops an understanding of their tuning parameters. The research also brings a comparative study of these methods with a benchmark technique called Neighbourhood Algorithms. This comparison reveals the superiority of the proposed methodologies in various areas such as computational efficiency and match quality

A survey on industry 4.0 for the oil and gas industry: upstream sector

The market volatility in the oil and gas (O&G) sector, the dwindling demand for oil due to the impact of COVID-19, and the push for alternative greener energy are driving the need for innovation and digitization in the O&G industry. This has attracted research interest from academia and the industry in the application of industry 4.0 (I4.0) technologies in the O&G sector. The application of some of these I4.0 technologies has been presented in the literature, but the domain still lacks a comprehensive survey of the application of I4.0 in the O&G upstream sector. This paper investigates the state-of-the-art efforts directed toward I4.0 technologies in the O&G upstream sector. To achieve this, first, an overview of the I4.0 is discussed followed by a systematic literature review from an integrative perspective for publications between 2012-2021 with 223 analyzed documents. The benefits and challenges of the adoption of I4.0 have been identified. Moreover, the paper adds value by proposing a framework for the implementation of I4.0 in the O&G upstream sector. Finally, future directions and research opportunities such as framework, edge computing, quantum computing, communication technologies, standardization, and innovative areas related to the implementation of I4.0 in the upstream sector are presented. The findings from this review show that I4.0 technologies are currently being explored and deployed for various aspects of the upstream sector. However, some of the I4.0 technologies like additive manufacturing and virtual reality are least explored