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Special Issue "Optimization and Big Data Analytics to Improve Profitability and Sustainability of the Oil and Gas Industry"

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Chemical Engineering and Technology".

Deadline for manuscript submissions: closed (30 September 2020).

Special Issue Editor

Prof. Dr. Ali Elkamel
E-Mail Website
Guest Editor
Department of Chemical Engineering, University of Waterloo, 200 University Avenue West, Waterloo, ON, N2L 3G1, Canada
Interests: energy and environmental engineering systems; air pollution modeling, simulation anenergy and environmental engineering systems; air pollution modeling; planning and optimization; sustainable development of the petrochemical industry.
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Special Issue Information

Dear Colleagues,

The oil and gas industry continues to have a great influence in the world, at least for the near foreseeable future.  The industry is usually divided into three sectors: Upstream, midstream, and downstream. The upstream sector involves the exploration and production of petroleum and natural gas including extraction, drilling, and support activities. The downstream sector involves transformations of the crude oil and raw natural gases into usable products (i.e., petroleum refining, gas processing, petrochemical industries). The midstream sector deals with the storage, distribution, marketing, and transportation issues. The quest for pollution prevention and increased pressure and demand for environmentally sustainable processes and products have been creating new challenges in the oil and gas industry. In order to face these challenges, optimization and big data analytics have been recognized to represent enabling technologies   that can be employed by the industry to remain competitive and at the same time improve its sustainability and reduce its environmental impact.

The scope of this Special Issue reflects the abovementioned challenges, and aims to present analytical tools and modeling strategies that can be readily used in the development and operation of the oil and gas industry to achieve minimum environmental impacts and maximum economic benefits.

Topics of interest include, but are not limited to:

  • Data mining and analytics in the oil and gas sector
  • Real time optimization
  • Energy management and energy return on energy investment
  • Dealing with fluctuating prices and economic uncertainty
  • Inherent safety
  • Environmental pollution, health, and safety
  • Planning and capacity expansion
  • Distribution and supply chain management
  • Process and enterprise-wide integration
  • Transportation and pipeline networks
  • Water management
  • Planning and scheduling
  • Renewable energy integration in oil and gas operations
Prof. Ali Elkamel
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Optimization
  • Big data
  • Simulation
  • Supply chain management
  • Enterprise-wide integration
  • Sustainability
  • Pollution prevention
  • Petroleum refining
  • Petrochemical industry

Published Papers (10 papers)

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Research

Article
Environmental and Economic Water Management in Shale Gas Extraction
Sustainability 2020, 12(4), 1686; https://0-doi-org.brum.beds.ac.uk/10.3390/su12041686 - 24 Feb 2020
Cited by 5 | Viewed by 1388
Abstract
This paper introduces a comprehensive study of the Life Cycle Impact Assessment (LCIA) of water management in shale gas exploitation. First, we present a comprehensive study of wastewater treatment in the shale gas extraction, including the most common technologies for the pretreatment and [...] Read more.
This paper introduces a comprehensive study of the Life Cycle Impact Assessment (LCIA) of water management in shale gas exploitation. First, we present a comprehensive study of wastewater treatment in the shale gas extraction, including the most common technologies for the pretreatment and three different desalination technologies of recent interest: Single and Multiple-Effect Evaporation with Mechanical Vapor Recompression and Membrane Distillation. The analysis has been carried out through a generic Life Cycle Assessment (LCA) and the ReCiPe metric (at midpoint and endpoint levels), considering a wide range of environmental impacts. The results show that among these technologies Multiple-Effect Evaporation with Mechanical Vapor Recompression (MEE-MVR) is the most suitable technology for the wastewater treatment in shale gas extraction, taking into account its reduced environmental impact, the high water recovery compared to other alternatives as well as the lower cost of this technology. We also use a comprehensive water management model that includes previous results that takes the form of a new Mixed-Integer Linear Programming (MILP) bi-criterion optimization model to address the profit maximization and the minimization Life Cycle Impact Assessment (LCIA), based on its results we discuss the main tradeoffs between optimal operation from the economic and environmental points of view. Full article
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Article
Prediction of the Rate of Penetration while Drilling Horizontal Carbonate Reservoirs Using the Self-Adaptive Artificial Neural Networks Technique
Sustainability 2020, 12(4), 1376; https://0-doi-org.brum.beds.ac.uk/10.3390/su12041376 - 13 Feb 2020
Cited by 20 | Viewed by 1255
Abstract
Rate of penetration (ROP) is one of the most important drilling parameters for optimizing the cost of drilling hydrocarbon wells. In this study, a new empirical correlation based on an optimized artificial neural network (ANN) model was developed to predict ROP alongside horizontal [...] Read more.
Rate of penetration (ROP) is one of the most important drilling parameters for optimizing the cost of drilling hydrocarbon wells. In this study, a new empirical correlation based on an optimized artificial neural network (ANN) model was developed to predict ROP alongside horizontal drilling of carbonate reservoirs as a function of drilling parameters, such as rotation speed, torque, and weight-on-bit, combined with conventional well logs, including gamma-ray, deep resistivity, and formation bulk density. The ANN model was trained using 3000 data points collected from Well-A and optimized using the self-adaptive differential evolution (SaDE) algorithm. The optimized ANN model predicted ROP for the training dataset with an average absolute percentage error (AAPE) of 5.12% and a correlation coefficient (R) of 0.960. A new empirical correlation for ROP was developed based on the weights and biases of the optimized ANN model. The developed correlation was tested on another dataset collected from Well-A, where it predicted ROP with AAPE and R values of 5.80% and 0.951, respectively. The developed correlation was then validated using unseen data collected from Well-B, where it predicted ROP with an AAPE of 5.29% and a high R of 0.956. The ANN-based correlation outperformed all previous correlations of ROP estimation that were developed based on linear regression, including a recent model developed by Osgouei that predicted the ROP for the validation data with a high AAPE of 14.60% and a low R of 0.629. Full article
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Article
New Computational Artificial Intelligence Models for Generating Synthetic Formation Bulk Density Logs While Drilling
Sustainability 2020, 12(2), 686; https://0-doi-org.brum.beds.ac.uk/10.3390/su12020686 - 17 Jan 2020
Cited by 16 | Viewed by 905
Abstract
Synthetic well log generation using artificial intelligence tools is a robust solution for situations in which logging data are not available or are partially lost. Formation bulk density (RHOB) logging data greatly assist in identifying downhole formations. These data are measured in the [...] Read more.
Synthetic well log generation using artificial intelligence tools is a robust solution for situations in which logging data are not available or are partially lost. Formation bulk density (RHOB) logging data greatly assist in identifying downhole formations. These data are measured in the field while drilling by using a density log tool in the form of either a logging while drilling (LWD) technique or (more often) by wireline logging after the formations are drilled. This is due to operational limitations during the drilling process. Therefore, the objective of this study was to develop a predictive tool for estimating RHOB while drilling using an adaptive network-based fuzzy interference system (ANFIS), functional network (FN), and support vector machine (SVM). The proposed model uses the mechanical drilling constraints as feeding input parameters, and the conventional RHOB log data as an output parameter. These mechanical drilling parameters are usually measured while drilling, and their responses vary with different formations. A dataset of 2400 actual datapoints, obtained from a horizontal well in the Middle East, were used to build the proposed models. The obtained dataset was divided into a 70/30 ratio for model training and testing, respectively. The optimized ANFIS-based model outperformed the FN- and SVM-based models with a correlation coefficient (R) of 0.93, and average absolute percentage error (AAPE) of 0.81% between the predicted and measured RHOB values. These results demonstrate the reliability of the developed ANFIS model for predicting RHOB while drilling, based on the mechanical drilling parameters. Subsequently, the ANFIS-based model was validated using unseen data from another well within the same field. The validation process yielded an AAPE of 0.97% between the predicted and actual RHOB values, which confirmed the robustness of the developed model as an effective predictive tool for RHOB. Full article
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Article
A Data Size Reduction Approach Applicable in Process Control System of Oil and Gas Plants
Sustainability 2020, 12(2), 639; https://0-doi-org.brum.beds.ac.uk/10.3390/su12020639 - 15 Jan 2020
Cited by 1 | Viewed by 1644
Abstract
In oil and gas plants, the cost of devices applicable for supervising and controlling systems directly depends on the transmission and storage systems, which are related to the data size of process variables. In this paper, process variables frequency-domain and statistical analysis results [...] Read more.
In oil and gas plants, the cost of devices applicable for supervising and controlling systems directly depends on the transmission and storage systems, which are related to the data size of process variables. In this paper, process variables frequency-domain and statistical analysis results have been studied to infer if there exists any possibility to reduce data size of the process variables without loss of any necessary information. Although automatic control is not applicable in a shutdown condition, for generalization of the obtained results, unscheduled shutdown data has also been analyzed and studied. The main goal of this paper is to develop an applicable algorithm for oil and gas plants to decrease the data size in controlling and monitoring systems, based on well-known and powerful mathematical techniques. The results show that it is possible to reduce the size of data dramatically (more than 99% for controlling, and more than 55% for monitoring purposes in comparison with existing methods), without loss of vital information and performance quality. Full article
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Article
A Stochastic Optimization Approach to the Design of Shale Gas/Oil Wastewater Treatment Systems with Multiple Energy Sources under Uncertainty
Sustainability 2019, 11(18), 4865; https://0-doi-org.brum.beds.ac.uk/10.3390/su11184865 - 05 Sep 2019
Cited by 8 | Viewed by 1438
Abstract
The production of shale gas and oil is associated with the generation of substantial amounts of wastewater. With the growing emphasis on sustainable development, the energy sector has been intensifying efforts to manage water resources while diversifying the energy portfolio used in treating [...] Read more.
The production of shale gas and oil is associated with the generation of substantial amounts of wastewater. With the growing emphasis on sustainable development, the energy sector has been intensifying efforts to manage water resources while diversifying the energy portfolio used in treating wastewater to include fossil and renewable energy. The nexus of water and energy introduces complexity in the optimization of the water management systems. Furthermore, the uncertainty in the data for energy (e.g., solar intensity) and cost (e.g., price fluctuation) introduce additional complexities. The objective of this work is to develop a novel framework for the optimizing wastewater treatment and water-management systems in shale gas production while incorporating fossil and solar energy and accounting for uncertainties. Solar energy is utilized via collection, recovery, storage, and dispatch of heat. Heat integration with an adjacent industrial facility is considered. Additionally, electric power production is intended to supply a reverse osmosis (RO) plant and the local electric grid. The optimization problem is formulated as a multi-scenario mixed integer non-linear programming (MINLP) problem that is a deterministic equivalent of a two-stage stochastic programming model for handling uncertainty in operational conditions through a finite set of scenarios. The results show the capability of the system to address water-energy nexus problems in shale gas production based on the system’s economic and environmental merits. A case study for Eagle Ford Basin in Texas is solved by enabling effective water treatment and energy management strategies to attain the maximum annual profit of the entire system while achieving minimum environmental impact. Full article
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Article
Big Data Analytics Capabilities and Eco-Innovation: A Study of Energy Companies
Sustainability 2019, 11(15), 4254; https://0-doi-org.brum.beds.ac.uk/10.3390/su11154254 - 06 Aug 2019
Cited by 8 | Viewed by 2007
Abstract
Increased greenhouse gas (GHG) emissions in the past decades have created concerns about the environment. To stymie global warming and the deterioration of the natural environment, global CO2 emissions need to reach approximately 1.3 tons per capita by 2050. However, in Malaysia, [...] Read more.
Increased greenhouse gas (GHG) emissions in the past decades have created concerns about the environment. To stymie global warming and the deterioration of the natural environment, global CO2 emissions need to reach approximately 1.3 tons per capita by 2050. However, in Malaysia, CO2 output per capita—driven by fossil fuel consumption and energy production—is expected to reach approximately 12.1 tons by the year 2020. GHG mitigation strategies are needed to address these challenges. Cleaner production, through eco-innovation, has the potential to arrest CO2 emissions and buttress sustainable development. However, the cleaner production process has been hampered by lack of complete data to support decision making. Therefore, using the resource-based view, a preliminary study consisting of energy and utility firms is undertaken to understand the impact of big data analytics towards eco-innovation. Linear regression through SPSS Version 24 reveals that big data analytics could become a strong predictor of eco-innovation. This paper concludes that information and data are key inputs, and big data technology provides firms the opportunity to obtain information, which could influence its production process—and possibly help arrest increasing CO2 emissions. Full article
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Article
Optimal Production Planning and Pollution Control in Petroleum Refineries Using Mathematical Programming and Dispersion Models
Sustainability 2019, 11(14), 3771; https://0-doi-org.brum.beds.ac.uk/10.3390/su11143771 - 10 Jul 2019
Cited by 2 | Viewed by 1281
Abstract
Oil refineries, producing a large variety of products, are considered as one of the main sources of air contaminants such as sulfur oxides (SOx), hydrocarbons, nitrogen oxides (NOx), and carbon dioxide (CO2), which are primarily caused by [...] Read more.
Oil refineries, producing a large variety of products, are considered as one of the main sources of air contaminants such as sulfur oxides (SOx), hydrocarbons, nitrogen oxides (NOx), and carbon dioxide (CO2), which are primarily caused by fuel combustion. Gases emanated from the combustion of fuel in an oil refinery need to be reduced, as it poses an environmental hazard. Several strategies can be applied in order to mitigate emissions and meet environmental regulations. This study proposes a mathematical programming model to derive the optimal pollution control strategies for an oil refinery, considering various reduction options for multiple pollutants. The objective of this study is to help decision makers select the most economic pollution control strategy, while satisfying given emission reduction targets. The proposed model is tested on an industrial scale oil refinery sited in North Toronto, Ontario, Canada considering emissions of NOx, SOx, and CO2. In this analysis, the dispersion of these air pollutants is captured using a screening model (SCREEN3) and a non-steady state CALPUFF model based on topographical and meteorological conditions. This way, the impacts of geographic location on the concentration of pollutant emissions were examined in a realistic way. The numerical experiments showed that the optimal production and pollution control plans derived from the proposed optimization model can reduce NOx, SOx, and CO2 emission by up to 60% in exchange of up to 10.7% increase in cost. The results from the dispersion models verified that these optimal production and pollution control plans may achieve a significant reduction in pollutant emission in a large geographic area around the refinery site. Full article
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Article
Support Vector Machine Algorithm for Automatically Identifying Depositional Microfacies Using Well Logs
Sustainability 2019, 11(7), 1919; https://0-doi-org.brum.beds.ac.uk/10.3390/su11071919 - 31 Mar 2019
Cited by 8 | Viewed by 1562
Abstract
Depositional microfacies identification plays a key role in the exploration and development of oil and gas reservoirs. Conventionally, depositional microfacies are manually identified by geologists based on the observation of core samples. This conventional method for identifying depositional microfacies is time-consuming, and only [...] Read more.
Depositional microfacies identification plays a key role in the exploration and development of oil and gas reservoirs. Conventionally, depositional microfacies are manually identified by geologists based on the observation of core samples. This conventional method for identifying depositional microfacies is time-consuming, and only the depositional microfacies in a few wells can be identified due to the limited core samples in these wells. In this study, the support vector machine (SVM) algorithm is proposed to identify depositional microfacies automatically using well logs. The application of SVM includes the following steps: First, the depositional microfacies are determined manually in several wells with core samples. Then, the training sets used in the SVM algorithm are extracted from the well logs. Finally, a quantitative discrimination model based on the SVM algorithm is established to realize the classification of depositional microfacies. Field application shows that this innovative and constructive solution can be effectively used in uncored wells to identify depositional microfacies with a rate of accuracy approaching 84%. It overcomes the limitation of the conventional manual method which greatly contributes to the cost-saving of core analysis and improves the sustainable profitability of oil and gas exploration. Full article
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Article
Detail Engineering Completion Rating Index System (DECRIS) for Optimal Initiation of Construction Works to Improve Contractors’ Schedule-Cost Performance for Offshore Oil and Gas EPC Projects
Sustainability 2018, 10(7), 2469; https://0-doi-org.brum.beds.ac.uk/10.3390/su10072469 - 14 Jul 2018
Cited by 10 | Viewed by 2433
Abstract
Engineering, Procurement, and Construction (EPC) contractors with lump-sum turnkey contracts have recently been suffering massive profit losses due to re-works and schedule delays in offshore oil and gas EPC megaprojects. The main objective of this research is to develop and implement a detail [...] Read more.
Engineering, Procurement, and Construction (EPC) contractors with lump-sum turnkey contracts have recently been suffering massive profit losses due to re-works and schedule delays in offshore oil and gas EPC megaprojects. The main objective of this research is to develop and implement a detail engineering completion rating index system (DECRIS) to assist EPC contractors to optimize fabrication and construction works schedules while minimizing potential re-work/re-order. This is achieved through adequate detail design development and results in minimizing schedule delays and potential liquidated damages (i.e., delay penalties). The developed DECRIS was based on findings from an extensive review of existing literature, industry-led studies, expert surveys, and expert workshops. The DECRIS model is an evolution, and improvement of existing tools such as the project definition raking index (PDRI) and front-end loading (FEL) developed specifically for the early stage of engineering maturity assessment (i.e., planning, basic design, and front-end engineering design (FEED)), prior to EPC projects. The DECRIS was evaluated and validated with thirteen sample as-built offshore megaprojects completed recently. When the DECRIS was applied to the completed projects post-hoc, a correlation (R-squared 0.71) was found between DECRIS scores and schedule/cost performances. This is much superior to the PDRI-Industrial model’s correlation (R-squared 0.04), which was primarily devised for owners’ basic engineering or FEED completion assessment. Finally, as a means of further validation, project schedule and cost performance of an ongoing project was predicted based on the correlations found on the thirteen completed projects. The resultant predicted schedule and cost performance was well matched with the current project performance status. Based on the accuracy of the DECRIS model found in the validation, said model is an effective prospective tool for EPC contractors to manage their engineering and procurement/construction risks during the initial detail design stages. Full article
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Communication
Measuring the Economic Benefits of Industrial Natural Gas Use in South Korea
Sustainability 2018, 10(7), 2239; https://0-doi-org.brum.beds.ac.uk/10.3390/su10072239 - 29 Jun 2018
Cited by 1 | Viewed by 1049
Abstract
Natural gas (NG) is an important input used in the industrial production of South Korea. Therefore, the government requires quantitative information to be provided about the economic benefits of industrial NG (ING) use to decide whether to invest in expanding the supply of [...] Read more.
Natural gas (NG) is an important input used in the industrial production of South Korea. Therefore, the government requires quantitative information to be provided about the economic benefits of industrial NG (ING) use to decide whether to invest in expanding the supply of ING or not. This manuscript tries to measure the economic benefits of NG use in the manufacturing industry by using a specific case in South Korea. For this purpose, a trans-log production function is estimated using the data collected from a national survey of 1100 firms in South Korea. Of them, 299 firms used ING. For a representative manufacturing firm, the point estimated for the economic benefits of ING use is obtained as KRW 2409 (USD 2.07) per m3, which is statistically meaningful. The average price of ING, which is defined as the total expenditure on ING purchased in 2016 and divided by the total amount of ING purchased in 2016, was KRW 667 (USD 0.57) per m3. Therefore, the economic benefits of ING use are 3.61 times as great as the average price of ING. This implies that ING produces more value than its price in South Korea. Full article
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