Submit to Sustainability Review for Sustainability Propose a Special Issue

Journal Browser

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

Special Issue Editors
Special Issue Information
Keywords
Published Papers

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 (26 March 2023) | Viewed by 46239

Share This Special Issue

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
Special Issues, Collections and Topics in MDPI journals

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 submissions that pass pre-check are 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 2400 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)

Download All Papers

Research

20 pages, 3481 KiB

Open AccessArticle

Environmental and Economic Water Management in Shale Gas Extraction

by José A. Caballero, Juan A. Labarta, Natalia Quirante, Alba Carrero-Parreño and Ignacio E. Grossmann

Sustainability 2020, 12(4), 1686; https://doi.org/10.3390/su12041686 - 24 Feb 2020

Cited by 18 | Viewed by 4404

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 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

(This article belongs to the Special Issue Optimization and Big Data Analytics to Improve Profitability and Sustainability of the Oil and Gas Industry)

► Show Figures

Graphical abstract

19 pages, 4501 KiB

Open AccessEditor’s ChoiceArticle

Prediction of the Rate of Penetration while Drilling Horizontal Carbonate Reservoirs Using the Self-Adaptive Artificial Neural Networks Technique

by Ahmad Al-AbdulJabbar, Salaheldin Elkatatny, Ahmed Abdulhamid Mahmoud, Tamer Moussa, Dhafer Al-Shehri, Mahmoud Abughaban and Abdullah Al-Yami

Sustainability 2020, 12(4), 1376; https://doi.org/10.3390/su12041376 - 13 Feb 2020

Cited by 43 | Viewed by 4554

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 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

(This article belongs to the Special Issue Optimization and Big Data Analytics to Improve Profitability and Sustainability of the Oil and Gas Industry)

► Show Figures

Figure 1

13 pages, 3415 KiB

Open AccessArticle

New Computational Artificial Intelligence Models for Generating Synthetic Formation Bulk Density Logs While Drilling

by Ahmed Gowida, Salaheldin Elkatatny, Saad Al-Afnan and Abdulazeez Abdulraheem

Sustainability 2020, 12(2), 686; https://doi.org/10.3390/su12020686 - 17 Jan 2020

Cited by 28 | Viewed by 3344

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 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

(This article belongs to the Special Issue Optimization and Big Data Analytics to Improve Profitability and Sustainability of the Oil and Gas Industry)

► Show Figures

Figure 1

22 pages, 6131 KiB

Open AccessArticle

A Data Size Reduction Approach Applicable in Process Control System of Oil and Gas Plants

by Reza Abbasinejad, Farzad Hourfar, Chandra Mouli R Madhuranthakam and Ali Elkamel

Sustainability 2020, 12(2), 639; https://doi.org/10.3390/su12020639 - 15 Jan 2020

Cited by 2 | Viewed by 3017

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 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

(This article belongs to the Special Issue Optimization and Big Data Analytics to Improve Profitability and Sustainability of the Oil and Gas Industry)

► Show Figures

Figure 1

39 pages, 4908 KiB

Open AccessArticle

A Stochastic Optimization Approach to the Design of Shale Gas/Oil Wastewater Treatment Systems with Multiple Energy Sources under Uncertainty

by Fadhil Y. Al-Aboosi and Mahmoud M. El-Halwagi

Sustainability 2019, 11(18), 4865; https://doi.org/10.3390/su11184865 - 05 Sep 2019

Cited by 22 | Viewed by 4148

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 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

(This article belongs to the Special Issue Optimization and Big Data Analytics to Improve Profitability and Sustainability of the Oil and Gas Industry)

► Show Figures

Figure 1

21 pages, 465 KiB

Open AccessArticle

Big Data Analytics Capabilities and Eco-Innovation: A Study of Energy Companies

by Russell Tatenda Munodawafa and Satirenjit Kaur Johl

Sustainability 2019, 11(15), 4254; https://doi.org/10.3390/su11154254 - 06 Aug 2019

Cited by 28 | Viewed by 6359

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, 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

(This article belongs to the Special Issue Optimization and Big Data Analytics to Improve Profitability and Sustainability of the Oil and Gas Industry)

► Show Figures

Figure 1

31 pages, 8680 KiB

Open AccessArticle

Optimal Production Planning and Pollution Control in Petroleum Refineries Using Mathematical Programming and Dispersion Models

by Amani Alnahdi, Ali Elkamel, Munawar A. Shaik, Saad A. Al-Sobhi and Fatih S. Erenay

Sustainability 2019, 11(14), 3771; https://doi.org/10.3390/su11143771 - 10 Jul 2019

Cited by 8 | Viewed by 4176

Abstract

Oil refineries, producing a large variety of products, are considered as one of the main sources of air contaminants such as sulfur oxides (SO_x), hydrocarbons, nitrogen oxides (NO_x), 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 NO_x, SO_x, 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 NO_x, SO_x, 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

(This article belongs to the Special Issue Optimization and Big Data Analytics to Improve Profitability and Sustainability of the Oil and Gas Industry)

► Show Figures

Figure 1

15 pages, 3823 KiB

Open AccessArticle

Support Vector Machine Algorithm for Automatically Identifying Depositional Microfacies Using Well Logs

by Dahai Wang, Jun Peng, Qian Yu, Yuanyuan Chen and Hanghang Yu

Sustainability 2019, 11(7), 1919; https://doi.org/10.3390/su11071919 - 31 Mar 2019

Cited by 18 | Viewed by 4442

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 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

(This article belongs to the Special Issue Optimization and Big Data Analytics to Improve Profitability and Sustainability of the Oil and Gas Industry)

► Show Figures

Figure 1

31 pages, 6308 KiB

Open AccessArticle

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

by Myung-Hun Kim, Eul-Bum Lee and Han-Suk Choi

Sustainability 2018, 10(7), 2469; https://doi.org/10.3390/su10072469 - 14 Jul 2018

Cited by 13 | Viewed by 7396

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 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

(This article belongs to the Special Issue Optimization and Big Data Analytics to Improve Profitability and Sustainability of the Oil and Gas Industry)

► Show Figures

Figure 1

10 pages, 240 KiB

Open AccessCommunication

Measuring the Economic Benefits of Industrial Natural Gas Use in South Korea

by Hyo-Jin Kim, Su-Mi Han and Seung-Hoon Yoo

Sustainability 2018, 10(7), 2239; https://doi.org/10.3390/su10072239 - 29 Jun 2018

Cited by 1 | Viewed by 2429

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 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

(This article belongs to the Special Issue Optimization and Big Data Analytics to Improve Profitability and Sustainability of the Oil and Gas Industry)

Journal Menu

Journal Browser

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

Share This Special Issue

Special Issue Editor

Special Issue Information

Keywords

Published Papers (10 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI