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Unconventional hydrocarbon resources: geological statistics, petrophysical characterization, and field development strategies

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JOURNAL OF PETROLEUM EXPLORATION AND PRODUCTION TECHNOLOGY 2022年第6期12卷 1463-1488页

作者： Muther, Temoor Qureshi, Haris Ahmed Syed, Fahad Iqbal Aziz, Hassan Siyal, Amaar Dahaghi, Amirmasoud Kalantari Negahban, Shahin Univ Kansas Dept Chem & Petr Engn Lawrence KS 66045 USA Al Sadim Oil Serv Tripoli Libya Dawood Univ Engn & Technol Dept Petr & Gas Engn Karachi Pakistan Khalifa Univ Abu Dhabi U Arab Emirates

Hydrocarbons exist in abundant quantity beneath the earth's surface. These hydrocarbons are generally classified as conventional and unconventional hydrocarbons depending upon their nature, geology, and exploitation procedure. Since the conventional hydrocarbons are under the depletion phase, the unconventional hydrocarbons have been a major candidate for current and future hydrocarbon production. Additionally, investment and research have increased significantly for its exploitation. Having the shift toward unconventional hydrocarbons, this study reviews in depth the technical aspects of unconventional hydrocarbons. This review brings together all the important aspects of unconventional reservoirs in single literature. This review at first highlights the worldwide unconventional hydrocarbon resources, their technical concept, distribution, and future supplies. A portion of this study also discusses the resources of progressive unconventional hydrocarbon candidates. Apart from this, this review also highlights the geological aspects of different unconventional hydrocarbon resources including tight, shale, and coalbed methane. The petrophysical behavior of such assists including the response to well logs and the discussion of improved correlation for petrophysical analysis is a significant part of this detailed study. The variation in geology and petrophysics of unconventional resources with conventional resources are also presented. In addition, the latest technologies for producing unconventional hydrocarbons ranging from fractured wells to different fluid injections are discussed in this study. In the end, the latest machine learning and optimization techniques have been discussed that aids in the optimized field development planning of unconventional reservoirs.

关键词： Unconventional hydrocarbons Machine learning optimization algorithms Shale and tight resources Geology Petrophysics Coalbed methane Unconventional reservoir development

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Multi-Strategy Improvement of Dun Beetle Optimizer 24

Multi-Strategy Improvement of Dun Beetle Optimizer

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2024 7th International Conference on Computer Information Science and Artificial Intelligence, CISAI 2024

作者： Cui, Jiayu Southwestern University of Finance and Economics Sichuan Chengdu China

ISBN: (纸本)9798400707254

A multi-strategy improved Dun Beetle Optimizer (MSIDBO) is proposed by this paper to address low convergence accuracy, weak global exploration capability, and susceptibility to local optima in the standard DBO. The MSIDBO algorithm improves DBO by incorporating dual Levy flight random numbers, adaptive adjustment, and probability perturbation strategies, which effectively balances the development and exploration of the optimization algorithm and enhances its optimization performance. Apart from its evaluation on 12 benchmark test functions, this algorithm is compared with various efficient optimization algorithms, demonstrating superior optimization performance compared to the selected benchmark algorithms and providing valuable references for practical engineering applications. © 2024 Copyright held by the owner/author(s).

关键词： optimization algorithms

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Optimal Scheduling Strategy for Microgrid in Service Area Based on Improved Sparrow Search Algorithm

Optimal Scheduling Strategy for Microgrid in Service Area Ba...

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International Conference of Electrical, Electronic and Networked Energy Systems, EENES 2024

作者： Xu, Xianfeng Jiang, Xinchen Wu, Jiahao Wu, Chunling Huang, Xinrong Lu, Yong College of Energy and Electrical Engineering Chang’an University Shaanxi Xi’an710064 China

ISBN: (纸本)9789819620791

It is significant to utilize renewable energy in expressway service area, establish grid-connected microgrid and manage its energy scheduling. To minimize the cost of service area microgrid, an energy optimization scheduling strategy for microgrid based on improved sparrow search algorithm(SSA) is proposed. Firstly, the load characteristics of the service area were analyzed, and the optimal scheduling model of the microgrid suitable for the service area was established. Aiming at the problems that the optimal scheduling model is difficult to solve and the traditional SSA algorithm converges too early and will fall into local optimum, the golden sine strategy and the adaptive T-distribution are introduced to improve the SSA. It can improve the optimization ability and convergence rate of the algorithm in solving the model. Taking a microgrid in a service area as an example, the results show that approach of this paper can reduce the daily operating cost by 12.5% compared with PSO, and reduce the operating cost by 7.1% compared with traditional SSA. The effectiveness of the presented method in solving the microgrid scheduling in service area is verified. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： optimization algorithms

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Application of Nature-inspired algorithms for Optimising Photovoltaic System Energy Production 33

Application of Nature-inspired Algorithms for Optimising Pho...

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33rd Southern African Universities Power Engineering Conference, SAUPEC 2025

作者： Gwebu, Marcia Olukamni, Peter Mabunda, Nkateko University of Johannesburg Electrical and Electronic Engineeing Technology South Africa

ISBN: (纸本)9798331535162

Researchers have explored methods to maximize energy output from PV systems, with tilt and azimuth optimization being a significant area of focus. While some studies have proposed standard guidelines for tilt and azimuth based on regional differences, this project utilizes intelligent optimization algorithms to achieve optimal setting of these variables based on known mathematical model, to maximize energy production. Specifically, the study explores two nature-inspired algorithms, the Genetic Algorithm (GA) and Simulated Annealing (SA). Real-world data was obtained from South African University Radiometric Network (SAURAN) and residential PV system from Pretoria, Gauteng Province for the four seasons of the years 2023 to 2024. Performance was measured using metrics such as prediction accuracy, Standard Deviation (SD), percentage difference, Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). Although both algorithms obtain tilt and azimuth settings that yield better energy production than actual production obtained from the recommended settings used in practice, the optimal settings produced by SA are more realistic (13-40°) compared to GA (30-80°). In terms of standard deviation, both algorithms exhibit high precision (low variability) across all seasons. GA exhibited a lower MAPE, indicating performance that is closer to what is already obtained in the system being studied. MAE values for both algorithms were relatively similar. Finally, sensitivity heat maps demonstrated that irradiance is more stable to variations in tilt and azimuth with SA compared to GA. Overall, this study provides valuable insights for designers and system owners, enabling them to determine the optimal tilt and azimuth angles for effective solar tracking, thereby maximizing energy output © 2025 IEEE.

关键词： optimization algorithms

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A Novel Green Communication

A Novel Green Communication

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2024 IEEE International Conference on Innovation and Novelty in Engineering and Technology, INNOVA 2024

作者： Sajjanar, Vijaykumar Dheshmuk, Mallikarjun Melligeri, T.Y. Patil, P.S. Math, RajinderKumar M. Yaragal, A.S. Dept. of ECE B.L.D.E.A’s Vachana Pitamaha Dr. P.G. Halakatti College of Engineering & Technology Vijayapura India Affiliated to Visvesvaraya Technological University Belagavi India

ISBN: (纸本)9798331505134

In this present scenario, energy efficiency has become increasingly important matter for wireless networks. In sequence to encounter the desires of additional capacity, enhanced information quality, and better quality of service for next-era networks, there is a need to adopt low power consumption structures. Therefore, green communication is essential. This work breaks the space between the wireless networks area and the unique Cyber IoT status. The work is not only aimed at providing effective hybrid optimization algorithms for green IoT system estimating;Hy-IoT, however, besides gives the actual structure of the IoT system to test the proposed protocol compared to conventional agreements. In integrated wireless sensor networks (WSNs), cluster heads are important nodes, they required to carry out multiple duties, so they use up a lot of power. Effective cluster-head choosing improves the power consumption of the nodes and thereby enlarges the network life time and packet transfer value to the sink. Hy-IoT utilizes various weighted election possibilities to select Cluster-head related on regional measure of heterogeneity. Simulation gives that Hy-IoT extends the life of the network and enlarge performance analyzed to Low-energy adaptive clustering hierarchy (LEACH). © 2024 IEEE.

关键词： optimization algorithms

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Setwise Coordinate Descent for Dual Asynchronous Decentralized optimization

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IEEE Transactions on Automatic Control 2025年

作者： Costantini, Marina Liakopoulos, Nikolaos Mertikopoulos, Panayotis Spyropoulos, Thrasyvoulos EURECOM Sophia Antipolis Nice06000 France Amazon Luxembourg City1255 Luxembourg Univ. Grenoble Alpes CNRS Inria Grenoble INP LIG Grenoble38000 France The Technical University of Crete EURECOM Chania73100 Greece

In decentralized optimization over networks, synchronizing the updates of all nodes incurs significant communication overhead. For this reason, much of the recent literature has focused on the analysis and design of asynchronous optimization algorithms where nodes can activate anytime and contact a single neighbor to complete an iteration together. However, most works assume that the neighbor selection is done at random based on a fixed probability distribution (e.g., uniform), a choice that ignores the optimization landscape at the moment of activation. Instead, in this work we introduce an optimization-aware selection rule that chooses the neighbor providing the highest dual cost improvement (a quantity related to a dualization of the problem based on consensus). This scheme is related to the coordinate descent (CD) method with the Gauss-Southwell (GS) rule for coordinate updates;in our setting however, only a subset of coordinates is accessible at each iteration (because each node can communicate only with its neighbors), so the existing literature on GS methods does not apply. To overcome this difficulty, we develop a new analytical framework for smooth and strongly convex functions that covers our new class of setwise CD algorithms -a class that applies to both decentralized and parallel distributed computing scenarios- and we show that the proposed setwise GS rule can speed up the convergence in terms of iterations by a factor equal to the size of the largest coordinate set. We analyze extensions of these algorithms that exploit the knowledge of the smoothness constants when available and otherwise propose an algorithm to estimate these constants. Finally, we validate our theoretical results through extensive simulations. © 1963-2012 IEEE.

关键词： optimization algorithms

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A Unifying Complexity Certification Framework for Active-Set Methods for Convex Quadratic Programming

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2022年第6期67卷 2758-2770页

作者： Arnstrom, Daniel Axehill, Daniel Linkoping Univ Div Automat Control S-58183 Linkoping Sweden

In model-predictive control (MPC), an optimization problem has to be solved at each time step, which in real-time applications makes it important to solve these efficiently and to have good upper bounds on worst-case solution time. Often for linear MPC problems, the optimization problem in question is a quadratic program (QP) that depends on parameters such as system states and reference signals. A popular class of methods for solving such QPs is active-set methods, where a sequence of linear systems of equations is solved. We propose an algorithm for computing which sequence of subproblems an active-set algorithm will solve, for every parameter of interest. These sequences can be used to set worst-case bounds on how many iterations, floating-point operations, and, ultimately, the maximum solution time the active-set algorithm requires to converge. The usefulness of the proposed method is illustrated on a set of QPs originating from MPC problems, by computing the exact worst-case number of iterations primal and dual active-set algorithms require to reach optimality.

关键词： Complexity theory Predictive control optimization Linear systems Real-time systems Quadratic programming Linear programming optimization algorithms predictive control for linear systems quadratic programming

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Improved non-monopoly search based crayfish search algorithm 3

Improved non-monopoly search based crayfish search algorithm

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3rd International Conference on Electronics Technology and Artificial Intelligence, ETAI 2024

作者： Cui, Jifan School of Automation China University of Geosciences Wuhan430074 China

ISBN: (纸本)9781510683143

Crayfish optimization algorithm (COA) is a swarm-based metaheuristic algorithm proposed in July 2023. The fact that the optimal individuals are not processed makes the COA algorithm less capable of exploring in the early optimization phase and converging in the later phase. In this paper, an improved COA algorithm based on non-monopoly search called NOCOA was proposed. The optimal individuals are perturbed using a non-monopoly search strategy, and the strategy is further optimized using Gaussian and Cauchy operators, which enhances the algorithm optimization ability. Finally, the superior performance of NOCOA is verified by the CEC2017 test suite. © 2024 SPIE.

关键词： optimization algorithms

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Research on Firefly Algorithm Enhancement by Diversifying Swarm 8th

Research on Firefly Algorithm Enhancement by Diversifying Sw...

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8th International Conference on Cognitive Computing, ICCC 2024, Held as Part of the Services Conference Federation, SCF 2024

作者： Pan, Xiuqin Ren, Shuangqing School of Information Engineering Minzu University of China Beijing100081 China

ISBN: (纸本)9783031779534

In response to the issues faced by the traditional Firefly Algorithm (FA), particularly its tendency to become trapped in local optima and slow convergence during the global optimization process, especially for high-dimensional optimization problems, an improved version of the algorithm is proposed—Diverse Swarm Firefly Algorithm (DSFA). Enhancements to the algorithm are twofold: firstly, a new adaptive randomization parameter strategy is designed to meet the search requirements at different stages;secondly, a novel random search mechanism is introduced to enhance the diversity and quality of solutions during the iterative process. To validate the effectiveness of DSFA, simulation experiments were conducted on eight widely recognized benchmark test functions. The experimental results demonstrate that, compared to traditional intelligent optimization algorithms and another version of the improved Firefly Algorithm, DSFA exhibits significant advantages in both convergence speed and solution precision. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： optimization algorithms

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Wide Range Reactive Power Compensation for Voltage Unbalance Mitigation in Electrical Power Systems

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2022年第6-7期49卷 715-728页

作者： Ghaeb, Jasim A. Alkayyali, Malek Tutunji, Tarek A. Philadelphia Univ Fac Engn Alternat Energy Technol Dept Amman Jordan Clemson Univ Dept Mech Engn Clemson SC USA Al Hussein Tech Univ Sch Engn Technol Amman Jordan

In this work, a wide range of reactive power compensation is achieved for voltage unbalance mitigation in 500 km electrical power systems. An Optimal Control Technique (OCT) is proposed to encompass the unbalance load changes at a wide range of Voltage Unbalance Factor (VUF), between 3.33% and 12.4601%, and to minimize it to an acceptable value (at average less than 2%). The technique uses a combination of Particle Swarm optimization (PSO) and Artificial Neural Networks (ANN) in three stages. In the first stage, the PSO finds the optimal firing angles of the Thyristor Controlled Reactor (TCR) and the optimal number of bank capacitors for the Thyristor Switched Capacitor (TSC) to restore the voltage balance. In the second stage, the voltage unbalance evaluations obtained by the PSO algorithm are used to train the ANN. In the third stage, the ANN is connected to the system to control and overcome the voltage unbalance problem accurately and quickly. Results are compared with other techniques available in the literature to confirm the superiority of the OCT performance. Furthermore, a laboratory model for the electrical power system is built and the proposed OCT for real voltage unbalance mitigation is validated.

关键词： TCR-TSC compensators power quality electric power system optimization algorithms ANN

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