检索结果-内蒙古大学图书馆

production optimization of heavy oil recovery utilizing Mo-Ni based liquid catalysts: A simulation approach

Petroleum Research 2025年第1期10卷 57-65页

作者： Ali Alarbah Arifur Rahman Ezeddin Shirif Na(Jenna)Jia Petroleum Systems Engineering Faculty of Engineering and Applied ScienceUniversity of ReginaReginaSKS4S 0A2Canada

In recent years,the demand for heavy oil has increased due to its abundant availability and low ***,the extraction of heavy oil poses a significant challenge due to its high viscosity and low ***,various methods have been developed to enhance the recovery of heavy oil,including the use of *** study has created a unique simulation approach that uses liquid catalysts(LCs)to improve heavy oil *** this work,laboratory testing dataset and numerical simulation studies were used to examine the potential of applying LCs as an alternative chemical agent for enhancing heavy oil ***-STARS and CMOST modules were used to historical match the laboratory scale results of two sand-pack flooding experiments(water flooding and liquid catalyst flooding in tertiary recovery mode).Moreover,a sensitivity study was conducted to apply a wide range of assumptions to determine the most effective process controlling ***,oil production optimization is performed using a genetic algorithm(particle swarm optimization)by selecting the optimum-operating *** comparison to typical water flooding,the results revealed a discernible rise in the heavy oil recovery factor(RF)when injecting *** simulation results showed that the optimized production strategy could increase the ultimate oil recovery by up to 45.06%.The injection rate,slug size,and injection temperature were found to be significant factors in optimizing the production of heavy *** simulation approach can be used to optimize the production of heavy oil using acidic Mo-Ni based liquid catalyst in different reservoirs.

关键词： Liquid catalyst Sensitivity analysis History matching production optimization Reservoir simulation etc

来源：评论

学校读者我要写书评

暂无评论

production optimization in the time of pandemic: an SIS-based optimal control model with protection effort and cost minimization

引用

ANNALS OF OPERATIONS RESEARCH 2025年第1期344卷 79-102页

作者： Brusset, Xavier Jebali, Aida La Torre, Davide Liuzzi, Danilo Univ Cote Azur SKEMA Business Sch Sophia Antipolis France Univ Cote Azur SKEMA Business Sch Paris France Univ Milan Milan Italy

The COVID-19 pandemic wreaks havoc in supply chains by reducing the production capacity of some essential suppliers, closure of production facilities or the absence of infected workers. In this paper, we present three decision support models for a plant manager to help in deciding on (a) the level of protection of the workforce against the spread of the virus in the absence of regional protection measures, (b) on the duration of the protection, and (c) the level of protection of the workforce with regional protection measures enforced by health authorities. These decision models are based on a SIS epidemiological model which takes into account the possibility that a worker can infect others but also that even when recovered can be infected again. The first and third models prescribe how, in time, the protection effort in terms of prophylactic measures must be deployed. The second model extends the first one as it also determines the length the protection effort must be deployed. The proposed models have been applied to the case of a meat processing plant that must satisfy the demand of a large-scale retailer. Clearly, to achieve production targets and satisfy customers' demand, plants in this labor-intensive industry rely on the number of healthy workers and the service level of suppliers. Our results indicate that these models provide managers with the tools to understand and measure the impact of an infection on production and the corresponding cost. Along the way, this work illustrates the ripple effect as suppliers affected by the pandemic are unable to fulfill the processing plant requirements and so the retailer's orders. Our findings provide normative guidance for supply chain decision support systems under risk of pandemic induced disruptions using a quantitative model-based approach.

关键词： COVID-19 Decision support Supply chain production optimization

来源：评论

学校读者我要写书评

暂无评论

Deep reinforcement learning for optimal hydraulic fracturing design in real-time production optimization

GEOENERGY SCIENCE AND ENGINEERING

引用

GEOENERGY SCIENCE AND ENGINEERING 2025年 250卷

作者： Yan, Bingyang Zhong, Zhi China Univ Geosci Sch Earth Resources Wuhan Hubei Peoples R China China Univ Geosci Sch Earth Resources Key Lab Theory & Technol Petr Explorat & Dev Hubei Wuhan Hubei Peoples R China

Hydraulic fracturing is a common method for improving oil recovery and plays an essential role in optimizing production and managing reservoirs by creating highly permeable channels in subterranean formations. The conventional optimization process, reliant on numerous iterations of numerical simulations, is notably time- intensive and computationally expensive. We introduce an innovative production optimization method that progressively maximizes net present value (NPV) through sequential decision-making. Specifically, the method dynamically adjusts fracture position, length, width, and height in response to newly acquired data on reservoir properties such as porosity and permeability. We formulate the field production optimization challenge as a Markov decision process, examining the hydraulic fracturing pattern as a sequence of decisions. Proximal policy optimization (PPO), a highly stable deep reinforcement learning (DRL) method, is implemented with a deep convolutional neural network (CNN) as the policy function. This network correlates the current state of the reservoir with the optimal actions for the subsequent step. In our DRL approach to formulating the optimal hydraulic fracture design, we define states by the comprehensive reservoir saturation and pressure fields, while actions are the location and characteristics of new induced fractures. Since the trained policy is an explicit function structure, the DRL agent can adjust the fracture locations and properties in real-time under different reservoir states. Comparative tests against genetic algorithm-based optimizations highlight the efficacy of the proposed method. In addition, the durability of the trained policy is confirmed through its application to an untested scenario, the synthetic 3D shale reservoir, where the average NPV attained by our DRL-based approach is 180% of that achieved with the genetic algorithm. This underscores the promise of incorporating DRL into real-time reservoir management and

关键词： Reinforcement learning Proximal policy optimization Hydraulic fracturing production optimization

来源：评论

学校读者我要写书评

暂无评论

Research on integrated injection-production optimization technology of electric submersible pump for Bohai oilfield heavy oil thermal recovery

引用

JOURNAL OF PETROLEUM EXPLORATION AND production TECHNOLOGY 2025年第2期15卷 1-17页

作者： Li, Menglong Yu, Jifei Hao, Tongchun Cao, Yanfeng Yang, Yang Yan, Xinjiang Liu, Zhilong Liu, Fengxun Li, Lingxi Dong, Shichao Natl Key Lab Offshore Oil & Gas Exploitat Beijing Peoples R China CNOOC Res Inst Co Ltd Drilling & Prod Technol Res Dept Beijing Peoples R China Cnooc Energy Dev Co LTD Engn Technol Branch Tianjin Peoples R China

The geological reserves of ordinary heavy oil suitable for thermal injection development in Bohai Oilfield are about 450 million tons. The traditional thermal recovery process uses a two trip injection-production string development method, which frequently starts and stops operations, resulting in additional cold damage and high costs. The efficiency of the jet pump injection-production integrated string system is also low, and production control is difficult. Therefore, based on the results of an integrated field test of electric submersible pump injection and production, this article optimizes its key technologies, removes high-temperature downhole on-off valves, re develops high-temperature deep well exhaust valves, and optimizes the structure of the packer as the installation platform for deep well exhaust valves. Indoor and field tests were conducted, and the results showed that the improved integrated technology of electric submersible pump injection and production met the requirements of 250 degrees C - room temperature and 21 MPa dynamic sealing. It can achieve multiple rounds of non repair injection and production conversion under steam throughput development mode, while reducing platform space requirements and injection heat loss, improving system efficiency and economy. The integrated optimization technology of electric submersible pump injection and production has good stability and applicability, and can be further promoted and applied.

关键词： Heavy oil thermal recovery Steam stimulation Electric submersible pump Integrated injection-production production optimization

来源：评论

学校读者我要写书评

暂无评论

Integrated wellbore-surface pressure control production optimization for shale gas wells

引用

Natural Gas Industry B 2025年第2期12卷 123-134页

作者： Zhou, Xingyu Zhang, Liming Qi, Ji Wang, Yanxing Zhang, Kai Zhang, Ruijia Sun, Yaqi School of Petroleum Engineering China University of Petroleum (East China) Qingdao 266580 China Exploration and Development Research Institute Shengli Oilfield Company SINOPEC Dongying 257015 China Oilfield Development Department Changqing Oilfield Company CNPC Xi'an 710016 China School of Information and Control Engineering Qingdao University of Technology Qingdao 266520 China

Shale gas wells often face challenges in maintaining continuous and stable production due to their coexistence with high- and low-pressure wells within the same development block, which leads to issues involving mixed-pressure flows. Traditional pipeline optimization methods used in conventional gas well blocks fail to address the unique needs of shale gas wells, such as the precise planning of airflow paths, pressure distribution, and compression. This study proposes a pressure-controlled production optimization strategy specifically designed for shale gas wells operating under mixed-pressure flow conditions. The strategy aims to improve production stability and optimize system efficiency. The decline in production and pressure for individual wells over time is forecasted using a predictive model that accounts for key factors of system optimization, such as reservoir depletion, wellbore conditions, and equipment performance. Additionally, the model predicts the timing and impact of liquid loading, which can significantly affect production. The optimization process involves analyzing the existing gathering pipeline network to determine the most efficient flow directions and compression strategies based on these predictions, while the strategy involves adjusting compressor settings, optimizing flow rates, and planning pressure distribution across the network to maximize productivity while maintaining system stability. By implementing these strategies, this study significantly improves gas well productivity and enhances the adaptability and efficiency of the gathering and transportation system. The proposed approach provides systematic technical solutions and practical guidance for the efficient development and stable production of shale gas fields, ensuring more robust and sustainable pipeline operations. © 2025 Sichuan Petroleum Administration

关键词： Integration Pipeline optimization production optimization Productivity prediction Shale gas

来源：评论

学校读者我要写书评

暂无评论

production optimization of a thermostable alkaline and detergent biocompatible protease by Bacillus paramycoides WSA for the green detergent industry

引用

SCIENTIFIC REPORTS 2025年第1期15卷 1-17页

作者： Alshehri, Wafa A. Alhothifi, Sarah A. Khalel, Ashjan F. Alqahtani, Fatimah S. Hadrich, Bilel Sayari, Adel Univ Jeddah Coll Sci Dept Biol Sci Jeddah 23890 Saudi Arabia Jazan Univ Univ Coll Aldarb Dept Biol Jazan Saudi Arabia Univ Bisha Fac Sci Dept Biol Bisha 61922 Saudi Arabia Imam Mohammad Ibn Saud Islamic Univ IMSIU Coll Engn Dept Chem Engn Riyadh 11432 Saudi Arabia

Industrial applications are evolving to replace chemical catalysts with eco-friendly solutions. This work enhances extracellular protease production from a newly isolated proteolytic Bacillus paramycoides WSA using Response Surface Methodology (RSM). The used quadratic model demonstrated a good fit with R-2 = 80.59%, R-Adj(2) = 57.94%, a high degree of significance (p = 0.017 < 0.05), and a low RMSE = 0.030 U/mL. Maximum protease activity of 1.346 +/- 0.060 U/mL was obtained after 8 h-cultivation at 45 degrees C and pH 7. The protease activity is maximal at 45 degrees C and pH 10. It maintains 75% of its activity after 10 min of incubation at 80 degrees C. Furthermore, the enzyme was found to be stable in an alkaline environment, retaining 89% of its activity after 1 h incubation at pH 12. Several metal ions, surfactants, and inhibitors increased protease performance. Indeed, 110% and 140% protease activity was measured in the presence of 5 mM CoCl2 and CaCl2, respectively. Similarly, 105%, 128%, 135%, 144%, and 160% protease activity were measured in the presence of 0.5% of Tween 20, EDTA, Sodium azide, uric acid, and SDS, respectively. Interestingly, the enzyme activity was enhanced (138%) after 1 h of incubation with 1% Reem detergent. Furthermore, this protease (10 U/mL), in combination with 0.7% detergent, could clean bloodstained fabric after 10 min incubation at 45 degrees C. Our results suggest that this thermostable, alkaline, and detergent-biocompatible protease could be a promising additive for eco-friendly use in detergent industries.

关键词： Alkaline and thermostable Bacillus paramycoides protease Response surface methodology production optimization Bloodstain enzymatic cleaning

来源：评论

学校读者我要写书评

暂无评论

production optimization with operating constraints for a fed-batch reactor with DMC predictive control

引用

CHEMICAL ENGINEERING SCIENCE 1999年第13-14期54卷 2745-2751页

作者： Rodrigues, JAD Marciel, R Escola Engn Maua Inst Maua Tecnol Dept Engn Quim & Alimentos BR-09580900 Sao Caetano Sul SP Brazil Univ Estadual Campinas Fac Engn Quim Dept Proc Quim BR-13081970 Campinas SP Brazil

This work presents a production optimization approach with operating constraints applied to a fed-batch penicillin process with a DMC predictive controller. A deterministic and non-structured mathematical model of the fed-batch penicillin production process solved by a fourth order variable step Runge-Kutta-Gill algorithm type simulated the system. The production process optimization has been performed through the manipulation of the initial cell and the substrate fed-batch concentrations besides the fed-batch flow rate strategy. In order to carry out this procedure it was necessary to compute a production index that considers product amount and process operating constraints such as cell, substrate and dissolved oxygen concentrations besides the bioreactor final volume through the penalty functions concept. The optimization algorithm was based on the modified simplex method. The DMC control algorithm was implemented in order to stabilize the dissolved oxygen concentration through the agitation speed manipulation. This controller was found to be appropriate to work coupled with a optimization procedure due to the fact that it gives a smooth behavior to this critical constraint even with all disturbs during the convergence. (C) 1999 Elsevier Science Ltd. All rights reserved.

关键词： production optimization DMC predictive control fed-batch reactor operating constraints dissolved oxygen concentration penicillin

来源：评论

学校读者我要写书评

暂无评论

production optimization and energy saving of complex chemical processes using novel competing evolutionary membrane algorithm: Emphasis on ethylene cracking

引用

ENERGY CONVERSION AND MANAGEMENT 2019年 196卷 311-319页

作者： Cui, Yunfei Han, Yongming Geng, Zhiqiang Zhu, Qunxiong Fan, Jinzhen Beijing Univ Chem Technol Coll Informat Sci & Technol Beijing Peoples R China Minist Educ China Engn Res Ctr Intelligent PSE Beijing 100029 Peoples R China Genentech Inc Prod Dev Dept Stat Programming & Anal San Francisco CA 94080 USA

As a relatively high energy-consuming in China, the chemical process is important and has potential to be optimized for energy saving, economic benefits and environmental protection. Multiple goals can be coordinated using multi-objective optimization algorithms. In order to improve the exploration and exploitation abilities in optimizing multiple objectives, a constrained competing evolutionary membrane algorithm is proposed. The proposed algorithm takes advantages of the distributed and parallel computing mode of the membrane computing. Populations are evolved independently in each membrane and share information between membranes based on competing communication rules. Meanwhile, the skin membrane archives global elitist solutions and serves as guidance for inner evolution processes. Finally, the optimization experiments on the ethylene cracking process, as an important production of complex chemical processes, prove that the proposed algorithm can provide enough selection for decision makers with well-distributed and converged candidate solutions. Furthermore, the solutions lead the ethylene cracking process to reach the coordinated optimum ethylene or propylene production, oil consumption reduction and carbon dioxide emission reduction. In average, the optimization solutions bring about reduction of 2697.58 tons of feed oil and 8281.57 tons of carbon dioxide emission.

关键词： Multi-objective optimization Evolutionary membrane algorithm production optimization Energy saving Emission reduction Complex chemical processes

来源：评论

学校读者我要写书评

暂无评论

production optimization under waterflooding with long short-term memory and metaheuristic algorithm

引用

Petroleum 2023年第1期9卷 53-60页

作者： Cuthbert Shang Wui Ng Ashkan Jahanbani Ghahfarokhi Menad Nait Amar Department of Geoscience and Petroleum Norwegian University of Science and TechnologyTrondheimNorway Département Etudes Thermodynamiques Division LaboratoiresSonatrachBoumerdesAlgeria

In petroleum domain,optimizing hydrocarbon production is essential because it does not only ensure the economic prospects of the petroleum companies,but also fulfills the increasing global demand of ***,applying numerical reservoir simulation(NRS)to optimize production can induce high computational *** models are suggested to alleviate this challenge because they are computationally less demanding and able to yield reasonably accurate *** this paper,we demonstrated how a machine learning technique,namely long short-term memory(LSTM),was applied to develop proxies of a 3D reservoir *** techniques were employed to create numerous simulation cases which served as the training database to establish the *** blind validating the trained proxies,we coupled these proxies with particle swarm optimization to conduct production *** training and blind validation results illustrated that the proxies had been excellently developed with coefficient of determination,R2 of *** also compared the optimization results produced by NRS and the *** comparison recorded a good level of accuracy that was within 3%*** proxies were also computationally 3 times faster than ***,the proxies have served their practical purposes in this study.

关键词： production optimization Numerical reservoir simulation Machine learning Long short-term memory(LSTM) Dynamic proxies Particle swarm optimization(PSO)

来源：评论

学校读者我要写书评

暂无评论

production optimization of extracellular amidase enzyme by newly isolated Pseudomonas putida AP-2 from agricultural soil

引用

RENDICONTI LINCEI-SCIENZE FISICHE E NATURALI 2014年第4期25卷 523-530页

作者： Abada, Emad A. Helwan Univ Fac Sci Dept Bot & Microbiol Cairo 11790 Egypt Jazan Univ Fac Sci Dept Biol Jazan 114 Saudi Arabia

Amidases are ubiquitous enzymes that have received increased attention due to their wide range of biotechnological applications, especially in industries for the synthesis of wide variety of carboxylic and hydroxamic acids, which find applications in pharmaceuticals, agrochemicals and waste water treatments. In the present study, 40 bacterial isolates were screened for extracellular amidase-producing capability, and on the basis of color development, 5 isolates were selected for amidase production in broth media. Based on enzyme production, one of the most potent isolates identified as Pseudomonas putida AP-2 was selected for further study. The effects of media composition and various fermentation conditions for optimization of amidase production were studied. The maximum extracellular amidase production was obtained at 30 A degrees C and pH 8.0 after 36 h of incubation in shaking condition. Among the substrate, acetamide was the best;however, P. putida AP-2 also utilized acrylamide which is a known carcinogen. Regarding carbon sources, glucose was the best, while peptone was found the best nitrogen source. The isolated bacterium, P. putida AP-2, is also tolerant to number of heavy metals at higher levels so this may also be applied for field application in contaminated soil.

关键词： Amidase Pseudomonas putida Heavy metal tolerance Bioremediation production optimization

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：