Developing a Robust Surrogate Model of Chemical Flooding Based on the Artificial Neural Network for Enhanced Oil Recovery Implications

Ahmadi, Mohammad Ali

doi:10.1155/2015/706897

Cited by 49 publications

(27 citation statements)

References 30 publications

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“…Furthermore, over recent decades, novel modeling techniques have been developed which can substantially aid the optimization of process systems. For instance, surrogate models such as Kriging [39][40][41][42][43][44], radial basis functions [45][46][47][48][49][50], artificial neural networks [51][52][53][54][55][56], splines [57,58], among others were shown to accurately represent complex physical systems while aiding optimal search algorithms. No literature exists which explores the application of such techniques to advance the study of CHP dispatch.…”

Section: Optimal Combined Heat and Power Dispatchmentioning

confidence: 99%

A Critical Review of the Modeling and Optimization of Combined Heat and Power Dispatch

Kazda¹,

Li²

2020

Processes

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Combined heat and power (CHP) systems are attracting increasing attention for their ability to improve the economics and sustainability of the electricity system. Determining how to best operate these systems is difficult because they can consist of many generating units whose operation is governed by complex nonlinear physics. Mathematical programming is a useful tool to support the operation of CHP systems, and has been the subject of substantial research attention since the early 1990s. This paper critically reviews the modeling and optimization work that has been done on the CHP economic dispatch problem, and the CHP economic and emission dispatch problem. A summary of the common models used for these problems is provided, along with comments on future modeling work that would beneficial to the field. The majority of optimization approaches studied for CHP system operation are metaheuristic algorithms. A discussion of the limitations and benefits of metaheuristic algorithms is given. Finally, a case study optimizing five classic CHP system test instances demonstrates the advantages of the using deterministic global search algorithms over metaheuristic search algorithms.

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Section: Optimal Combined Heat and Power Dispatchmentioning

confidence: 99%

A Critical Review of the Modeling and Optimization of Combined Heat and Power Dispatch

Kazda¹,

Li²

2020

Processes

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“…Comparing experimental and simulation results, the authors showed that the neural networks had modeled this system properly, and that they could develop a proper model for the oil recovery factor in various conditions. A similar problem of neural network use for predictive modeling of chemical flooding in petroleum reservoirs was considered in the paper (Ahmadi, 2015). The considered problem is important both from the economical (net present value, NPV) and the technical point of view (recovery factor, RF).…”

Section: Evaluation Of Hydrocarbon Reservoir Characteristicsmentioning

confidence: 99%

Neural Networks In Mining Sciences – General Overview And Some Representative Examples

Tadeusiewicz¹

2015

Archives of Mining Sciences

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The many difficult problems that must now be addressed in mining sciences make us search for ever newer and more efficient computer tools that can be used to solve those problems. Among the numerous tools of this type, there are neural networks presented in this article -which, although not yet widely used in mining sciences, are certainly worth consideration. Neural networks are a technique which belongs to so called artificial intelligence, and originates from the attempts to model the structure and functioning of biological nervous systems. Initially constructed and tested exclusively out of scientific curiosity, as computer models of parts of the human brain, neural networks have become a surprisingly effective calculation tool in many areas: in technology, medicine, economics, and even social sciences. Unfortunately, they are relatively rarely used in mining sciences and mining technology. The article is intended to convince the readers that neural networks can be very useful also in mining sciences. It contains information how modern neural networks are built, how they operate and how one can use them. The preliminary discussion presented in this paper can help the reader gain an opinion whether this is a tool with handy properties, useful for him, and what it might come in useful for.Of course, the brief introduction to neural networks contained in this paper will not be enough for the readers who get convinced by the arguments contained here, and want to use neural networks. They will still need a considerable portion of detailed knowledge so that they can begin to independently create and build such networks, and use them in practice. However, an interested reader who decides to try out the capabilities of neural networks will also find here links to references that will allow him to start exploration of neural networks fast, and then work with this handy tool efficiently. This will be easy, because there are currently quite a few ready-made computer programs, easily available, which allow their user to quickly and effortlessly create artificial neural networks, run them, train and use in practice.The key issue is the question how to use these networks in mining sciences. The fact that this is possible and desirable is shown by convincing examples included in the second part of this study. From the very rich literature on the various applications of neural networks, we have selected several works that show how and what neural networks are used in the mining industry, and what has been achieved thanks to their use. The review of applications will continue in the next article, filed already for publication

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“…The application of machine learning strategies has been widely practiced in the oil and gas development. These attempts have covered aspects of enhanced oil recovery [7][8][9][10][11][12][13][14], fracture detection [15], development plan optimization [15,16], dynamic production prediction [18][19][20][21] and asphaltene precipitation prediction [22]. Some studies have also focused on applying machine learning strategies to model permeability impairment due to mineral scale deposition [23][24][25] and predict the success of an inhibition scenario in the field [4].…”

Section: Introductionmentioning

confidence: 99%

A Data Mining Perspective on the Confluent Ions` Effect for Target Functionality

Fazelabdolabadi

Montazeri

Pourafshary

2021

HighTech. Innov. J.

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The production of hydrocarbon resources at an oil field is concomitant with challenges with respect to the formation of scale inside the reservoir rock – intricately impairing its permeability and hindering the flow. Historically, the effect of ions is attributed to the undergone phenomenon; nevertheless, there exists a great deal of ambiguity about its relative significance compared to other factors, or the effectiveness as per the ion type. The present work applies a data mining strategy to unveil the influencing hierarchy of the parameters involved in driving the process within major rock categories – sandstone and carbonate – to regulate a target functionality. The functionalities considered evolve around maximizing the oil recovery, minimizing permeability impairment/ scale damage. A pool of experimental as well as field data was used for this sake, accumulating the bulk of the available literature data. The methods used for data analysis in the present work included the Bayesian Network, Random Forest, Deep Neural Network, as well as Recursive Partitioning. The results indicate a rolling importance for different ion species - altering under each functionality – which is not ranked as the most influential parameter in either case. For the oil recovery target, our results quantify a distinction between the source of ion of a single type, in terms of its influencing rank in the process. This latter deduction is the first proposal of its kind – suggesting a new perspective for research. Moreover, the machine learning methodology was found to be capable of reliably capturing the data – evidenced by the minimal errors in the bootstrapped results. Doi: 10.28991/HIJ-2021-02-03-05 Full Text: PDF

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Developing a Robust Surrogate Model of Chemical Flooding Based on the Artificial Neural Network for Enhanced Oil Recovery Implications

Cited by 49 publications

References 30 publications

A Critical Review of the Modeling and Optimization of Combined Heat and Power Dispatch

A Critical Review of the Modeling and Optimization of Combined Heat and Power Dispatch

Neural Networks In Mining Sciences – General Overview And Some Representative Examples

A Data Mining Perspective on the Confluent Ions` Effect for Target Functionality

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