Applications of type-2 fuzzy logic system: handling the uncertainty associated with candidate-well selection for hydraulic fracturing

Zoveidavianpoor, Mansoor; Gharibi, Abdoullatif

doi:10.1007/s00521-015-1977-x

Cited by 13 publications

(5 citation statements)

References 72 publications

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“…Some researchers (cf. Liu [8], Chen and Chang [9], Malin and Castillo [10], Yang et al [11,12], Liu et al [13], Tavoosi et al [14], Zoveidavianpoor et al [15], Tavoosi and Badamchizadeh [16], etc.) have developed type reduction strategies for continuous generalized T2FS.…”

Section: Introductionmentioning

confidence: 93%

Mean and CV reduction methods on Gaussian type-2 fuzzy set and its application to a multilevel profit transportation problem in a two-stage supply chain network

Jana

Pramanik

Maiti

2016

Neural Comput & Applic

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The transportation problem (TP) is an important supply chain optimization problem in the traffic engineering. This paper maximizes the total profit over a three-tiered distribution system consisting of plants, distribution centers (DCs) and customers. Plants produce multiple products that are shipped to DCs. If a DC is used, then a fixed cost (FC) is charged. The customers are supplied by a single DC. To characterize the uncertainty in the practical decision environment, this paper considers the unit cost of TP, FC, the supply capacities and demands as Gaussian type-2 fuzzy variables. To give a modeling framework for optimization problems with multifold uncertainty, different reduction methods were proposed to transform a Gaussian type-2 fuzzy variable into a type-1 fuzzy variable by mean reduction method and CV reduction method. Then, the TP was reformulated as a chanceconstrained programming model enlightened by the credibility optimization methods. The deterministic models are then solved using two different soft computing techniques-generalized reduced gradient and modified particle swarm optimization, where the position of each particle is adjusted according to its own experience and that of its neighbors. The numerical experiments illustrated the application and effectiveness of the proposed approaches.

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Section: Introductionmentioning

confidence: 93%

Mean and CV reduction methods on Gaussian type-2 fuzzy set and its application to a multilevel profit transportation problem in a two-stage supply chain network

Jana

Pramanik

Maiti

2016

Neural Comput & Applic

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“…Consequently, numerous scholars have undertaken main controlling factor screening studies in the petroleum engineering domain [17,18]. Zoveidavianpoor et al [19] employed Gaussian distribution membership functions, considering seven geological parameters such as permeability and skin coefficient as input variables. They determined weights based on expert knowledge and conducted fuzzy comprehensive evaluations of candidate fracturing wells.…”

Section: Corresponding Loss Function Y Leftmentioning

confidence: 99%

Production Capacity Prediction Method of Shale Oil Based on Machine Learning Combination Model

Qian,

Lu,

Zhong

et al. 2024

ENERGY

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The production capacity of shale oil reservoirs after hydraulic fracturing is influenced by a complex interplay involving geological characteristics, engineering quality, and well conditions. These relationships, nonlinear in nature, pose challenges for accurate description through physical models. While field data provides insights into real-world effects, its limited volume and quality restrict its utility. Complementing this, numerical simulation models offer effective support. To harness the strengths of both data-driven and model-driven approaches, this study established a shale oil production capacity prediction model based on a machine learning combination model. Leveraging fracturing development data from 236 wells in the field, a data-driven method employing the random forest algorithm is implemented to identify the main controlling factors for different types of shale oil reservoirs. Through the combination model integrating support vector machine (SVM) algorithm and back propagation neural network (BPNN), a model-driven shale oil production capacity prediction model is developed, capable of swiftly responding to shale oil development performance under varying geological, fluid, and well conditions. The results of numerical experiments show that the proposed method demonstrates a notable enhancement in R 2 by 22.5% and 5.8% compared to singular machine learning models like SVM and BPNN, showcasing its superior precision in predicting shale oil production capacity across diverse datasets.

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“…This is still an area of active research. Furthermore, there is a room for proppant and completion selection criteria research to step in and utilize data analytics to improve prediction [101,102].…”

Section: Challenges Of Hydraulic Fracturingmentioning

confidence: 99%

Advancement in Hydraulic Fracturing for Improved Oil Recovery

Merzoug,

Ouadi,

Tomomewo

2023

Innovations in Enhanced and Improved Oil Recovery - New Advances

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This chapter provides a comprehensive overview of advancements in hydraulic fracturing in unconventional plays. The narrative starts with an introduction to hydraulic fracturing and its transformative potential in the U.S., showcasing innovations in fracturing volumes, proppant masses, and well laterals. A detailed examination of fracturing fluids follows, emphasizing the dominance of slickwater treatments in unconventional plays. The chapter then delves into the crucial role of proppants, highlighting their surge in usage over a decade and the consequential shifts in material choice. The intricacies of perforation design are explored, particularly the revolutionary Xtreme Limited Entry approach and its subsequent impacts on production efficiency. In the realm of diagnostic technologies, the chapter presents a range, from traditional methods to emerging ones like Microseismic Depletion Delineation and time-lapse geochemical fingerprinting. The topic of refracturing is also addressed, spotlighting its merits in combating rapid production declines and the associated challenges. Finally, the chapter elucidates the phenomenon of fracture-driven interaction, offering insights into its historical context, influential factors, and proposed strategies to manage its repercussions. Through its breadth and depth, this chapter underscores the multifaceted nature of hydraulic fracturing advancements and their significance in the oil industry.

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Applications of type-2 fuzzy logic system: handling the uncertainty associated with candidate-well selection for hydraulic fracturing

Cited by 13 publications

References 72 publications

Mean and CV reduction methods on Gaussian type-2 fuzzy set and its application to a multilevel profit transportation problem in a two-stage supply chain network

Mean and CV reduction methods on Gaussian type-2 fuzzy set and its application to a multilevel profit transportation problem in a two-stage supply chain network

Production Capacity Prediction Method of Shale Oil Based on Machine Learning Combination Model

Advancement in Hydraulic Fracturing for Improved Oil Recovery

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