Hybrid Intelligent Modelling of the Viscoelastic Moduli of Coal Fly Ash Based Polymer Gel System for Water Shutoff Treatment in Oil and Gas Wells

Adewunmi, Ahmad A.; Ismail, Suzylawati; Owolabi, Taoreed O.; Sultan, Abdullah S.; Olatunji, Sunday O.; Ahmad, Zulkifli

doi:10.1002/cjce.23436

Cited by 26 publications

(10 citation statements)

References 51 publications

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“…Therefore, the research on automation and intelligence of oil and gas exploitation technology is of great guarantee significance and economic value for national development and industrial progress. The continuous exploitation of oil fields for many years makes the properties of underground crude oil change, such as viscosity increase, exhaustion, and even the oilfield become unrecoverable dead oil due to lack of displacement energy [5][6][7][8][9]. In industry, water injection technology is often used to stabilize the pressure difference between oil zones and improve oil recovery efficiency.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Oil Production Stratified Water Injection Technology

Cheng

Dong

et al. 2022

Wireless Communications and Mobile Computing

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As the driving energy to deal with the decrease of interlayer pressure caused by continuous oil production, the layered water injection technology has the characteristics of inhibiting the decrease of oil production and slowing down the increase of oil/gas ratio. In engineering, water injection technology is often used to improve the properties of crude oil, such as excessive viscosity, weak liquidity, and depleted storage, to avoid the formation of dead oil. Injecting appropriate amount of water into different production horizons can effectively maintain the formation water injection pressure, improve the sustainable development speed of the oilfield, ensure the oil production and effectively control the production cost. It is of great value to petroleum engineering and has been widely concerned by the industrial and academic circles at home and abroad. With the continuous development of oilfields over the years, most oilfields have become high-water-cut oilfields. Through the existing layered water injection technology, there are defects such as high labor cost, low operating efficiency, and long commissioning cycle. The ratio of water injection cost to constant increase gradually decreases, and the technical shortcomings become more and more obvious, which is difficult to meet production needs. It is urgent to study and optimize water injection technology scheme to meet oilfield production and technology iteration. In recent years, electronic technology, communication technology, automatic control technology, and other advanced production technology applied to geological exploration, logging technology fields such as engineering, oilfield development is towards integration and intelligent direction, which makes the advanced control and real-time communication intelligent power precision, and the layered water injection technology is possible. This paper summarizes the development history and status quo of oil recovery stratified water injection technology at home and abroad and points out that there are technical bottlenecks and development limitations in the development of water injection technology at present. Focusing on the current hot spots of intelligent oil recovery stratified water injection technology, the advantages and disadvantages of various intelligent water injection technology are compared and analyzed. It provides a certain theoretical reference value for the theoretical research and engineering application of intelligent stratified water injection technology to the equipment design and production of oilfield production and oil recovery technology research institutes and technology and equipment manufacturers.

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Section: Introductionmentioning

confidence: 99%

Intelligent Oil Production Stratified Water Injection Technology

Cheng

Dong

et al. 2022

Wireless Communications and Mobile Computing

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“…Support vector regression can connect the energy gap of doped polyvinyl alcohol with the corresponding refractive indices through data transformation from a two-dimensional structure to a higher-level structure of n dimensions [ 25 , 26 ]. Consider a dataset of

samples of polyvinyl alcohol composite consisting of input energy gap

and measured refractive indices

such that

.…”

Section: Mathematical Descriptions Of the Algorithmsmentioning

confidence: 99%

Modeling the Optical Properties of a Polyvinyl Alcohol-Based Composite Using a Particle Swarm Optimized Support Vector Regression Algorithm

Owolabi

Rahman

2021

Polymers

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We developed particle swarm optimization-based support vector regression (PSVR) and ordinary linear regression (OLR) models for estimating the refractive index (n) and energy gap (E) of a polyvinyl alcohol composite. The n-PSVR model, which can estimate the refractive index of a polyvinyl alcohol composite using the energy gap as a descriptor, performed better than the n-OLR model in terms of root mean square error (RMSE) and mean absolute error (MAE) metrics. The E-PSVR model, which can predict the energy gap of a polyvinyl alcohol composite using its refractive index descriptor, outperformed the E-OLR model, which uses similar descriptor based on several performance measuring metrics. The n-PSVR and E-PSVR models were used to investigate the influences of sodium-based dysprosium oxide and benzoxazinone derivatives on the energy gaps of a polyvinyl alcohol polymer composite. The results agreed well with the measured values. The models had low mean absolute percentage errors after validation with external data. The precision demonstrated by these predictive models will enhance the tailoring of the optical properties of polyvinyl alcohol composites for the desired applications. Costs and experimental difficulties will be reduced.

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“…Slack parameters (ξ j , ξ * j ) are incorporated into the convex optimization problem to limit the effect of some constraints that prevent the actualization of the flat model. e optimization expression is simplified as shown in the following equation [28,29]:…”

Section: Background Of the Support Vector Regression Algorithmmentioning

confidence: 99%

Barium Titanate Semiconductor Band Gap Characterization through Gravitationally Optimized Support Vector Regression and Extreme Learning Machine Computational Methods

Olatunji

Owolabi

2021

Mathematical Problems in Engineering

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Barium titanate (BaTiO3) is a class of ceramic multifunctional materials with unique thermal stability, prominent piezoelectricity constant, excellent dielectric constant, environmental friendliness, and excellent photocatalytic activities. These features have rendered barium titanate indispensable in many areas of applications such as electromechanical devices, thermistors, multilayer capacitors, and electrooptical devices. The photocatalytic activity of barium titanate semiconductor is hindered by its large band gap and high rate of charge recombination. Doping of the parent barium titanate compound for band gap tuning is challenging and consumes appreciable time and other valuable resources. This present work relates the influence of foreign material incorporation into the parent barium titanate with the corresponding energy band gap by developing extreme learning machine- (ELM-) based models and hybridization of support vector regression (SVR) with gravitational search algorithm (GSA) using the structural lattice distortion that emanated from doping as model descriptors. The developed gravitationally optimized SVR (GSVR) is characterized with a low value of mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) of 0.036 ev, 1.145 ev, and 0.122 ev, respectively. The developed GSVR model outperforms ELM-Sine and ELM-Sig models using various performance evaluators. The developed GSVR model investigates the significance of iodine and samarium incorporation on the band gap of the parent barium titanate and the attained energy gaps conform excellently to the experimentally reported values. The demonstrated precision of the developed GSVR as measured from the closeness of its estimates with the measured values provides a quick and accurate method of energy gap characterization with circumvention of experimental stress and conservation of valuable time as well as other resources.

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Hybrid Intelligent Modelling of the Viscoelastic Moduli of Coal Fly Ash Based Polymer Gel System for Water Shutoff Treatment in Oil and Gas Wells

Cited by 26 publications

References 51 publications

Intelligent Oil Production Stratified Water Injection Technology

Intelligent Oil Production Stratified Water Injection Technology

Modeling the Optical Properties of a Polyvinyl Alcohol-Based Composite Using a Particle Swarm Optimized Support Vector Regression Algorithm

Barium Titanate Semiconductor Band Gap Characterization through Gravitationally Optimized Support Vector Regression and Extreme Learning Machine Computational Methods

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