Multiphysics Field Coupled to a Numerical Simulation Study on Heavy Oil Reservoir Development via Electromagnetic Heating in a SAGD-like Process DOI Creative Commons

Jifei Yu,

Wenchao Liu, Yang Yang

et al.

Energies, Journal Year: 2024, Volume and Issue: 17(20), P. 5125 - 5125

Published: Oct. 15, 2024

Conventional thermal recovery methods for heavy oil suffer from significant issues such as high water consumption, excessive greenhouse gas emissions, and substantial heat losses. In contrast, electromagnetic heating, a waterless method recovery, offers numerous advantages, including energy utilization, reduced carbon volumetric heating of the reservoir, making it focus recent research in technologies. This paper presents numerical simulation study using block Bohai Bay oilfield China case study. Firstly, multiphysics field coupled to mathematical model was established, considering impact temperature on viscosity, threshold pressure gradient non-Darcy flow, dielectric properties along with dissipation overlying undercover sandstone gravitational effects fluid flow. Secondly, fields developed, convergence stability were tested. Finally, sensitivity analysis based results identified factors affecting production. It found that significantly enhances production, greatly influences prediction Moreover, severely reduces cumulative When production well is located below antenna, larger spacing higher Higher achieved when antenna positioned at center reservoir studied cases. Power has big effect increasing but its influence diminishes power increases. There exists an optimal range frequencies maximum saturation leads poorer efficiency. provides theoretical foundation technical support technology development plan optimization reservoirs subjected heating.

Language: Английский

A Comprehensive Investigation of Nanocomposite Polymer Flooding at Reservoir Conditions: New Insights into Enhanced Oil Recovery DOI Creative Commons

Khalaf G. Salem,

Adel M. Salem, Mahmoud A. Tantawy

et al.

Journal of Polymers and the Environment, Journal Year: 2024, Volume and Issue: 32(11), P. 5915 - 5935

Published: July 8, 2024

Abstract Recently, the polymer-nanoparticle combination has garnered significant interest in enhanced oil recovery (EOR) due to its promising experimental results. However, previous research was mostly directed at silica, while alumina and zirconia nanoparticles have gotten least consideration. Unlike works, this study aims investigate influence of three NPs: Silica (SiO 2 ), Alumina (Al O 3 Zirconia (ZrO ) on hydrolyzed polyacrylamide (HPAM). To end, nanocomposites were formulated: HPAM-SiO , HPAM-Al HPAM-ZrO . Rheological evaluations performed examine viscosity degradation HPAM under reservoir conditions. Furthermore, interfacial tension (IFT) oil–water interface wettability studies investigated. Moreover, sand-pack flooding incremental recovery. The results revealed that polymer boosted by 110%, 45%, 12% for respectively investigation range temperature. improved 73%, 48%, salinity. Nanocomposites are also found be a remarkable agent reducing changing contact angle. experiments confirmed EOR HPAM, 8.6%, 17.4%, 15.3%, 13.6% OOIP respectively. well validated matched numerical simulation. Such findings work afford new insights into reinforce outlook such technique field scale.

Language: Английский

Citations

23
Preparation and plugging characteristics investigation of a high temperature induced calcium salt precipitation system for profile control in high temperature reservoirs DOI
Binfei Li, Binfei Li,

Zhuang Shi

et al.

Colloids and Surfaces A Physicochemical and Engineering Aspects, Journal Year: 2025, Volume and Issue: unknown, P. 136413 - 136413

Published: Feb. 1, 2025

Language: Английский

Citations

2
Machine learning prediction of methane, nitrogen, and natural gas mixture viscosities under normal and harsh conditions DOI Creative Commons
Sayed Gomaa, Mohamed A. E. Abdalla,

Khalaf G. Salem

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: July 2, 2024

Abstract The accurate estimation of gas viscosity remains a pivotal concern for petroleum engineers, exerting substantial influence on the modeling efficacy natural operations. Due to their time-consuming and costly nature, experimental measurements are challenging. Data-based machine learning (ML) techniques afford resourceful less exhausting substitution, aiding research industry at that is incredible reach in laboratory. Statistical approaches were used analyze data before applying learning. Seven specifically Linear Regression, random forest (RF), decision trees, gradient boosting, K-nearest neighbors, Nu support vector regression (NuSVR), artificial neural network (ANN) applied prediction methane (CH 4 ), nitrogen (N 2 mixture viscosities. More than 4304 datasets from real utilizing pressure, temperature, density employed developing ML models. Furthermore, three novel correlations have developed CH , N composite using ANN. Results revealed models anticipated predicted methane, nitrogen, viscosities with high precision. designated ANN, RF, Boosting performed better coefficient determination (R ) 0.99 testing sets However, linear NuSVR poorly 0.07 − 0.01 respectively viscosity. Such offer cost-effective fast tool accurately approximating under normal harsh conditions.

Language: Английский

Citations

10
Elevated temperature and pressure performance of water based drilling mud with green synthesized zinc oxide nanoparticles and biodegradable polymer DOI Creative Commons
Milad Khashay, Mohammad Zirak, James J. Sheng

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: April 8, 2025

Language: Английский

Citations

1
Investigating aggregation of heavy oil droplets: Effect of asphaltene anionic carboxylic DOI
Peng Cui, Heng Zhang, Shiling Yuan

et al.

Chemical Physics Letters, Journal Year: 2024, Volume and Issue: 845, P. 141315 - 141315

Published: May 6, 2024

Language: Английский

Citations

4
Theories and applications of phase-change related rock mechanics in oil and gas reservoirs DOI Creative Commons
Yan Jin, Botao Lin, Yanfang Gao

et al.

Petroleum Exploration and Development, Journal Year: 2025, Volume and Issue: 52(1), P. 157 - 169

Published: Feb. 1, 2025

Language: Английский

Citations

0
The Enhanced Oil Recovery Mechanisms in Heavy Oil Reservoirs by Chemical Compound Flooding after Multiple Cycles of Huff-n-Puff DOI
Jiajia Bai,

Tianshuai GU,

Lei Tao

et al.

Energy & Fuels, Journal Year: 2025, Volume and Issue: unknown

Published: March 24, 2025

Language: Английский

Citations

0
Molecular Dynamics Simulation of Nanoparticle‐Driven Oil Detachment in Nanochannels DOI Open Access
Qianli Ma, Minglu Shao, Lipei Fu

et al.

ChemistrySelect, Journal Year: 2025, Volume and Issue: 10(13)

Published: March 28, 2025

Abstract Nanoparticles exhibit significant potential in modulating oil‐water interfacial tension, altering rock wettability, and optimizing fluid flow for enhanced oil recovery. This study employs MD simulations to investigate the effects of surface‐modified nanoparticles (pure‐NP, alkyl‐NP, carboxylate‐NP) on layer thickness, displacement energy. Results reveal that alkyl‐NP reduces tension most effectively (32.57 mN·m⁻¹), followed by carboxylate‐NP (38.64 mN·m⁻¹) pure‐NP (45.02 mN·m⁻¹). Alkyl‐NP also demonstrates greatest reduction oil‐particle interaction energy (−500 kcal/mol), while Pure‐NP Carboxylate‐NP show weaker capacity. Notably, nanoparticle addition significantly increases thickness ( t : 9.5 ∼ 17.4 Å, water 7.9 12.5 total 13.4 22.5 Å) compared pure system = 4.8 3.8 6.5 Å). These findings suggest systems enhance recovery lowering thickening layers, improving crude stripping migration. emerges as promising modifier due its superior interface control reduction.

Language: Английский

Citations

0
Machine learning models for estimating the overall oil recovery of waterflooding operations in heterogenous reservoirs DOI Creative Commons
Sayed Gomaa, Ahmed Ashraf Soliman, Mohamed Mansour

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: April 26, 2025

Abstract Waterflooding is the most widely used improved oil recovery technique. Predicting overall resulting from waterflooding in reservoirs crucial for effective reservoir management and appropriate decision-making. Machine learning (ML) techniques present resourceful fast-track tools, aiding predicting recovery, which time-consuming costly to accomplish by simulation studies. In this paper, four machine models: artificial neural network (ANN), Random Forest (RF), K-Nearest Neighbor (K-NN), Support Vector (SVM) are applied estimate (R) of water flooding. Initially, statistical methods were employed analyze input data before applying techniques. These models take into consideration mobility ratio (M), permeability variation (V), water-oil production (WOR), initial saturation (S Wi ). 1054 datasets utilized develop machine-learning models. ANN-based correlation was developed waterflooding. The ANN proposed model achieves a high coefficient determination (R 2 ) 0.999 low root-mean-square error (RMSE) 0.0063 on validation dataset. On other hand, like RF, K-NN, SVM achieve accurate estimation (R), where coefficients values 0.97, 0.95, 0.80 RMSE scores 0.0282, 0.0405, 0.0629 dataset, respectively. innovative application such ML demonstrates significant improvements prediction accuracy reliability, offering robust solution optimizing processes. provide industry research with efficient economical tools accurately estimating operations within heterogeneous reservoirs.

Language: Английский

Citations

0
Calcium Precipitates as Novel Agents for Controlling Steam Channeling in Steam Injection Processes for Heavy Oil Recovery DOI Open Access
Guolin Shao,

Zhuang Shi,

Yunfei Jia

et al.

Processes, Journal Year: 2025, Volume and Issue: 13(5), P. 1319 - 1319

Published: April 25, 2025

Unconventional heavy oil reservoirs are particularly susceptible to steam breakthrough, which significantly reduces crude production. Profile control is a crucial strategy used for stabilizing production and minimizing costs in these reservoirs. Conventional plugging agent systems the thermal recovery of currently fail meet high-temperature, high-strength, deep profile requirements this process. Precipitation-type calcium salt blocking agents demonstrate long-term stability at 300 °C concentrations up 250,000 mg/L, making them highly effective channeling blockage during injection stages recovery. This study proposes two types precipitation-type agents: CaSO4 CaCO3 crystals. The precipitation behavior was investigated, their dynamic growth patterns were examined. sulfate exhibits slower crystal rate, allowing single-solution injection, while carbonate precipitates rapidly, requiring dual-solution injection. Both incorporate scale inhibitors delay crystals, aids control. Through microscopic visualization experiments, micro-blocking characteristics within pores compared, elucidating positions precipitated salts under porous conditions. Calcium crystals preferentially precipitate block larger pore channels, whereas more evenly distributed throughout reducing reservoir’s heterogeneity. final single-core displacement experiment demonstrated sealing properties systems. developed exhibit excellent performance.

Language: Английский

Citations

0