Loading…

Experimental Investigation and Modeling of Fluid and Carbonated Rock Interactions with EDTA Chelating Agent during EOR Process

The injection of chemical fluids into oil reservoirs is gaining widespread attention in light of the declining conventional oil resources by recovering more hydrocarbons. This study is focused on using a chemical called ethylenediaminetetraacetic acid (EDTA) chelating agent in a carbonate reservoir...

Full description

Saved in:
Bibliographic Details
Published in:Energy & fuels 2023-01, Vol.37 (2), p.919-934
Main Authors: Daneshfar, Reza, Karimi Nouroddin, Maryam, Mousavi Golsefid, Seyedeh Zahra, Mohammadi-Khanaposhtani, Mohammad, Davoudi, Ehsan, Shariati, Kaveh
Format: Article
Language:English
Subjects:
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The injection of chemical fluids into oil reservoirs is gaining widespread attention in light of the declining conventional oil resources by recovering more hydrocarbons. This study is focused on using a chemical called ethylenediaminetetraacetic acid (EDTA) chelating agent in a carbonate reservoir to shed light on contact angle differences of 625 aged thin sections and rock dissolution under the influence of different pHs, temperatures, chelating times, and various chelating agent concentrations in seawater. According to a rock dissolution test, at least 5 wt % of EDTA chemical is needed to obtain oil recovery. A ζ potential test and scanning electron microscopy (SEM) images revealed that the mechanism of adsorption at low pH values and the expansion of the electrical double layer (EDL) at high pH values were responsible for wettability alteration, and an increase in EDTA concentration intensified each mechanism. Interfacial tension (IFT) measurements also showed that adding 1 and 10 wt % of the EDTA to the seawater solution reduced the IFT by 67.75% and 76.08%, respectively. The contact angle experiments demonstrated an increase in the mechanism that leads rock to behave more hydrophilically as pH, solution temperature, and chelating agent concentration in saltwater increased. Artificial neural network (ANN) methods also led to the introduction of a model to predict the contact angle employing multilayer perceptron neural networks (MPNN) and cascade feedforward neural networks (CFFNN). The CFFNN with two hidden neurons and trained by the Levenberg–Marquardt backpropagation algorithm is the most accurate model when comparing the accuracy of models for predicting contact angle values. The CFFNN model indicated that the weight percentage of the chelating chemical, which has a share of about 90%, had the greatest influence on the contact angle, and chelating time, with a share of less than 10%, had the least.
ISSN:0887-0624
1520-5029
DOI:10.1021/acs.energyfuels.2c02702