Data-driven modeling for forecasting oil recovery: A timeseries neural network approach for tertiary CO2 WAG EOR-Reference-Cited by-同舟云学术

Data-driven modeling for forecasting oil recovery: A timeseries neural network approach for tertiary CO2 WAG EOR

Published:2024-02 Issue: Volume:233 Page:212555
ISSN:2949-8910
Container-title:Geoenergy Science and Engineering
language:en
Short-container-title:Geoenergy Science and Engineering

Author:

Asante Jonathan^ORCID,Ampomah William,Tu Jiawei,Cather Martha

Publisher

Elsevier BV

Reference61 articles.

1. Quantitative interpretation of time-lapse seismic data at Farnsworth field unit: Rock physics modeling, and calibration of simulated time-lapse velocity responses;Acheampong;Greenhouse Gases: Science and Technology,2022

2. Assessment of chemo-mechanical impacts of CO2 sequestration on the caprock formation in Farnsworth oil field, Texas;Adu-Gyamfi;Sci. Rep.,2022

3. Toward reliable model for prediction Drilling Fluid Density at wellbore conditions: a LSSVM model;Ahmadi;Neurocomputing,2016

4. Prediction of Shear Wave Velocity Using Artificial Neural Network Technique , Multiple Regression and Petrophysical Data : A Case Study in Asmari Reservoir (SW Iran);Akhundi,2014

5. Generation of a complete profile for porosity log while drilling complex lithology by employing the artificial intelligence;Al-Sabaa,2021

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