Prediction of CO2 storage performance in reservoirs based on optimized neural networks

Liu, Miaomiao; Fu, Xiaofei; Meng, Lingdong; Du, Xuejia; Zhang, Yuying

doi:10.1016/j.geoen.2023.211428

Cited by 7 publications

(2 citation statements)

References 11 publications

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“…Indeed, ML shows the feasibility of offering a more straightforward and quicker method than rigorous and numerous simulations or experiments. Many ML correlations have emerged with the development of computer tools, particularly in reservoir characterization, CO2 storage, production, and drilling operations (Ghoraishy et al, 2008;Liu et al, 2023; Nait Amar & Zeraibi, 2020; You & Lee, 2022).…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Summary of the Application of Machine Learning Techniques for CO<sub>2</sub>-EOR Projects

Du,

Salasakar,

Thakur

2024

Preprint

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In recent years, machine learning (ML) techniques have emerged as an efficient and effective technology within the petroleum industry. This paper focuses on the current application of ML in enhanced oil recovery (EOR) through CO2 injection, which exhibits promising economic and environmental benefits of climate-change mitigation strategy. Our comprehensive review explores the diverse use cases of ML techniques in CO2-EOR, including aspects such as minimum miscible pressure (MMP) prediction, well location optimization, oil production and recovery factor prediction, multi-objective optimization, Pressure-Volume-Temperature (PVT) properties estimation, Water Alternating Gas (WAG) analysis, and CO2-foam EOR, from 101 reviewed papers. In this comprehensive review, we catalog relative information, including the input parameters, objectives, data sources, train/test/validate information, results, evaluation, and rating score for each area based on criteria such as data quality, ML building process, and analysis of results. We also briefly summarized the benefits and limitations of ML methods in petroleum industry applications. Our detailed and extensive study could serve as an invaluable reference for employing ML techniques in the petroleum industry. Based on the review, we found that ML techniques offer great potential in solving problems in the majority of CO2-EOR areas involving prediction and regression. With the generation of massive amounts of data in the everyday oil and gas industry, machine learning techniques can provide efficient and reliable preliminary results for the industry.

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Section: Introductionmentioning

confidence: 99%

A Comprehensive Summary of the Application of Machine Learning Techniques for CO<sub>2</sub>-EOR Projects

Du,

Salasakar,

Thakur

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Indeed, ML shows the feasibility of offering a more straightforward and quicker method than rigorous and numerous simulations or experiments. Many ML correlations have emerged with the development of computer tools, particularly in reservoir characterization, CO 2 storage, production, and drilling operations [7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Summary of the Application of Machine Learning Techniques for CO2-Enhanced Oil Recovery Projects

Du,

Salasakar,

Thakur

2024

MAKE

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This paper focuses on the current application of machine learning (ML) in enhanced oil recovery (EOR) through CO2 injection, which exhibits promising economic and environmental benefits for climate-change mitigation strategies. Our comprehensive review explores the diverse use cases of ML techniques in CO2-EOR, including aspects such as minimum miscible pressure (MMP) prediction, well location optimization, oil production and recovery factor prediction, multi-objective optimization, Pressure–Volume–Temperature (PVT) property estimation, Water Alternating Gas (WAG) analysis, and CO2-foam EOR, from 101 reviewed papers. We catalog relative information, including the input parameters, objectives, data sources, train/test/validate information, results, evaluation, and rating score for each area based on criteria such as data quality, ML-building process, and the analysis of results. We also briefly summarized the benefits and limitations of ML methods in petroleum industry applications. Our detailed and extensive study could serve as an invaluable reference for employing ML techniques in the petroleum industry. Based on the review, we found that ML techniques offer great potential in solving problems in the majority of CO2-EOR areas involving prediction and regression. With the generation of massive amounts of data in the everyday oil and gas industry, machine learning techniques can provide efficient and reliable preliminary results for the industry.

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Optimization of CO2 EOR and geological sequestration in high-water cut oil reservoirs

Liu,

Meng,

Zhao

et al. 2024

J Petrol Explor Prod Technol

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In terms of the collaborative optimization of CO2 flooding for Enhanced Oil Recovery (EOR) and CO2 sequestration, previous studies have co-optimized both cumulative oil production and CO2 sequestration by various algorithms. However, these solutions fail to optimize the CO2 injection schemes for high-water cut oil reservoirs. This paper presents an optimization methodology for CO2 flooding and sequestration in high-water cut oil reservoirs. The production optimization was carried out by adjusting the injection and production rate. To solve the proposed objective functions, the simultaneous perturbation stochastic approximation (SPSA) algorithm is applied in this paper, and the CMG-GEM module is utilized to simulate the reservoir production performance. A typical high-water cut reservoir in the Shengli oilfield was used to verify the feasibility of the presented methodology. In this paper, the production performance and net present value (NPV) for continuous gas injection under different water cuts were analyzed. The optimal timing of transforming from water flooding to gas displacement for the high-water cut reservoir was optimized. In addition, the optimal water–gas ratios for Water-Alternating-Gas (WAG) flooding were determined. The sensitivity of NPV to gas injection price and carbon subsidy was analyzed. The results show that when the gas price is 0.178 $/m3 and the carbon subsidy is 0.0169 $/m3, the optimal timing of transforming from water flooding to gas injection should be earlier than the time when the water cut is 0.82. Through the combination of NPV, cumulative oil production rate, and CO2 sequestration volume for WAG flooding, the optimal WAG ratio should be 1:2. The presented method in this paper considers various economic indicators and can optimize CO2 flooding and sequestration in high-water cut oil reservoirs efficiently, which can provide some guidance for the design of CO2 flooding schemes in high-water cut oil reservoirs.

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Prediction of CO2 storage performance in reservoirs based on optimized neural networks

Cited by 7 publications

References 11 publications

A Comprehensive Summary of the Application of Machine Learning Techniques for CO<sub>2</sub>-EOR Projects

A Comprehensive Summary of the Application of Machine Learning Techniques for CO<sub>2</sub>-EOR Projects

A Comprehensive Summary of the Application of Machine Learning Techniques for CO2-Enhanced Oil Recovery Projects

Optimization of CO2 EOR and geological sequestration in high-water cut oil reservoirs

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