Predicting oil recovery through CO<sub>2</sub> flooding simulation using methods of continuous and water alternating gas

Fathaddin, Muhammad Taufiq; Thomas, Mandy; Pasarai, Usman

doi:10.1088/1742-6596/1402/5/055015

Cited by 3 publications

(3 citation statements)

References 3 publications

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“…Currently, oil recovery factor prediction in tight oil reservoirs mainly revolves around water-driven development. The prediction methods can be broadly categorized into three main approaches: macro-equilibrium analysis, micro-experimental mechanistic analysis, and numerical simulation method [8][9][10][11][12][13][14][15]. Sun et al [16] developed a power-function-based material balance equation for high-pressure and ultrahigh-pressure gas reservoirs and investigated the impact of reservoir pressure depletion and recovery degree on reserve estimation reliability.…”

Section: Introductionmentioning

confidence: 99%

Prediction of ORF for Optimized CO2 Flooding in Fractured Tight Oil Reservoirs via Machine Learning

Yue,

Dai,

Liao

et al. 2024

Energies

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Tight reservoirs characterized by complex physical properties pose significant challenges for extraction. CO2 flooding, as an EOR technique, offers both economic and environmental advantages. Accurate prediction of recovery rate plays a crucial role in the development of tight oil and gas reservoirs. But the recovery rate is influenced by a complex array of factors. Traditional methods are time-consuming and costly and cannot predict the recovery rate quickly and accurately, necessitating advanced multi-factor analysis-based prediction models. This study uses machine learning models to rapidly predict the recovery of CO2 flooding for tight oil reservoir development, establishes a numerical model for CO2 flooding for low-permeability tight reservoir development based on actual blocks, studies the effects of reservoir parameters, horizontal well parameters, and injection-production parameters on CO2 flooding recovery rate, and constructs a prediction model based on machine learning for the recovery. Using simulated datasets, three models, random forest (RF), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM), were trained and tested for accuracy evaluation. Different levels of noise were added to the dataset and denoised, and the effects of data noise and denoising techniques on oil recovery factor prediction were studied. The results showed that the LightGBM model was superior to other models, with R2 values of 0.995, 0.961, 0.921, and 0.877 for predicting EOR for the original dataset, 5% noise dataset, 10% noise dataset, and 15% noise dataset, respectively. Finally, based on the optimized model, the key control factors for CO2 flooding for tight oil reservoirs to enhance oil recovery were analyzed. The novelty of this study is the development of a machine-learning-based method that can provide accurate and cost-effective ORF predictions for CO2 flooding for tight oil reservoir development, optimize the development process in a timely manner, significantly reduce the required costs, and make it a more feasible carbon utilization and EOR strategy.

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Section: Introductionmentioning

confidence: 99%

Prediction of ORF for Optimized CO2 Flooding in Fractured Tight Oil Reservoirs via Machine Learning

Yue,

Dai,

Liao

et al. 2024

Energies

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“…At present, the recovery rate of tight oil can be divided into microexperimental mechanistic analysis and macroequilibrium analysis after reservoir fracturing and water drive, including the core analysis method, related empirical formula method, water drive characteristic curve method, and numerical reservoir simulation method. Previous researchers applied them to solve the recovery prediction problem. − The core analysis method is to analyze the taken cores for tests such as simulated water injection to determine the original oil saturation of the reservoir and the residual oil saturation after the test so that recovery prediction can be made in microscopic cores . Based on this method, Hadia et al analyzed the relative permeability as a function of water saturation using core drive experiments and predicted the recovery rate by establishing a numerical simulation model for the dimensionless Buckley–Leverett equation .…”

Section: Introductionmentioning

confidence: 99%

“…formed a new water drive characteristic curve for recovery prediction that can reflect the relationship between oil–water relative permeability and water saturation more accurately for the actual situation of high water-cut oil reservoirs . The production decline method, including the Arps method, Blassingame method, etc, is the main method to predict the recovery rate through numerical reservoir simulations, which can predict future parameters of oil wells by judging the declining type to figure out the law, performing the overall balance analysis, and predicting the recovery rate. − There are also many solutions based on the production decline method combined with the water drive curve. Chen et al used the A-type water drive curve combined with Wong’s model in numerical simulations for a reasonable recovery rate .…”

Section: Introductionmentioning

confidence: 99%

Support Vector Regression Based on the Particle Swarm Optimization Algorithm for Tight Oil Recovery Prediction

et al. 2021

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Tight oil fields are affected by factors such as geology, technology, and development, so it is difficult to directly obtain an accurate recovery rate. The accurate prediction of the recovery rate is very important for measuring reservoir development effects and dynamic analysis. Traditional tight oil recovery predictions are obtained by conventional formula calculations and curve fitting, which are less applicable and very different from actual conditions. Machine learning can make accurate predictions based on a large amount of data, so it is used to predict the recovery rate of tight oil reservoirs. The recovery rate of 200 wells in M tight oil reservoirs ranges widely between 8.8 and 27.6%, with more than 14 factors affecting the recovery rate, and the overall declining rule is not clear. Therefore, this research combines the production data of horizontal wells with random forest, support vector regression (SVR), and other methods, establishing recovery prediction models to gain more accurate recovery predictions. First, the Pearson correlation coefficient and the random forest (RF) machine learning method are used to measure and calculate the degree of nonlinear influence of factors on oil well recovery. Second, SVR and optimization of support vector regression by particle swarm (PSO-SVR) recovery prediction models are developed and tested, with 75% of the data being used to train SVR and PSO-SVR recovery prediction models and 25% to verify the model. Third, the accuracy of the results of these two SVR oil recovery prediction models is compared, suggesting that when the data are scarce, the optimized model is more accurate than the unoptimized one by 10.85%. Thus, this model can assure a relatively more accurate prediction of oil recovery. Machine learning recovery prediction, being more accurate and applicable, enables the data of factors such as construction and production systems to be optimized in the future, enhancing the oil recovery rate.

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Study of the effect of cycle time and WAG ratio of CO2 miscible water alternating gas (WAG) injection in field X

Wahyuningrum¹,

Fathaddin²,

Nugrahanti³

et al. 2023

4th International Conference on Earth Science, Mineral and Energy

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Predicting oil recovery through CO₂ flooding simulation using methods of continuous and water alternating gas

Cited by 3 publications

References 3 publications

Prediction of ORF for Optimized CO2 Flooding in Fractured Tight Oil Reservoirs via Machine Learning

Prediction of ORF for Optimized CO2 Flooding in Fractured Tight Oil Reservoirs via Machine Learning

Support Vector Regression Based on the Particle Swarm Optimization Algorithm for Tight Oil Recovery Prediction

Study of the effect of cycle time and WAG ratio of CO2 miscible water alternating gas (WAG) injection in field X

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Predicting oil recovery through CO2 flooding simulation using methods of continuous and water alternating gas

Cited by 3 publications

References 3 publications

Prediction of ORF for Optimized CO2 Flooding in Fractured Tight Oil Reservoirs via Machine Learning

Prediction of ORF for Optimized CO2 Flooding in Fractured Tight Oil Reservoirs via Machine Learning

Support Vector Regression Based on the Particle Swarm Optimization Algorithm for Tight Oil Recovery Prediction

Study of the effect of cycle time and WAG ratio of CO2 miscible water alternating gas (WAG) injection in field X

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Predicting oil recovery through CO₂ flooding simulation using methods of continuous and water alternating gas