Deterministic tools to predict recovery performance of carbonated water injection

Esene, Cleverson; Zendehboudi, Sohrab; Shiri, Hodjat; Aborig, Amer

doi:10.1016/j.molliq.2019.111911

Cited by 24 publications

(5 citation statements)

References 59 publications

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“…Moreover, deterministic models are generally easier to comprehend and interpret, they boast efficiency, and have a proven track record in numerous environmental studies. Notably, the relationship between input and output parameters does not require governing equations [547]. The visualisation of results, often in the form of animations-a common feature in deterministic software-can also be more engaging for decision-makers and stakeholders compared to outcomes of probabilistic representations [548].…”

Section: Deterministic Modelsmentioning

confidence: 99%

A Critical Review of the Modelling Tools for the Reactive Transport of Organic Contaminants

Samborska-Goik,

Pogrzeba

2024

Applied Sciences

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The pollution of groundwater and soil by hydrocarbons is a significant and growing global problem. Efforts to mitigate and minimise pollution risks are often based on modelling. Modelling-based solutions for prediction and control play a critical role in preserving dwindling water resources and facilitating remediation. The objectives of this article are to: (i) to provide a concise overview of the mechanisms that influence the migration of hydrocarbons in groundwater and to improve the understanding of the processes that affect contamination levels, (ii) to compile the most commonly used models to simulate the migration and fate of hydrocarbons in the subsurface; and (iii) to evaluate these solutions in terms of their functionality, limitations, and requirements. The aim of this article is to enable potential users to make an informed decision regarding the modelling approaches (deterministic, stochastic, and hybrid) and to match their expectations with the characteristics of the models. The review of 11 1D screening models, 18 deterministic models, 7 stochastic tools, and machine learning experiments aimed at modelling hydrocarbon migration in the subsurface should provide a solid basis for understanding the capabilities of each method and their potential applications.

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Section: Deterministic Modelsmentioning

confidence: 99%

A Critical Review of the Modelling Tools for the Reactive Transport of Organic Contaminants

Samborska-Goik,

Pogrzeba

2024

Applied Sciences

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“…Cheraghi et al [36] suggested employing deep ANN and random forest (RF) models for identifying the most appropriate EOR techniques, leveraging data sourced from oil and gas publications. Esene et al [37] conducted predictions of the ORF using ANN, least-squares support vector machines, and gene expression programing for carbonate water-injection processes. In another study, Pan et al [38] constructed a machine learning model utilizing extreme gradient boosting (XGBoost) to infer reservoir porosity from well log data.…”

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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“…This algorithm has been applied in many contexts, such as predicting drilling fluid density [28], gas solubility [29][30][31][32], water availability [33], energy consumption [34,35], shale gas adsorption [36], wind power [37,38] and even tourism flow [39]. LSSVM has been successfully combined with optimizers such as particle swarm optimization (PSO), genetic algorithm (GA) and grey wolf Optimization (GWO) and has, in many cases, outperformed regression algorithms such as artificial neural networks, radial basis function, gene expression programming and adaptive neuro-fuzzy interference system [29,30,40,41].…”

Section: Introductionmentioning

confidence: 99%

“…[41] used LSSVM and other ML approaches to predict two-phase relative permeabilities and combined them via correlations in previous research to estimate threephase relative permeabilities and the performance of a WAG core flood. [40] correlated the oil recovery performance of EOR carbonated water injection using LSSVM. [44] used ML to optimize well placement during WAG injection.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Oil Recovery Factor in Stratified Reservoirs after Immiscible Water-Alternating Gas Injection Based on PSO-, GSA-, GWO-, and GA-LSSVM

2022

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In this study, we solve the challenge of predicting oil recovery factor (RF) in layered heterogeneous reservoirs after 1.5 pore volumes of water-, gas- or water-alternating-gas (WAG) injection. A dataset of ~2500 reservoir simulations is analyzed based on a Black Oil 2D Model with different combinations of reservoir heterogeneity, WAG hysteresis, gravity influence, mobility ratios and WAG ratios. In the first model MOD1, RF is correlated with one input (an effective WAG mobility ratio M*). Good correlation (Pearson coefficient −0.94), but with scatter, motivated a second model MOD2 using eight input parameters: water–oil and gas–oil mobility ratios, water–oil and gas–oil gravity numbers, a reservoir heterogeneity factor, two hysteresis parameters and water fraction. The two mobility ratios exhibited the strongest correlation with RF (Pearson coefficient −0.57 for gas-oil and −0.48 for water-oil). LSSVM was applied in MOD2 and trained using different optimizers: PSO, GA, GWO and GSA. A physics-based adaptation of the dataset was proposed to properly handle the single-phase injection. A total of 70% of the data was used for training, 15% for validation and 15% for testing. GWO and PSO optimized the model equally well (R2 = 0.9965 on the validation set), slightly better than GA and GSA (R2 = 0.9963). The performance metrics for MOD1 in the total dataset were: RMSE = 0.050 and R2 = 0.889; MOD2: RMSE = 0.0080 and R2 = 0.998. WAG outperformed single-phase injection, in some cases with 0.3 units higher RF. The benefits of WAG increased with stronger hysteresis. The LSSVM model could be trained to be less dependent on hysteresis and the non-injected phase during single-phase injection.

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Deterministic tools to predict recovery performance of carbonated water injection

Cited by 24 publications

References 59 publications

A Critical Review of the Modelling Tools for the Reactive Transport of Organic Contaminants

A Critical Review of the Modelling Tools for the Reactive Transport of Organic Contaminants

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

Prediction of Oil Recovery Factor in Stratified Reservoirs after Immiscible Water-Alternating Gas Injection Based on PSO-, GSA-, GWO-, and GA-LSSVM

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