Accurate prediction of properties of carbon dioxide for carbon capture and sequestration operations

Ahmadi, Mohammad Ali; Kashiwao, Tomoaki; Rozyn, J; Bahadori, Alireza

doi:10.1080/10916466.2015.1107847

Cited by 18 publications

(9 citation statements)

References 25 publications

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“…The artificial neural network (ANN) or multilayer perceptron (MLP), a sub-field of machine learning and artificial intelligence, deals with algorithms inspired by the biological structure and functioning of human brains to solve complex non-linear problems by using the results from similar past observations [34]. The ANN includes one or more hidden layers, the input layer and the output layer [46].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

“…Thus, several studies use supervised machine learning for reservoir characterization, production forecasting, well spacing optimization, monitoring, testing, history matching, fluid property estimation, enhanced oil recovery, outlier detection, etc. [31][32][33][34][35].…”

Section: Introductionmentioning

confidence: 99%

“…Other studies have used different ANN models to optimize the CO 2 -EOR projects [37] and estimate the oil recovery performance of depleted reservoirs [38]. Ahmadi et al [34] used an ANN to predict the feasibility of CO 2 sequestration in CCS in deep saline aquifers. They used 800 different data points from the literature to predict the viscosity of the injected CO 2 by using an ANN.…”

Section: Introductionmentioning

confidence: 99%

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Physics-Based Proxy Modeling of CO2 Sequestration in Deep Saline Aquifers

Khanal

Shahriar

2022

Energies

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The geological sequestration of CO2 in deep saline aquifers is one of the most effective strategies to reduce greenhouse emissions from the stationary point sources of CO2. However, it is a complex task to quantify the storage capacity of an aquifer as it is a function of various geological characteristics and operational decisions. This study applies physics-based proxy modeling by using multiple machine learning (ML) models to predict the CO2 trapping scenarios in a deep saline aquifer. A compositional reservoir simulator was used to develop a base case proxy model to simulate the CO2 trapping mechanisms (i.e., residual, solubility, and mineral trapping) for 275 years following a 25-year CO2 injection period in a deep saline aquifer. An expansive dataset comprising 19,800 data points was generated by varying several key geological and decision parameters to simulate multiple iterations of the base case model. The dataset was used to develop, train, and validate four robust ML models—multilayer perceptron (MLP), random forest (RF), support vector regression (SVR), and extreme gradient boosting (XGB). We analyzed the sequestered CO2 using the ML models by residual, solubility, and mineral trapping mechanisms. Based on the statistical accuracy results, with a coefficient of determination (R2) value of over 0.999, both RF and XGB had an excellent predictive ability for the cross-validated dataset. The proposed XGB model has the best CO2 trapping performance prediction with R2 values of 0.99988, 0.99968, and 0.99985 for residual trapping, mineralized trapping, and dissolution trapping mechanisms, respectively. Furthermore, a feature importance analysis for the RF algorithm identified reservoir monitoring time as the most critical feature dictating changes in CO2 trapping performance, while relative permeability hysteresis, permeability, and porosity of the reservoir were some of the key geological parameters. For XGB, however, the importance of uncertain geologic parameters varied based on different trapping mechanisms. The findings from this study show that the physics-based smart proxy models can be used as a robust predictive tool to estimate the sequestration of CO2 in deep saline aquifers with similar reservoir characteristics.

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Section: Artificial Neural Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Physics-Based Proxy Modeling of CO2 Sequestration in Deep Saline Aquifers

Khanal

Shahriar

2022

Energies

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“…To address this issue, a new approach needs to be explored for improving the accuracy of predicting trapping efficiency in brine aquifers. In the most recent years, machine learning (ML) techniques are well-known as powerful for solving the complex in the petroleum industry and environmental science (Ahmadi et al, 2016(Ahmadi et al, , 2015Mahdaviara et al, 2021;Vo Thanh et al, 2020d). Also, ML algorithms have successfully developed several high accuracy predictive models in CCS.…”

Section: Introductionmentioning

confidence: 99%

Predicting CO2 Trapping Efficiency In Saline Aquifers By Machine Learning System: Implication To Carbon Sequestration

Thanh¹,

Lee²

2021

Preprint

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Carbon dioxide (CO2) storage in saline formations has been identified as a practical approach to reducing CO2 levels in the atmosphere. The residual and solubility of CO2 in deep saline aquifers are essential mechanisms to enhance security in storing CO2. In this research, CO2 residual and solubility in saline formations have been predicted by adapting three Machine Learning models called Random Forest (RF), extreme gradient boosting (XGboost), and Support Vector Regression (SVR). Consequently, a diversity of the field-scale simulation database including 1509 data samples retrieved from reliable studies, was considered to train and test the proposed models to achieve this task. Graphical and statistical indicators were evaluated and compared the predictive ML model performance. The predicted results denoted that the proposed ML models are ranked from high to low as follows: XGboost>RF>SVR. Additionally, the performance analyses revealed that the XGboost model demonstrates higher accuracy in predicting CO2 trapping efficiency in saline formation than previous ML models. The XGboost model yields very low root mean square error (RMSE) and R2 for both residual and solubility trapping efficiency. At last, the applicable domain of XGboost model was validated, and only 24 suspected data points were recognized from the entire databank.

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“…These authors stated that the ANN model is a robust tool for predicting the feasibility of CO 2 sequestration with high accuracy. Moreover, Ahmadi et al 35 applied ANN for the prediction of the CO 2 properties in carbon capture and sequestration operations. Besides, ANN models were used for the evaluation performance of the WAG process in CO 2 -EOR and sequestration projects 36 , 37 .…”

Section: Introductionmentioning

confidence: 99%

Application of artificial neural network for predicting the performance of CO2 enhanced oil recovery and storage in residual oil zones

Thanh

Sugai

Sasaki

2020

Sci Rep

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Residual Oil Zones (ROZs) become potential formations for Carbon Capture, Utilization, and Storage (CCUS). Although the growing attention in ROZs, there is a lack of studies to propose the fast tool for evaluating the performance of a CO2 injection process. In this paper, we introduce the application of artificial neural network (ANN) for predicting the oil recovery and CO2 storage capacity in ROZs. The uncertainties parameters, including the geological factors and well operations, were used for generating the training database. Then, a total of 351 numerical samples were simulated and created the Cumulative oil production, Cumulative CO2 storage, and Cumulative CO2 retained. The results indicated that the developed ANN model had an excellent prediction performance with a high correlation coefficient (R2) was over 0.98 on comparing with objective values, and the total root mean square error of less than 2%. Also, the accuracy and stability of ANN models were validated for five real ROZs in the Permian Basin. The predictive results were an excellent agreement between ANN predictions and field report data. These results indicated that the ANN model could predict the CO2 storage and oil recovery with high accuracy, and it can be applied as a robust tool to determine the feasibility in the early stage of CCUS in ROZs. Finally, the prospective application of the developed ANN model was assessed by optimization CO2-EOR and storage projects. The developed ANN models reduced the computational time for the optimization process in ROZs.

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Accurate prediction of properties of carbon dioxide for carbon capture and sequestration operations

Cited by 18 publications

References 25 publications

Physics-Based Proxy Modeling of CO2 Sequestration in Deep Saline Aquifers

Physics-Based Proxy Modeling of CO2 Sequestration in Deep Saline Aquifers

Predicting CO2 Trapping Efficiency In Saline Aquifers By Machine Learning System: Implication To Carbon Sequestration

Application of artificial neural network for predicting the performance of CO2 enhanced oil recovery and storage in residual oil zones

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