Prediction of Oil Recovery Factor in Stratified Reservoirs after Immiscible Water-Alternating Gas Injection Based on PSO-, GSA-, GWO-, and GA-LSSVM
In this study, we solve the challenge of predicting oil recovery factor (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>RF</mi></mrow></semantics></math></inline-formu...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-01-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/15/2/656 |
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| author | Pål Østebø Andersen Jan Inge Nygård Aizhan Kengessova |
| author_facet | Pål Østebø Andersen Jan Inge Nygård Aizhan Kengessova |
| author_sort | Pål Østebø Andersen |
| collection | DOAJ |
| description | In this study, we solve the challenge of predicting oil recovery factor (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>RF</mi></mrow></semantics></math></inline-formula>) 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi>M</mi><mo>*</mo></msup></mrow></semantics></math></inline-formula>). 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 (<i>R</i><sup>2</sup> = 0.9965 on the validation set), slightly better than GA and GSA (<i>R</i><sup>2</sup> = 0.9963). The performance metrics for MOD1 in the total dataset were: RMSE = 0.050 and <i>R</i><sup>2</sup> = 0.889; MOD2: RMSE = 0.0080 and <i>R</i><sup>2</sup> = 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. |
| first_indexed | 2024-03-10T01:32:57Z |
| format | Article |
| id | doaj.art-c6795bde3ad74e9b900bd68604bf2848 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T01:32:57Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-c6795bde3ad74e9b900bd68604bf28482023-11-23T13:39:58ZengMDPI AGEnergies1996-10732022-01-0115265610.3390/en15020656Prediction of Oil Recovery Factor in Stratified Reservoirs after Immiscible Water-Alternating Gas Injection Based on PSO-, GSA-, GWO-, and GA-LSSVMPål Østebø Andersen0Jan Inge Nygård1Aizhan Kengessova2Department of Energy Resources, Faculty of Science and Technology, University of Stavanger, 4021 Stavanger, NorwayBouvet, 4020 Stavanger, NorwayDepartment of Energy Resources, Faculty of Science and Technology, University of Stavanger, 4021 Stavanger, NorwayIn this study, we solve the challenge of predicting oil recovery factor (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>RF</mi></mrow></semantics></math></inline-formula>) 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi>M</mi><mo>*</mo></msup></mrow></semantics></math></inline-formula>). 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 (<i>R</i><sup>2</sup> = 0.9965 on the validation set), slightly better than GA and GSA (<i>R</i><sup>2</sup> = 0.9963). The performance metrics for MOD1 in the total dataset were: RMSE = 0.050 and <i>R</i><sup>2</sup> = 0.889; MOD2: RMSE = 0.0080 and <i>R</i><sup>2</sup> = 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.https://www.mdpi.com/1996-1073/15/2/656water-alternating-gas (WAG)physics-informed machine learningleast square support vector machine (LSSVM)particle swarm optimization (PSO)dimensionless numbershysteresis |
| spellingShingle | Pål Østebø Andersen Jan Inge Nygård Aizhan Kengessova Prediction of Oil Recovery Factor in Stratified Reservoirs after Immiscible Water-Alternating Gas Injection Based on PSO-, GSA-, GWO-, and GA-LSSVM Energies water-alternating-gas (WAG) physics-informed machine learning least square support vector machine (LSSVM) particle swarm optimization (PSO) dimensionless numbers hysteresis |
| title | Prediction of Oil Recovery Factor in Stratified Reservoirs after Immiscible Water-Alternating Gas Injection Based on PSO-, GSA-, GWO-, and GA-LSSVM |
| title_full | Prediction of Oil Recovery Factor in Stratified Reservoirs after Immiscible Water-Alternating Gas Injection Based on PSO-, GSA-, GWO-, and GA-LSSVM |
| title_fullStr | Prediction of Oil Recovery Factor in Stratified Reservoirs after Immiscible Water-Alternating Gas Injection Based on PSO-, GSA-, GWO-, and GA-LSSVM |
| title_full_unstemmed | Prediction of Oil Recovery Factor in Stratified Reservoirs after Immiscible Water-Alternating Gas Injection Based on PSO-, GSA-, GWO-, and GA-LSSVM |
| title_short | Prediction of Oil Recovery Factor in Stratified Reservoirs after Immiscible Water-Alternating Gas Injection Based on PSO-, GSA-, GWO-, and GA-LSSVM |
| title_sort | prediction of oil recovery factor in stratified reservoirs after immiscible water alternating gas injection based on pso gsa gwo and ga lssvm |
| topic | water-alternating-gas (WAG) physics-informed machine learning least square support vector machine (LSSVM) particle swarm optimization (PSO) dimensionless numbers hysteresis |
| url | https://www.mdpi.com/1996-1073/15/2/656 |
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