Acidification oxidation reagent system optimization on coal seams and stimulation effect evaluation
Notice bibliographique
Résumé
China has abundant coalbed methane (CBM) resources, and most of them are low-permeability and tight reservoirs, with generally low production rate and small recovery factor. Existing technologies face great challenges to meet the demand on CBM in China. It is desirable to develop new methods to improve the production rate and enhance recovery factor. In addition to physical stimulation methods such as hydraulic fracturing and open-hole cave completion, the use of chemical methods to improve physical properties of coal reservoirs has also been a hot research topic in recent years. Coal reservoir acidification and oxidation technology can promote desorption of gas and enlarge permeability of reservoir. But for different coal rank coal reservoirs, the acidification and oxidation agents need to be optimized and their performance evaluated. Laboratory experiments are conducted to compare and analyze the physical properties coal samples from Baode, Mu’ai, and Xinjiang blocks, including coal rank, texture, macroscopic characteristics, quality, porosity, permeability, element, and mineral composition. The optimal concentration of hydrochloric acid is determined through pre-dissolution experiment of coal powder in acid solution. Then a five-factor and three-level orthogonal experiment for acid solution optimization is designed and performed by using Design-Expert software, which identifies the sensitive factors affecting the dissolution. For the coal samples in Baode, Mu’ai, and Xinjiang blocks, the oxidant types and the corresponding acidification and oxidation agent systems are optimized. Applying these acidification and oxidation agent systems to coal samples from Baode, Mu’ai, and Xinjiang blocks, the change of porosity, permeability, and wettability are compared and analyzed. Finally, through numerical simulation, the gas production is predicted for acidification and oxidation in typical well group in Block Mu’ai. Results show that the acid solution has the best dissolution at a concentration of hydrochloric acid of 3 mol/L to 4 mol/L; Top factors played in the experiment are soaking time, acid type, soaking temperature, coal sample type, and acid concentration, in descending order of importance; The optimal oxidant is a hydrogen peroxide solution with a concentration of 3%; the mixed acidification oxidant formula in Baode block is 10% HCl + 2% CH3COOH + 2% HF + 3% H2O2; The optimal mixed acidification oxidant formula in Mu’ai block is 8% HCl + 2% CH3COOH + 4% HF + 3% H2O2; the optimal mixed acidification oxidant formula in Xinjiang block is 12% HCl + 1% CH3COOH + 1% HF + 3% H2O2; The higher the coal rank, the greater the HF content in the optimal acidification oxidant system. Both acidification and oxidation improve the porosity and permeability of coal samples to some extent, and the improvement in low-rank coal is more significant than that in high-rank coal. Acidification and oxidation have different effects on the wettability of coal: Acidification increases the hydrophilicity of coal, whereas oxidation reduce the hydrophilicity of coal; and the hydrophilicity of coal samples treated by the optimized acidification and oxidation system is weakened. Reservoir simulation results show that acidification and oxidation lead to a recovery factor of 64.64% after 10 years of production, which is 19.72% higher than that without acidification and oxidation. The advantage of acidification and oxidation is 0.97% after 18 years of production. However, the acidification and oxidation saved 8 years of production time to achieve a close final recovery factor, which greatly reduces the operating costs. The optimized acidizing oxidation agent systems for CBM reservoirs with low, medium, and high ranks improved the desorption and permeability of the target reservoirs, and increase well production and recovery factor. This research provides technical support for stimulation practices of CBM reservoirs in the aforementioned blocks in China, as well as similar coal reservoirs in the world.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».