Acidification oxidation reagent system optimization on coal seams and stimulation effect evaluation
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".