Selection of Acid Types to Achieve Uniform Stimulation in Long Laterals of Horizontal Wells
Bibliographic record
Abstract
Abstract Horizontal wells with long laterals are being used to develop low-permeability carbonate reservoirs in the world. After completion of a well, acidizing is usually conducted to remove the potential damage caused by drilling and completion. However, in bullheading acid injection, it is usually difficult for the acid to distribute evenly along a lateral due to quick consumption of acid along the wellbore, especially in MRC (Maximum Reservoir Contact) wells or wells with long laterals (>1000m). In many cases the toe area is even left un-stimulated at all. Different types of retarded acids are being developed in industry to reduce acid consumption rate in order to let them penetrate further, but the effective travelling distances of these acids are never systemically studied and compared, leaving field engineers a difficulty in selecting an acid for a specific well. In our work, the properties of different types of acids in industry are reviewed. A flow and reaction model with consideration of acid flow friction and acid-rock reaction kinetics is established and a method is found to solve it. Five typical cases are run based on the model and the results of an effective acid flowing distance, effective acid reacting distance and effective acid travelling distance are obtained and compared. Based on our study, two principles of selecting acid types for horizontal wells with long laterals are proposed: "flow to the toe" and "react to the toe", which mean the friction of an acid as well as the leak off rate of the acid should be small enough to let the fluid reach the toe under a given injection rate; the diffusion rate of H+ and the acid-rock reaction rate must be small enough to ensure that the acid is still active when it reaches the toe. Four key considerations are proposed regarding acid type selection: acid flow friction, acid leak off rate, mass transfer of H+, and acid-rock reaction rate. A general guideline for the key parameter control and acid type selection to achieve a good acid distribution in the wellbore under different conditions is also shown. Field engineers can use our guideline to select acid candidates initially, and then use our model and program to quantitatively evaluate if the acid can effectively cover the whole lateral length.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".