Dynamic Simulation to Predict Self-Restart Potential of Acid Stimulated Wells by Bullhead Treatment in Deepwater Environment
Bibliographic record
Abstract
Abstract Matrix stimulation by acid is a technique used to enhance production from underperforming wells. It involves injection of acid at pressures lower than fracture pressure, with the aim of dissolving (in sandstones) or bypassing (in carbonates) the damage in the near-wellbore region, thereby clearing/improving the rock pore-throats and improving flow of hydrocarbons. Dynamic modelling of the acid stimulation process is essential to optimise the process by understanding conditions that will oppose self-restart of the wells treated by fluid bullhead and also to formulate operational guidelines. In particular, for the production systems discussed in this paper, it was imperative to determine whether the well(s) could self-restart (i.e., self-unload the intervention liquid volumes left in the well and nearwellbore zone) without intervention (e.g., nitrogen kickoff) after the acidizing treatment is completed. Dynamics in the various system components—the pipeline, wellbore, and near-wellbore reservoir area—affect each other and also the overall feasibility of attaining a self-restart (liquid unloading) after stimulation. Evidently, for an operation such as this, changes in saturations and effective permeability of the fluid phases in the near-wellbore region are of great importance. It follows that integration of the transient multiphase well model with a near-wellbore reservoir model becomes necessary to capture the full dynamics of the system. The integration of the well model to a near-wellbore reservoir model is, in this paper, discussed as a coupled model. By contrast, a typical dynamic standalone well model would use an inflow performance relationship (IPR) to represent the reservoir performance, which would simply have no history of fluids injected into the reservoir and their distribution in the near-wellbore area of the reservoir rock. As a result, there would be a lesser degree of confidence in the predictive capability of such standalone well model. Using coupled models, two gas wells were tested for their self-restart feasibility following acid stimulation. Furthermore, two methods of fluid injection were simulated to compare their effectiveness in aiding the self-restart of the wells. One approach involves sequential injection of fluids, followed by wellbore displacement with nitrogen to squeeze the treatment fluids (liquid) away from the near-wellbore region, making self-restart more likely. The second approach is to simultaneously inject the treatment fluids and nitrogen to lower the effective density of the treatment fluid mixture and also to energize the injected stimulation fluids, with the aim of facilitating self-restart. The fluids sequence of this second approach also ends with the wellbore displacement by nitrogen. This paper presents the results of the modelling and simulations carried out for gas wells and their fluids injection sequence. Important differences for the design of the operation were found when the stand alone and the coupled models were compared. The findings of this study have since been supported by observations in the field.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".