Stimulation Challenges and Solutions in Complex Carbonate Reservo
Bibliographic record
Abstract
Abstract This paper details the challenges of stimulation in one of the fields in North Oman. The field is a mature, heavily faulted and fractured carbonate reservoir on water flood where acid stimulation carries an inherently high risk of inducing several fold increases in water production. Acid placement challenges are immense as the fracture networks impart massive permeability contrasts and are most often the principal conduits for "premature" injection water breakthrough. We present field data of acid stimulation treatments on cased hole, oil producers as well as open-hole horizontal injectors using a novel self-diverting acid system. This fluid has been recently applied in other reservoirs but to date, none of these applications have held the same magnitude of challenges and barriers to successful implementation of stimulation treatments. We present numerous field studies which identify self-diverting acid systems as a key solution, together with best practices in candidate selection (through reservoir logging evaluation) and coiled tubing placement methodology, in providing consistent improvement in net hydrocarbon well productivity and enhanced injection profile for reservoir management. In addition we present results of a new surfactant based self-diverting system and compare the results to conventional polymer based treatments in this field. The results are indicative of a further improvement in success ratio of acidizing treatments. Similar methodology and fluid systems are applicable in other complex fractured reservoirs where acid placement and minimizing water production present the principal challenges. Moreover, as reservoir management becomes an ever-increasing concern, we present a treatment methodology to enhance productivity, reduce water cut and optimise reservoir sweep, all of which reduce the overall lifting costs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".