Hydraulic Fracturing as Development Strategy in the Congo Onshore
Bibliographic record
Abstract
Abstract The benefits of Hydraulic Fracturing (HF) are well recognized in the oil industry, even if in many world regions it is still seen as a remedial operation rather than a reservoir development strategy. The big part of worldwide HF operations, are performed extensively in the US and Canada, primarily for reservoir development purposes of tight gas fields. However during the last few years, the global trend has seen a change and HF is now encouraged for adding new reserves, aiding the development of low permeability marginal reservoirs and prolonging life of brown fields. In Congo Onshore, HF is now a consolidated reality, with more than 70 frac jobs pumped. Good results have encouraged management to increase fracturing activity: nowadays HF is performed on all the infill wells that are drilled in the low permeability layers of the reservoir. From the early stages of development only the layers with the higher permeability were produced, while the possibility to develop the low permeability layers was not considered, because of very poor or zero production results due to the application of conventional completion strategy. Since HF is now performed as a standard practice on new wells, it has been reconsidered for the application on old wells completed in the low permeability layers. The challenge encountered on these old wells, has been the presence of long perforated interval. Rigless operations (such as sand plug) and work-over operations (such as cementing of old perforated interval and re-perforations) have been needed for fracturing in order to avoid fracture initiation issues like multiple fractures and early screenout. This paper will show the lessons learned and the main results achieved during this campaign and it is particularly focused on operational and logistic aspects, offering a full operational overview to all Companies and Operators that intend to apply this technology on their assets, maximizing oil recovery.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".