Bibliographic record
Abstract
Abstract Development of mature oil fields has been, and will increasingly be, an attractive subject. Mature field development practices can be divided into two major groups: (1) well engineering and (2) reservoir engineering. This paper focuses on the reservoir engineering aspects. An extensive review of previously reported reservoir management practices for mature field development is provided. After the definition of mature field and an overview, different aspects of mature field development are outlined. The first issue covered is the estimation of remaining reserves focusing on the determination of the amount and location of the residual oil after primary and secondary recovery using field, log, and core data. After valuing the remaining oil, methods to recover it are classified. They include tertiary recovery, infill drilling, horizontals, optimal waterflooding design for mature fields, optimal well placement and other reservoir management practices. Suggested or implemented field application examples for big fields owned by majors and small fields owned by independents are presented. Special attention is given to tertiary oil recovery. An extensive review and critical analysis of tertiary recovery techniques covering the theoretical, practical, and economical aspects of it are provided. The emphasis is on their applicability in mature field development in terms of effectiveness (incremental recovery) and efficiency (cost and recovery time). Laboratory and field scale applications of different tertiary recovery techniques, i.e., gas (double displacement, WAG, and miscible-immiscible HC, CO2, and N2), chemical (dilute surfactant, polymer, and micellar injection), and thermal (air and steam) injection, conducted to develop mature fields are included. Specific examples of big/giant fields, fields producing for decades, and mid to small size fields were selected. Differences in reservoir management strategies for majors, independents, and national oil companies are discussed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".