Completion Sand Control Evolution in the Peng Lai Field, Bohai Bay, Offshore China
Bibliographic record
Abstract
Abstract This paper presents the evolution of completion sand control designs for the Peng Lai field in Bohai Bay, offshore China. A range of sand control designs from open hole gravel packs to open hole standalone screens to cased hole high rate water packs and cased hole frac-packs have been tried over time. Evaluation of these various designs indicates that single-trip multi-zone cased hole frac-pack designs provide the most effective method for developing the thick, low net-to-gross, unconsolidated sandstone reservoirs in this waterflood field. The paper describes the completion design and evaluation process used in the field and shows how field challenges led to innovative frac-pack downhole equipment designs. This paper highlights completion practices and results from a range of well types used to develop the large Peng Lai field, offshore China. Development challenges and productivity and reliability results for alternative completion designs are discussed on the basis of field performance. The resulting preferred design, consisting of cased hole frac-packs using large-bore and small-bore single-trip multi-zone equipment are then discussed in further detail with emphasis on development of an innovative, fit-for-purpose, small-bore design. The installation challenges, solutions and successes experienced during the design and installation process and the actual performance and benefits of the new system are also discussed in this paper. Over the course of Peng Lai field development, a range of alternative completion sand control designs have been tried and evaluated. Cased hole frac-packs provide the most effective completion method for the thick, low net-to-gross, sandstone reservoirs. To enable effective completions in sidetracked wells, a 7" single-trip multi-zone frac pack tool has been developed. This small-bore system has been used to complete 13 wells with 67 zones in the field to date, saving over 43 rig days and $8MM. This paper presents reliability and productivity comparisons for a range of alternative completion sand control designs in a single, large field environment. A new 7" single-trip, fit-for-purpose design has been developed for the field and successfully implemented in multiple well campaigns. Installation time data and cost savings are provided.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".