Well Productivity in North Sea Chalks Related to Completion and Hydraulic Fracture Stimulation Practices
Bibliographic record
Abstract
Abstract The Joint Chalk Research (JCR) initiative is set up by a group of operators and partners in the Southern North Sea. The objective of the initiative is to increase the ultimate recovery in their respective chalk assets to 60%. Analyzing the different production technology options used in the assets thus far was the next step in better understanding the different recovery increment options. The initial 4 year productivity from 4 assets was analyzed. This paper presents the results of a study focused on increasing the understanding of productivity drivers using a database on well productivity related to different completions, stimulations and production options. The database contains 56 wells from 4 different assets and 750 acid and proppant treatments in 663 perforated intervals. It was found that the absolute total production per interval is similar for all assets; however the drawdown applied in 1 asset is 4 times lower than the other assets. The performance of the wells in most assets dropped strongly over time, except in the low drawdown asset. It was found that in addition to transient effects, it is likely that a decline in hydraulic fracture completion efficiency also contributes to this decrease in production performance. The low drawdown asset has a much better normalized production performance than the other assets. Other factors might also influence this result, however, compelling evidence has been found that the much lower drawdown may cause the better performance over time. The normalized recovery of propped fracced wells was better than the acid treated well in 2 of 3 assets using both stimulation types. The higher recovery of the propped treatments was mainly from better productivity over time compared with the acid fracture treatments. However, the initial productivity of the acid fractured well was much better, hence the economic balance could still tip to the acid treatments. The analysis showed that, for all assets except one, there is a significant difference in the performance of acid fractured wells and propped fractured wells over time. Indications are that production declines are not only due to depletion, but also related to deterioration of the completion efficiency as a function of pressure drawdown and suboptimal efficiency of acid treatments. An extensive statistical analysis indicated a strong dependency on asset grouping, which hampers the extrapolation of experience from one asset to the others
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".