Production Array Logs in Bakken Horizontal Shale Play Reveal Unique Performance Based on Completion Technique
Bibliographic record
Abstract
Abstract ConocoPhillips drilled and completed three horizontal wells in the Bakken Shale, North Dakota in 2010; these wells contained between 33 and 40 stages. The lateral of each well consisted of a hybrid design using sliding sleeves in the toe- most half of the well and plugs/perforations for the heel-most half. Multiple completion techniques were pumped in an alternating pattern throughout the plug and perforation section of each well, and production array logs were deployed on coiled tubing in an attempt to determine fracture design best practices. As one of the key parameters to horizontal well performance in the shale plays, fracture performance evaluation becomes the main objective. The flow contribution from each fracture stage was first determined from array production log interpretations in terms of the in-situ productivity index, which became the basis for fracture stage performance analysis. This paper also includes a discussion of the challenges associated with understanding the multiphase fluid flow behavior in horizontal wells. The subsequent analysis of fracture performance was performed to relate the in-situ productivity index with other well parameters, such as well trajectory, fracture method, fluid and reservoir information around the well and the mud log information during drilling. Oil, gas, and water rates were generated for each stage and correlated to completion technique. The final analysis was aimed to answer some of the questions about how certain fracture stages performed better in comparison to others. This analysis includes identifying parameters in favor of increasing fracture performance and defining the steps needed to deal with the challenges related to the geological nature of the field. The information from this integrated evaluation result was then used to define a better strategy to improve the well performance in the future drilling campaign and to optimize the commercial value of the field.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".