Multiphase Analytics in a High Water-Cut Environment
Bibliographic record
Abstract
Abstract Historically, Rate Transient Analysis (RTA) has been performed to characterize the reservoir properties associated with multi-fractured horizontal wells (MFHW), using single-phase linear flow models (Clarkson et al., [2014]). This paper purposes a different look at RTA, by applying traditional diagnostics to more than one producing fluid phase. These RTA techniques can yield substantial insight into reservoir characteristics and performance, and simultaneously help one understand the true reservoir being accessed by the wellbore. By incorporating multiphase RTA techniques, the strategic development of some high water producing unconventional targets can be optimized through section spacing design and reduced offset fracture communication. The premise behind this paper is centered on the extremely high water cut seen in wells within the Wolfcamp and Bone Spring formations, located in West Texas's Delaware Basin. As a wells produced fluid gains substantial proportions of water, traditional single phase analytic techniques need adjustment to allow for accurate fluid flow modeling and reservoir characterization. This paper evaluates an 8 well case study of actively producing wells in the lower 3rd Bone Spring formation. This case study provides insight into the RTA workflow for multiphase analytics, evaluating the oil and water fluid constituents, both independently and together. This approach yields more accurate characteristics of producing reservoir geometry, completion properties, stimulated formation permeability and inter-well fracture communication.
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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) | 0.000 | 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 | 0.001 | 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".