Reservoir Flow Allocation and Quantification Using Spectral Acoustic Data and Temperature Logs: Case Studies in Highly Deviated and Horizontal Wells Equipped with Slotted Liners and Sandscreens
Bibliographic record
Abstract
Abstract In highly deviated and horizontal wells, focusing on inside wellbore data (flowmeter, hold-up, density, etc.) may not be enough to understand in full the well and particulary reservoir behavior, as most of the time downhole logging tools lack the capacity to reveal reservoir/well interaction, mainly in those cases where flows could occur outside the wellbore (e.g. slotted liner or sandscreen). The objective of this paper is to provide a unique solution following which a thorough understanding of flow inside the wellbore and up to five meters into the reservoir can be obtained by temperature and spectral acoustic logs. Fluid flows in wells completed with slotted liners or sandscreens, especially those in deviated and horizontal wells, can occur either inside or outside the liner and/or casing. The conventional production logging technique in this type of completion is usually based on results generated by a mechanical flowmeter which measures flow only inside the wellbore but fails to depict flows and entry points behind tubing/casing; for example, when there is flow through cement channels or flow inside the annular section of the wells completed with a slotted liner. Mechanical spinners have other limitations; 1) they cannot pick up small rates in low potential wells, 2) they can give false response of apparent down flow (heavy-phase recirculation) in deviated wells with 2- and 3-phase flow, 3) they are prone to harsh wellbore conditions, particulary in open-hole completions. The Total Flow analisys (borehole and reservoir flow) uses Chorus spectral acoustic and Cascade temperature logging suite is an innovative addition to conventional single and multi-array PLT tools which are designed to address the above issues. This is done by a combination of hardware and modelling solutions which can be run in highly deviated and horizontal wells with a multi-phase flow and is able to pick up small flows behind multiple barriers. This paper presents the Total Flow results of spectral acoustics and temperature logging suite run in two horizontal wells equipped with sandscreens, and two highly deviated wells, all completed with slotted liners. These are the most difficult cases where conventional production logging tools usually fail to give vigorous results on fluid entry points as wells as the flow rate at each entry point, whereas spectral acoustic and temperature logs combination can provide detailed results on both entry points and quantity of each phase. The paper also compares the results of production logging and spectral acoustics/temperature logs in three wells to emphasize how using an inappropriate tool for a certain condition could give a wrong result which is far from the actual downhole situation.
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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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".