An Evaluation of Well Deployment Aspects Affecting Well Flow Performance on Horizontal Production Log Results
Bibliographic record
Abstract
Abstract Production logging uses measurements to understand the velocity & fluid types comingling as open reservoir intervals deliver products which begin to flow up hole. In a horizontal flowing well environment, the logging tools of choice can be individual discreet measurements situated across the cross sectional flow area to measure and define the fluid type & velocity. Flow measurements are much more difficult to measure as most horizontal flowing environments are not stable. Deployment of this tool type can be conveyed using a coiled tubing setup or a well tractor conveyed tethered to a wireline. These deployment methods can have an effect on the flow regime during the logging survey. When oil company operations engineering teams require production log data across a flowing lateral, one aspect seldom addressed is how the deployment intervention can affect the well flow performance when deploying the production logging measurements. Often times the perturbation causing the well performance is based on the deployment intervention selection. This in return causes the well to underperform at the point in time a production logging survey is needed; leaving the logging technology with an unstable environment to deliver a confident result. What tends to occurs within the engineering teams is the perception that there is an inability of present day technology to accurately measure the well performance, meanwhile the deployment aspect chosen & the procedures to convey a production log actually can be a main culprit in changing the well flow dynamics & stability. Via deployment experience & a thorough understanding of well flow combined with production log analytical skill sets. This technical paper will discuss in a case history a production log run in a horizontal well, the deployment aspects & well flow challenges using wireline tractor & coiled tubing interventions, and how the end result was able to assist the customer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".