An Operators Investigation of Performance Drivers Using Diagnostics and Data Analytics
Bibliographic record
Abstract
Abstract In multi-stage plug-and-perf horizontal well completions, there are a multitude of moving parts and variables to consider when evaluating performance drivers. Properly identifying performance drivers allows an operator to focus their efforts to maximize the rate of return of resource development. Typically, well-to-well comparisons are made to help identify performance drivers, but in many cases the differences are not clear. Identifying these drivers may require a better understanding of performance variability along a single lateral. Data analytics can help to identify performance drivers using existing data from development activities. In the case study below, multiple diagnostics are utilized to identify performance drivers. A combination of completion diagnostics including oil and water tracers, stimulation data, reservoir data, 3D seismic, and borehole image logs were collected on a set of wells in the early appraisal phase of a field. Using oil tracers as the best indication of stage level performance along the laterals, data analytics is applied to uncover the relationships between the tracers and the numerous diagnostics. After smoothing was applied to the dataset, trends between oil tracer recovery, several independent variables and features seen in image logs and 3D seismic were identified. All the analyses pointed to decreasing tracer recovery, and likely decreased oil production, near faulted areas along each lateral. A random forest model showed a moderate prediction power, where the model's predicted tracer recovery on blind stages was able to explain 54% of the variance seen in the tracer response (r2=0.54). This analysis suggests the identification of certain faulted areas along the wellbore could lead to ways of improving individual well economics by adjusting completion design in these areas.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".