Simulation-Based Technology for Rapid Assessment of Redevelopment Potential in Stripper-Gas-Well Fields—Technology Advances and Validation in the Garden Plains Field, Western Canada Sedimentary Basin
Bibliographic record
Abstract
Abstract It is often difficult to quantify the redevelopment potential of marginal oil and gas fields due to a wide range of depositional environments, variability in reservoir properties, large numbers of wells, and limited reservoir information. With traditional simulation methods, evaluation of infill potential for these fields is time consuming, labor intensive and frequently cost-prohibitive. Without adequate assessment technology, some unprofitable infill campaigns may be initiated while other promising infill campaigns may be terminated prematurely due to disappointing early results. In this study, we developed a simulation-based regression technique to assess infill drilling potential in stripper gas well fields. With limited, basic reservoir information, this technique first estimates the spatial distribution of subsurface reservoir properties by rapid history matching of well production data. We implemented a sequential regression algorithm to estimate not only the permeability distribution, but also, the pore volume distribution from available flow rate measurements. Future production is forecast and infill drilling potential is determined using the estimated permeability and pore volume distributions. Because the method employs an approximate reservoir description, it identifies regions of the field with promising infill potential rather than individual infill well locations. The proposed technique provides rapid, reliable and cost-effective assessment of redevelopment potential in stripper gas well fields. In the paper we first validate our approach using synthetic reservoir data. We then apply the approach to the Second White Specks formation, Garden Plains field, Western Canada Sedimentary Basin. Prediction of infill potential in this gas field, which has more than 700 wells, demonstrates the power and utility of the proposed technique.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".