Measuring Interwell Communication Using the Capacitance Model in Tight Reservoirs
Bibliographic record
Abstract
Abstract The Capacitance Model (CM) has been used to analyze flow rates to measure interwell connectivity (IWC). Numerous case studies show the CM can successfully predict production and identify flow paths and barriers in conventional reservoirs. The challenge is to extend the CM to perform as well in tight reservoirs, which includes fields with high well densities. Fields with such large numbers of wells creates the need to perform IWC evaluations over small regions, called windows, to speed computation and preserve the accuracy of estimates. Windowing, however, creates a problem in that wells within the window may be in communication with wells outside the window. The contribution of the outside wells can be significant and affect the IWC estimation. A CM modification is described which has a ‘pseudo well’, decoupling the outside wells from the window and accounting for varying parameters embedded in an estimate in low permeability reservoirs. We tested the new model with numerical simulation cases. The CM accuracy (ie simulated vs CM-predicted production rates) is very high (R2> 0.98) for both cases with balanced injection/production and those with local imbalances. The errors (RMSE) of the modified CM-predicted production rates are one-half the unmodified CM errors.. In the Cardium East Pembina field, we used the modified CM to analyze several areas having differing amounts of conglomerate and hydraulically fractured wells. IWC evaluations included areas with wells which were fractured after several years of production and we could therefore compare the pre-fracture IWC with the post-fracture IWC values. The IWC results clearly show elevated connectivities reflecting the presence of conglomerate. The model also captures differences between pre- and post-fracturing connectivities. The direction of the largest IWC change agrees with the expected maximum stress (fracture) direction (NE-SW) in the Western Canada Sedimentary Basin. The matches to measured production are very good, 0.76 < R2 < 0.95. With the new method, we do not need to include all nearby wells in the window and it tolerates the changes in an estimate accounting for the storage effect and long production well shut-in periods during analysis (tshut-in > 12 months). Due to the windowing capability, the method also enables us to make local IWC evaluations accurately in large tight reservoirs where injectors might have a similar injection profile. The field data illustrate the method's utility and insights it brings.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".