Production Forecast Using Produced Water Production for Wells Producing Tight/Shale Reservoirs
Bibliographic record
Abstract
Abstract As most readers can appreciate, stimulation of a multi-staged fractured horizontal well requires the injection of a water/slurry, combined with proppant, to create a fracture network for fluid flow to the wellbore. After this stimulation and general completion activities, much of the injected fracture fluid, along with reservoir fluids, are produced by virtue of drawdown and a declining flowing bottom hole wellbore pressure over the well's life. The well's production life naturally adheres to the law of "material balance" under all circumstances. This paper captures this flow physics from fracture flowback to online production all while establishing a novel relationship of the fracturing fluid and the produced reservoir hydrocarbons. Leveraging existing collected data, this proposed methodology can reliably forecast the primary phase of the reservoir for 10 years or more even with as little as 6 months of production history being used. More than 4500 tight/shale oil wells from both the Canadian and US shale plays (Bakken, Montney, Duvernay, Barnett, Eagle Ford, Permian) that have at least 10 years of production history have been tested and validated employing a ‘hindcasting’ technique. Thus, these case studies provide data-based evidence to compare actual field production data vs. the forecast from the novel proposed technique. The results from this study show that over 70% of the sample set of gas wells producing unconventional wells, all of which are limited to the first 6 months of production history, have 10-year forecasts within a 10% variance of field actuals. If 1.5 years of history is used, over 90% of the case study sample set have variances of 10% or less. For those oil producers using 6 months of history, over 80% of wells are within a 10% variance; however, with 1.5 years history also over 90% of the wells fall within 10% error. Conformance variances are very strongly dependent on data quality. In other words, where variances exceeded 10%, the historical production data was inherently very noisy and of low quality. Those wells are mostly attributed to some changing element of operating conditions. As in classical decline curve analysis, the historical operating conditions need to be maintained go-forward otherwise re-initialization would be required.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".