Appraising Unconventional Play from Mini-Frac Test Analysis, Actual Field Case
Bibliographic record
Abstract
Abstract Unconventional reservoirs are an increasingly important worldwide resource. These reservoirs, generally defined by permeability values lower than 0.1 millidarcy, present today's engineer with unique challenges. To date, there are no available methods for evaluation of unconventional wells with respect to estimated ultimate recoveries (EURs). As a result, the conventional approach has been used resulting in initial well reserve and performance predictions containing large amounts of uncertainty. Ultimately, capital efficiency will suffer through these misinterpretations. A better understanding of completion efficiencies and of a well's performance in these new unconventional frontiers is needed. This paper will present an optimized EUR prediction procedure for the ultra-low permeability unconventional reservoir. The data from a standard mini-frac test will be analyzed and interpreted to produce the desired results. Illustrated within is the integration of geological data, Mini-Frac test (DFIT) analysis, and logging data to predict EUR's and future performance profiles. The DFIT analysis shows itself to be the most influential in this predictive workflow. One should note that the critical step in the process is proper identification of the reservoirs parameters: initial reservoir pressure, permeability, fracture half-length, and reservoir boundary A reservoir model was built using the results of the proposed workflow. Through multiple simulations, the model produced well-performance curves and EUR values that were in agreement. This procedure generates more accurate and reliable curves than the conventional decline curve analysis approach and properly applied, will improve individual well performance predictions with additional benefits including optimization of future completion metrics and spacing decisions.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".