A practical workflow for performance prediction of low permeability reservoirs
Bibliographic record
Abstract
The Society of Petroleum Evaluation Engineers (SPEE) recently released ‘Monograph 4 – estimating ultimate recovery of developed wells in low-permeability reservoirs’ (hereafter called ‘Monograph 4’; SPEE 2016). This paper outlines a practical engineering workflow enabling companies to evaluate unconventional plays developed with horizontal multi-stage fractured wells consistent with the principles summarised in Monograph 4. This workflow has many applications including assessing potential acquisitions, defining new plays, evaluating competitor results, corporate budget processes, long-term business planning, portfolio management and reserves certification. The workflow, developed and refined over several years, has proven effective in large-scale applications. It enables engineers to readily identify and assess flow regimes, estimate the time to boundary dominated flow and estimate the flow patterns of boundary dominated flow for large groups of wells. The workflow also allows the engineer to deal with changing well designs and completion techniques. Throughout the workflow, the geological, engineering and statistical methods described in Monograph 4 are used. This provides the foundation to define and create representative type curves, yielding statistically reliable estimates of expected ultimate recovery (EUR) and production forecasts for asset evaluation with an accompanying characterisation of the confidence of these estimates. A case study demonstrating application of this workflow and a summary of results are presented. Potential sources of error in the technical analysis and application of type curves are identified; the technical and commercial impacts of these errors are highlighted. By allowing the evaluator to focus time and attention on the details of the technical analyses, companies can achieve a quicker, more in-depth analysis of the development of these large-scale unconventional resource projects.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.008 |
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".