A Workflow from Seismic to Forecast for Tight-Oil Reservoir Development Using a Semianalytical Well-Testing Model with Boundary Element Method
Bibliographic record
Abstract
Abstract Treatments of stimulated reservoir volume (SRV) have been the most effective techniques to enhance well productivity in unconventional plays, like tight-oil reservoirs. Unlike conventional reservoirs, the work in unconventional plays needs to focus on the SRV scale rather than field scale, which requires an integration of many techniques to properly evaluate the SRV properties. Unfortunately, there still lacks of a comprehensive integration of seismic, models, and data for SRV, although much work has been done for fracturing estimations and performance predictions of wells with SRV. To narrow the gap, this paper presents a comprehensive seismic-forecast workflow for tight-oil reservoir development on the SRV scale. The workflow is an integration of microseismic data, well testing interpretation, rate normalized pressure (RNP) interpretation, and numerical production history match. Its contents are described as the following steps: (1) introducing the technique of microseismic monitoring (MSM) and proposing a corresponding simplified SRV model, (2) developing and verifying its well-testing model with boundary element method (BEM), integral method, and superposition principle, (3) discussing the rate normalized pressure (RNP) model of SRV, (4) building a numerical SRV model, and (5) demonstrating the workflow by a field case from Junggar Basin. This paper paves a good way to evaluate well performance and improve future completions in tight-oil reservoirs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".