Probabilistically Mapping Well Performance in Unconventional Reservoirs with a Physics-Based Decline-Curve Model
Bibliographic record
Abstract
Summary Decline curves are the simplest type of model to use to forecast production from oil and gas reservoirs. Using a selected decline model and observed production data, a trend is projected to predict future well performance and reserves. Despite capturing general trends, these models are not sufficient at describing the underlying physics of complex multiphase porous-media flow phenomena and at explaining variations in production caused by changes in operational conditions. The application of these models within a Bayesian framework is a feasible alternative to mitigate this issue and obtain more-robust forecasts by representing the possible outcomes with probability distributions. However, one important aspect that conditions the production forecasts and their uncertainty is the design of a suitable prior distribution, which can be subjective. To address the aforementioned issue, this paper presents a workflow for the development of a localized prior distribution for new wells drilled in shale formations that combines production data from pre-existing surrounding wells and spatial data, specifically well-surface/bottom coordinates. This workflow aims to establish engineering criteria to reduce the subjectivity in the design of a prior distribution, assessing spatial continuity of the parameters of a physics-based decline-curve model (θ2 model), automatically identifying regions where uncertainty can be reduced a priori, and reliably quantifying the uncertainty. A case study of 814 gas wells in the Barnett Shale is presented, and several maps are generated for the analysis of important properties to be considered during field development. The dry-gas window presented more-continuous decline-curve parameters than the wet-gas and gas/condensate windows, which resulted in lower uncertainty with the localized prior approach. As more data are acquired with time, the uncertainty in the production forecasts is further reduced and the localized prior becomes more informative, especially in the dry-gas window. The localized prior can then serve as an indicator for the performance of new infill wells in different locations. Portions of the content of this paper were initially presented in Holanda et al. (2018b), and are further developed and reviewed here.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".