New Time/Rate Relations for Decline-Curve Analysis of Unconventional Reservoirs
Bibliographic record
Abstract
This article, written by Editorial Manager Adam Wilson, contains highlights of paper SPE 162910, ’Practical Considerations for Decline-Curve Analysis in Unconventional Reservoirs - Application of Recently Developed Time/Rate Relations,’ by V. Okouma, SPE, Shell Canada Energy; D. Symmons, Consultant; N. Hosseinpour-Zonoozi, SPE, and D. Ilk, SPE, DeGolyer and MacNaughton; and T.A. Blasingame, SPE, Texas A&M University, prepared for the 2012 SPE Hydrocarbon Economics and Evaluation Symposium, Calgary, 24-25 September. The paper has not been peer reviewed. This work presents a workflow that can be used to analyze and forecast time/rate data of wells in low- and ultralow-permeability reservoirs. The key component of the workflow is the application of diagnostic plots to guide the analysis and obtain model parameters for a given time/rate relation. Once model parameters are obtained, the production profile is extrapolated to yield the estimated ultimate recovery (EUR) at a specified time limit or abandonment rate. Introduction The starting point for any discussion of decline-curve analysis for unconventional reservoirs must be an understanding that no simplified time/rate model can accurately capture all elements of the performance behavior. In addition, no time/rate model can be expected to pro-vide a completely unique forecast of future performance or prediction of EUR. It is important to be both realistic and practical when attempting to characterize production performance from systems where the permeability is on the order of 10–500 nd, the reservoir flow system is complex, and, although the induced-hydraulic-fracture system enables (and dominates) the production performance, there is only the most rudimentary understanding of the flow structure in the fracture systems. It is essential that these conditions be established as a starting point. Not doing so will inevitably lead the analyst to interpretations based on incorrect assumptions as well as significant bias. The authors assert that reasonable production forecasts and predictions of EUR can be made, but not in isolation, not solely looking at the data and the selected time/rate model. The analyst must consider the nature of the resource and the significant uncertainty in the ability to apply simple time/rate relations to a very complex reservoir system. As an attempt to better represent the general character of time/rate production data for a multistage-fractured horizontal well in an ultralow-permeability reservoir, numerous authors have developed time/rate relations using certain specific bases to represent a particular scenario. These developments include the following time/rate relations: Power-law exponential model Stretched exponential model Logistic growth model Duong model
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".