Application of the "Continuous Estimation of Ultimate Recovery" Methodology to Estimate Reserves in Unconventional Reservoirs
Bibliographic record
Abstract
Abstract The estimation of reserves in unconventional reservoirs using rate-time decline relations is both challenging and often non-unique due to the very long transient flow periods exhibited by the production data. The misuse of rate-time relations during the transient flow regime can result in significant overestimation of reserves. Consequently the development of a systematic methodology which uses rate-time production data analysis and also deals with the uncertainty associated with reserve estimates should be beneficial. This work presents the application of the recently introduced "Continuous Estimation of Ultimate Recovery" or "Continuous EUR" methodology (Currie et al. 2010) to estimate reserves in unconventional reservoirs by providing a variety of tight and shale gas examples. The "Continuous EUR" methodology is a procedure that employs several rate-time models to produce a profile of EUR versus production time. Upper and lower limits of ultimate recovery are established thereby reducing the uncertainty in reserves estimation prior to the onset of boundary-dominated flow. We integrate both traditional and new rate-time relations to provide the upper limit for EUR. We show that the rate-time relations that better represent the transient flow regimes (i.e., the power law exponential rate decline relation) provide a more accurate upper limit for EUR compared to tradition rate decline relations (i.e., Arps' hyperbolic relation). We also use a straight line extrapolation technique and the power law exponential relation to produce forecasts of rate-time data that are influenced by boundary-dominated flow. The EUR estimates from these relations are used to establish a lower limit for reserves. The difference between the upper and lower limit of reserves decreases with time and converge to the "true" value of reserves. The proposed methodology is applicable for all reservoir systems where production data is continuously acquired. In this work, the proposed methodology is extremely useful for estimating time-dependent reserves in unconventional reservoirs. We successfully demonstrate that the "Continuous EUR" methodology is a valuable tool for reducing the uncertainty in estimating reserves for unconventional gas reservoirs. Introduction Unconventional reservoir systems have recently become a topic of increased interest because of their potential for significant hydrocarbon production and reserves potential. These reservoir systems offer unique challenges as they are difficult to characterize and produce using conventional methods due to their low permeability nature. In particular, unconventional gas reservoir (i.e., tight and shale gas reservoirs) require enhanced drilling and completion techniques (i.e., horizontal drilling and hydraulic fracturing) to establish production at economic rates. In addition, new approaches for reservoir characterization and production analysis have been developed to better describe reservoir behavior and predict long-term well performance.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".