Efficient Detection of Productive Intervals in Oil and Gas Reservoirs
Bibliographic record
Abstract
Abstract The utility and evaluation of cutoff values for net pay or net-to-gross determination have been hotly debated topics since the 1950s. There are numerous subtleties to cutoffs, but exactly how the values are calculated has largely been overlooked. Most cutoff users have been content to use a regression line to calculate the cutoff value. We show that cutoffs obtained using a regression line are likely to be inferior to estimates produced by other methods. When four methods were applied to two field datasets and compared, regression-based porosity cutoffs were between 1 and 2 pu different than the values that give the smallest number of errors. Monte Carlo simulations broadly support the results obtained from the datasets. One method, the "trial-and-error?? method, performed well through most of the tests, reducing errors by 40% from those obtained using the regression line-based cutoff. All cutoff estimation methods have errors, caused by the imperfect relationships we have between variables, such as porosity and permeability. This study shows we have a choice of methods. Because the better method can be easily applied in spreadsheet software, this should be a valuable addition to the petrophysicist's toolbox. Introduction Net pay (NP) may be defined as any interval containing economically producible hydrocarbon using a specific production method. This represents the portion of the reservoir that contains sufficient porosity, permeability and hydrocarbons for economic exploitation. NP can be interpreted as an effective thickness that is pertinent to identification of flow units and target intervals for well completions and stimulation programs [Worthington and Cosentino(1)]. The associated net-to-gross ratio (NGR) corresponds to the proportion of the total or gross thickness, which is composed of net pay. Numerous papers have reviewed and proposed methods for NP and NGR determination. Snyder(2) covers many of the methods in use up to the early 1970s, which used the self-potential (SP) or gamma ray (GR) logs and core analysis. More recent proposals include using capillary pressure [Vavra et al.(3)], probe permeameter measurements [Flølo et al.(4)] and percolation modelling [Li et al.(5)]. Of the large variety of possible methods, one approach is much more commonly discussed than any other. This method involves defining threshold values (or cutoffs) for the characteristics of interest and their surrogates. These limiting values are designed to define those rock intervals that show potential to contribute significantly to economic hydrocarbon production.
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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 | 0.003 | 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 | 0.001 |
| 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".