Direct Method of Estimating Average Reservoir Pressure for Flowing Oil and Gas Wells
Bibliographic record
Abstract
Abstract A knowledge of the average reservoir pressure (p¯) and its changes as a function of time or cumulative production is essential to determine the oil-in-place (OIP) or original gas-in-place (OGIP), to estimate reserves and to track and optimize reservoir performance. The common practice of determining p¯ in moderate permeability reservoirs has been to run pressure buildup tests. In the current economic environment, buildup tests are almost non-existent except for very expensive exploratory wells. Moreover, time required for a pressure buildup test to reach p¯ in low permeability reservoirs is prohibitively long. Fortunately, flowing pressures and rate data are continually collected from oil and gas wells. Data quality and quantity is usually good especially from wells installed with permanent pressure gauges. Such data for gas wells is currently being analyzed by assuming OGIP and estimating p¯ required for calculating pseudo time. This is done in an iterative manner for using advanced decline curve analysis methods. The purpose of this paper is to discuss a new finding that will enable direct estimation of p¯ using flowing pressures and rate data obtained from oil and gas wells during the pseudo steady-state flow period. In theory, pseudo steady-state flow requires that a well is produced at a constant rate. However, this limitation can be easily removed based on the work published in the SPE literature by this author and others whereby variable rate data can be converted to constant rate production data. The significance of the subject paper is that it will permit: a) direct determination of p¯ using flowing wellbore pressures and rate data thus facilitating estimates of OGIP and OIP, b) estimation and/or validation of the value of the initial reservoir pressure (pi), which is normally suspect, and finally, c) enhancement or possible elimination of the current iterative process used for determining OGIP by advanced decline curve analysis methods.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.002 | 0.001 |
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".