Improved Estimation of Gas Well Deliverability from Single-Point Tests
Bibliographic record
Abstract
Abstract Chase and Alkandari developed dimensionless inflow performance (IPR) curves for predicting the stabilized deliverability of hydraulically fractured gas wells using just a single-point test, namely a pressure build-up or draw-down test. Unfractured wells can also be analyzed by converting the apparent skin factor to an equivalent ratio of Xe/Xf. Results obtained from the dimensionless IPR curve model can be used to generate values of n and C for the equation of stabilized deliverability. This research describes the process used to evaluate the effectiveness of the single-point model using data from twenty-five Canadian well tests and nine simulated well tests. The tests were analyzed using four-point test methods, the dimensionless IPR curve method, and by assuming that the exponent of the stabilized deliverability equation was equal to one. The absolute value of error between the AOF predicted using multi-point deliverability test analysis methods and the dimensionless IPR curve method for the twenty-five Canadian wells was 9.2 %, with a standard deviation of 8.7 %. The absolute value of error between the AOF predicted using multipoint deliverability test analysis methods and the dimensionless IPR curve method for the nine simulated wells was 5.1 % with a standard deviation of 4.7 %. The absolute value of error between the AOF predicted using multi-point test methods and by assuming that the exponent of the stabilized deliverability equation was equal to one for the twenty-five wells was 30.5 % with a standard deviation of 25.2 %. Introduction The deliverability or inflow performance of a gas well is usually predicted by utilizing one of three well testing methods: the conventional backpressure test(1); the isochronal test(2); or the modified isochronal test(3). All three methods normally require that four flow tests be performed on a well, including one to stabilization, to accurately predict stabilized deliverability. Industry practice sometimes shortcuts these methods utilizing just three, two and sometimes just one flow test. In the latter case, the exponent, n, of the stabilized deliverability equation, given by equation (1) is frequently assumed to be equal to one in order to estimate deliverability. Equation (1) (Available in full paper) Chase and Alkandari(4) developed a single-point test method that uses dimensionless IPR curves for predicting the inflow performance of fractured gas wells producing under stabilized or pseudosteady state flow conditions. The model was developed in an attempt to better estimate gas well deliverability when just a one-point test, namely a drawdown or build-up test, is conducted. The following equation serves as the basis for the single-point dimensionless IPR curve method. Equation (2)(Available in full paper) The SPE paper by Chase and Alkandari describes how Monte Carlo simulation was used to develop the model and generate values for the coefficient M and exponent N as a function of Xe/Xf. The M values were plotted versus the Xe/Xfratio on a log-log plot and a least squares cubic fit of the data was obtained resulting in equation (3). Equation (3)(Available in full paper)
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.000 |
| 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".