Rate transient analysis of multiple wells system producing at constant bottomhole pressures
Bibliographic record
Abstract
This paper presents rate transient analysis of multiple wells system producing at constant bottomhole pressures during boundary-dominated flow period in a closed rectangular reservoir. The proposed algorithm is based on an analytical model with numerical approximation, and production decline is predicted through a series of mathematical methods such as the Laplace transform, the Dirac Delta function, convolution, the Green’s function and the superposition principle. The proposed model is validated by the Computer Modeling Group (CMG) simulation, the results show that the proposed model is accurate enough to predict the production performance of multi-well system producing under constant bottomhole pressures during boundary-dominated flow period in a closed rectangular reservoir. We conclude that at a given time, the flow rate of a well decreases as the total number of wells increases and the bottomhole pressures of adjacent wells decrease, while the total reservoir production increases as the bottomhole pressures of reservoir wells decreases. And at a given time, the greater distance between the observation well and adjacent wells, the larger the flow rate and cumulative production the observation well. In terms of the decline rate of the flow rate, it depends on the number of wells, the bottomhole pressures of adjacent wells, and the size of reservoir. The conventional models presented in the literature are mostly empirical or semi-analytical, which are not grounded in fundamental theory. Our proposed model has a solid theoretical basis, it provides a computationally efficient, accurate and convenient method for predicting transient flow rates of multiple wells producing at constant bottomhole pressures in a closed rectangular reservoir. Document Type: Original article Cited as: Lu, J., Zhai, Y., Yang, E. Rate transient analysis of multiple wells system producing at constant bottomhole pressures. Computational Energy Science, 2024, 1(3): 150-163. https://doi.org/10.46690/compes.2024.03.03 References Agarwal, R. G., Gardner, D. C., Kleinsteiber, S. W., et al. Analyzing well production data using combined type curve and decline curve analysis concepts. Paper SPE 49222 Presented at SPE Annual Technical Conference and Exhibition, New Orleans, Louisiana, 27–30 September, 1998. Agbi, B., Ng, M. C. A numerical solution to two-parameter representation of production decline curve analysis. Paper SPE 16505 Presented at Petroleum Industry Application of Microcomputers, Lake Conroe, Texas, 23–26 June, 1987. Anderson, D. M., Thompson, J. M., Behmanesh, H. Diagnosing the health of your well with rate transient analysis. 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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".