Field Application of an Interpretation Method of Downhole Temperature and Pressure Data for Detecting Water Entry in Inclined Gas Wells
Bibliographic record
Abstract
Abstract Accurate and reliable downhole data acquisition has been made possible by advanced permanent monitoring systems such as downhole pressure and temperature gauges and fiber optic sensors. These downhole measurement instruments are increasingly incorporated as part of the intelligent completion in complex (highly slanted, horizontal, and multilateral) wells where they provide bottomhole temperature, pressure and sometimes volumetric flow rate along the wellbore. To fully realize the value of these intelligent completions, there is a need for a systematic data analysis process to improve our understanding of reservoir and production conditions using the acquired data and to make decisions for well performance optimization. We have successfully developed a model to predict well flowing pressure and temperature (i.e. the forward model), and applied inversion method to detect water and gas entry into wellbore using the synthetic data generated by the forward model (i.e. the inversion model) in the previous study. It is concluded that temperature profiles could provide sufficient information to identify fluid entries, especially in gas wells. However, both the mathematical complexity and advanced well structure lead to challenges in model validation and application. In this study, we applied the wellbore-reservoir flow coupled thermal simulation model to high-rate gas wells with field data. The main objectives are to evaluate applicability of the model to field problems, to study the sensitivity of parameters such as permeability and reservoir pressure on accuracy of interpretation, and to generate practical guidelines on how to initialize the inversion process. The model is applied to highly-slanted gas wells with water produced from a bottom aquifer. The interpretation result was compared against production logging data. The sensitivity of interpretation error to input reservoir properties are examined and the results showed that temperature and pressure anomalies caused by water production and flow rate changes can be detected theoretically and also practically. Judgments should be used based on the understanding of temperature and pressure behavior when initializing the forward model and this can increase efficiency of model application. The study results and guidelines developed in this study will help us to design permanent monitoring systems and set realistic expectation for predictive capability of intelligent well systems.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| 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".