Time-Lapse Surveillance of Multiphase Reservoirs in Harsh Environments Using Heat-Flask Next-Generation Pulsed Neutron Logging Technology
Bibliographic record
Abstract
Abstract Heavy oil production often requires a thermal recovery technique. An accurate understanding of the current reservoir fluid components in the post-steam injection stage is essential for evaluating the efficiency of the thermal recovery process and making timely reservoir management decisions. This paper presents the characterization of steam propagation and subsequent heavy oil and water redistribution in a thermal recovery process using heat-flask next-generation pulsed neutron (PN) well logging technology. We developed a next-generation multidetector PN tool equipped with a heat-flask housing for logging wells where the thermal heavy oil recovery process is implemented. The key features of the next-generation PN tool include upgrading a pulsed neutron generator (PNG), denser and higher gamma-ray count rates, higher-resolution lanthanum bromide (LaBr3) detectors, and a digital electronics system. These improvements in the tool enabled faster acquisition of the required pulsed neutron data without sacrificing data quality. Moreover, these enhancements allowed the simultaneous acquisition of two different types of PN measurements, which historically required two separate logging runs. The two primary datasets are (1) a salinity-independent time-spectra-based gamma-ray ratio measurement sensitive to the steam volume and (2) inelastic energy-spectra-based C/O ratios that mainly differentiate oil from water. Tool-, well- and reservoir-specific forward modeling of two PN measurements was performed using the Monte Carlo N-Particle (MCNP) method. Two salinity-independent nuclear data and predicted stochastic models were integrated to quantify multiphase formation volumes simultaneously. We present a case study of time-lapse monitoring of steam-flooded heavy oil reservoirs using legacy and new PN tools in a steam-assisted gravity drainage (SAGD) project. Before steam injection, a base log run was acquired to identify the unperturbed heavy oil distribution. Subsequent logging runs were performed after steam was injected. A comparison of the acquisition time and data quality from the previous- and next-generation PN tools revealed that equivalent quality data were recorded at least three times faster with the next-generation tool than with the previous-generation tool. Furthermore, PN surveillance revealed steam chamber growth and heavy oil distribution profiles. With the introduction of the next-generation PN tool and heat-flask housing, PN well logging in harsh environments at temperatures exceeding 175 degrees Celsius (or 350 degrees Fahrenheit) became efficient. In addition, compared with the conventional sequential approach, an innovative PN data analysis technique effectively characterized and quantified three fluid components with the simultaneous use of two PN measurements.
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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 | 0.000 | 0.000 |
| 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".