Simulation of Flow-Control Devices With Feedback Control for Thermal Operations
Bibliographic record
Abstract
This article, written by Special Publications Editor Adam Wilson, contains highlights of paper SPE 163594, ’Advanced Wellbore Simulation of Flow-Control Devices With Feedback Control for Thermal Operations,’ by Terry Stone, SPE, Schlumberger Information Solutions; Carlos Emilio Perez Damas, Schlumberger Calgary Regional Technology Centre; Glenn Woiceshyn, SPE, Absolute Completion Technologies; David Hin-Sum Law, SPE, Schlumberger Calgary Regional Technology Centre; George Brown, Schlumberger Fibre Optics Technology; and Peter Olapade and William J. Bailey, SPE, Schlumberger-Doll Research, prepared for the 2013 SPE Reservoir Simulation Symposium, The Woodlands, Texas, USA, 18-20 February. The paper has not been peer reviewed. Factors such as hydraulic gradients in the horizontal completion, geologic and fluid variations in the reservoir, and well-placement issues can produce very poor steam conformance in steam-assisted gravity drainage (SAGD). Using proportional-integral- derivative (PID) feedback to control steam injection can lead to improvements in SAGD. Inflow- or injection-control devices (ICDs) can also improve SAGD performance. This paper examines detailed wellbore simulations of a SAGD process in which wells are equipped with a combination of ICD completions and feedback control to determine the physical mechanisms and outline practical procedures to determine an improved ICD completion and feedback-control design. Introduction SAGD is the most extensively used process for development of the bitumen resources in western Canada. Fig. 1 shows the concept of this process in which two closely spaced horizontal wells are placed such that the upper well injects steam and the lower producer collects reservoir fluids that drain mostly by gravity from a constantly evolving steam chamber. Ideally, the steam chamber evolves uniformly along the entire length of the well pair. However, the actual pattern of SAGD well pairs shows very irregular steam-chamber development along the lengths of most of the pairs in the pattern. For many years, SAGD operators in this region have been evaluating methods to improve conformance along well pairs. One such method is to install dual- tubing strings in the injector and producer. Prescribed injection and production rates from each tubing string may be determined from a reservoir-engineering analysis of the formation around the pair, but such analyses are often inexact. In order to prevent the steam chamber from touching the lower producer, which would then remove hot steam instead of using it more efficiently in the upper reaches of the chamber, the injection and production rates are usually set to maintain a prescribed temperature difference between fluids exiting the upper injector and entering the lower producer. This temperature difference, also referred to as a “subcool” because it is set to be several degrees below a water saturation temperature, may be controlled at both the heel and the toe of the well pair by use of the ability to inject and produce from the two tubing strings that are landed at these points. However, setting these injection and production rates to reflect the current state of the reservoir and current subcool is difficult with conventional reservoir-engineering analysis. To improve on this, a proposal was made to use a feedback controller to monitor temperatures of produced and injected fluids automatically, with a target subcool at the heel and the toe of the well pair. By targeting the same subcool at the heel and the toe, two objectives are accomplished: (1) achieving the subcool prevents steam from entering the lower producer, and (2) both toe and heel halves are encouraged to produce uniformly because both are targeting the same subcool (i.e., if one-half temporarily operates at a lower or higher subcool than the target, steam injection is decreased or increased, respectively, in that half to compensate). Another method to improve conformance is to install carefully constructed ICDs in either the injector or the producer, or in both. When placed in the injector, these devices can equalize the outflow of steam from heel to toe better, regardless of variations in reservoir mobility properties.
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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".