Optimizing Well Trajectories in Steam-Assisted-Gravity-Drainage Reservoir Development
Bibliographic record
Abstract
Summary An important task before starting a steam-assisted-gravity-drainage operation is optimizing the location of the well trajectories. There are shale barriers and other heterogeneous features at different positions in these reservoirs. Steam cannot pass a thick shale barrier. Also, if there is a shale barrier between the injector and producer, oil cannot drain to the producer. For these reasons, optimizing the injector- and producer-well trajectories is important. Running the reservoir simulator is time-consuming, and running trial-and-error cases to optimize the producer and injector trajectories is not practical. On the other hand, robust well-trajectory optimization must consider uncertainty in the reservoir parameters. This optimization problem cannot be solved by calling the simulator for many possible trajectories and geostatistical realizations. To tackle this problem, a new semianalytical approximate thermal simulator modeled after Butler's theory (a proxy) has been developed and tested on different synthetic and realistic history-matched 2D and 3D models (Dehdari and Deutsch 2013; Dehdari 2014). Many modifications, extensions, and improvements have been made to Butler's original model. This proxy can be used as a substitute to the reservoir simulator. In this paper, two methods are tested for well-trajectory optimization. The first method is based on random sampling from a 3D box that has been selected for drilling wells. Then, the differential-evolution-optimization algorithm has been used for automatically improving the trajectory location. The second method is based on parameterizing the trajectory by use of a Hermite spline, then optimizing the parameters of the spline. The producer and injector trajectories of a realistic model with a single realization and a synthetic model with 100 realizations have been optimized by use of these methods.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".