Prototype Test of an All-Electric Intelligent-Completion System for Extreme-Reservoir-Contact Wells
Bibliographic record
Abstract
Summary One of the core focus areas in Saudi Aramco's effort to increase hydrocarbon recovery is in the application of extreme-reservoir-contact (ERC) wells. These wells, with more than 20 km of reservoir contact, are needed to ensure fluid off-take points are distributed throughout the reservoir efficiently. Accurate sensing and control are crucial to the efficient sweep of heterogeneous formations. This paper describes a recent multilateral (ML)-well trial that validated a number of core technologies and methods required to make ERC a reality, including Well construction and deployment practices to allow electrical umbilicals to be reliably deployed in openhole laterals Deployment and testing of revolutionary low-power, infinitely positioned electric flow-control valves (FCVs) designed to be deployed in each compartment in an openhole segmented lateral completion Validation of the fully integrated onboard-production-monitoring system providing direct downhole measurements of pressures, temperatures, flow rates, and water cut for each controlled compartment Integration of the surface acquisition and monitoring system to the production supervisory-control-and-data-acquisition (SCADA) system to provide real-time downhole production information, as well as valuable system-health-status data that can ensure operational integrity during the life cycle of the well An ML well close to the oil/water-contact point was allocated to validate the functionality of two prototype systems installed and has provided valuable reservoir data for more than 1 year of production. The well trial successfully demonstrated the ability to install and retrieve an umbilical completion from a 10,000-ft horizontal lateral. The ability to control downhole flow to within a few barrels per day measured at the reservoir face is proving revolutionary to the way the operator will approach future reservoir management. The sensing system will be capable of delivering continuous compartment productivity. The SCADA integration allows for a real-time management function such that the compartment can be controlled to a target off-take rate directly, without resorting to the use of traditional well-system models for estimating control settings. This paper highlights the objectives, installation, validation, and functional aspects of this new ERC well system, as well as identifying some of the immediate production effects emerging from this level of visibility and control at the formation face.
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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 | 0.001 | 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".