Multizone Casedhole Frac Packs and Intelligent-Well Systems Improve Recovery in Subsea Gas Fields
Bibliographic record
Abstract
Summary A nine–well subsea development project has been completed using casedhole frac packs (CHFPs) for sand control and multizone intelligent–well systems (IWSs) to improve recovery from a series of shallow, low–pressure gas reservoirs. In these wells, CHFPs have been installed to provide reliable sand control over the long, low–net–to–gross–ratio sand/shale target sequence: typically, three to six frac packs per well. This outer CHFP completion is then augmented with a multizone IWS, consisting of isolation seals, surface–controlled zonal–isolation valves, and downhole–pressure/temperature (DHP/T) gauges. The IWS string is run as a separate inner string to provide flow–monitoring capability and allow shutoff of zones producing high water volumes. This critical water–shutoff capability eliminates the risk of one or more high–water–production zones loading up and killing adjacent low–pressure gas zones, with the associated loss of reserves. To date, a total of nine wells have been completed and are being produced from three subsea gas fields. To maximize recovery from the fields’ numerous but relatively thin gas reservoirs, production wells are completed over three to six separate intervals. These frac–packed intervals are then grouped to allow flow control and pressure/temperature monitoring to occur through up to six surface–operated interval control valves (ICVs) and associated downhole gauges. This combination of sand control and intelligent–well control has provided an ability to perform multirate tests (MRTs) and pressure–buildup (PBU) tests on each reservoir interval to detect the start of water production or identify other impending production issues. After approximately 6 years of production service to the October 2018 date of this paper, 16 of the 34 zones completed in the nine–well project have been shut in to eliminate high water production. These water–shutoff actions performed using the surface–controlled ICVs are estimated to have improved gas–recovery factors from 50 to 60% without requiring rig intervention. This paper describes the reservoir challenges addressed and the completion–design and –operating practices used in this successful program.
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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".