Coupling of Wellbore and Surface-Facilities Models With Reservoir Simulation To Optimize Recovery of Liquids From Shale Reservoirs
Bibliographic record
Abstract
Summary The objective of this paper is to couple wellbore and surface-production-facilities models with reservoir simulation for a shale reservoir that contains dry gas, condensate, and oil in separate geologic containers within the same structure. The goal of this integration is to improve liquid recoveries by dry-gas injection and gas recycling. Methods previously published investigate possible means of improving recovery from shales and have concentrated on laboratory work and the reservoir itself, but have ignored the wellbore and surface-production facilities. The coupling of these facilities in the simulation work is critical, particularly in cases involving condensate and oil reservoirs, gas injection, and recycling operations. This is so, because a change in pressure in the reservoir is reflected almost immediately in a change in pressure in the wellbore and in the surface installations. The development presented in this paper considers multistage hydraulically fractured horizontal wells. Dry gas is injected into zones that contain condensate and oil. Gas stripped from the condensate production is reinjected in the condensate zone in a recycling operation. The study focuses on the Eagle Ford Shale, which has separate containers for each fluid within the same structure. The study leads to the conclusion that, for the studied system, liquid recoveries can be maximized with continuous and huff ’n’ puff gas-injection schemes. In general, huff ’n’ puff injection provides better results in terms of production and economics. Molecular diffusion is found to play a crucial role in continuous gas-injection operations. Conversely, the effect of this phenomenon is negligible in huff ’n’ puff gas injection. This research demonstrates that proper design of wellbore and surface installations, including, for example, downhole pumps and compressors, is important because it plays a critical role in the performance of production and injection operations and in maximizing recovery of liquids from shale reservoirs. The novelty of the methodology developed in this paper is the coupling of models that handle surface facilities; wellbores; numerical simulation including oil, condensate, and dry-gas reservoirs; gas injection; and gas/condensate-recycling operations. Essentially, the shale containers, wellbore, and surface facilities are continuously “talking” to each other. To the best of our knowledge, this integration for shales has not been published previously in the literature.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".