Review of and Outlook for Enhanced Oil Recovery Techniques in Kuwait Oil Reservoirs
Bibliographic record
Abstract
Abstract In this paper, we carried out an updated investigation of enhanced oil recovery (EOR) applications in Kuwait. The investigation employs EOR screening analysis to determine the suitability of EOR processes to mature oil reservoirs. Following the determination of the suitable EOR process, we performed incremental recovery (IR) calculations. We also performed a preliminary economic analysis to determine the economic feasibility of the EOR processes in question. The screening analysis revealed that CO2 and other miscible processes would have wide applications in these mature oil reservoirs. Polymer, surfactant/polymer, and alkaline flooding processes would also be widely applicable. In some cases, the polymer and surfactant/polymer processes were not suitable due to high reservoir temperature. Some of the important oil reservoirs have shown to be suitable for the application of thermal recovery processes, particularly steam injection. A relatively new injection method known as steam assisted gravity drainage (SAGD) appears to be viable for enhancing heavy oil recovery from the oil reservoirs containing heavy oil. These reservoirs are thick fractured layers, which can be good candidates for drilling horizontal wells where injected steam chambers can contact large volumes of oil. The presence of fractures may present a conduit for steam to cover a large volume pore space. The incremental recovery calculations indicated that the additional recovery is 10-12% for miscible, 4-5% for polymer, 20-22% for surfactant/polymer, all of which refer to percentage of original oil in place (OOIP) in addition to water flooding. The fractional recovery due to the application of steam injection (SAGD) could be as high as 50% of the OOIP for the reservoirs containing the heavy oil. The economic analysis revealed that the oil market environment is favorable for the application of EOR processes in Kuwait where reservoirs are thick and contain huge oil volumes. Introduction Kuwait has one of the largest recoverable oil reserves in the world and most, if not all, of its production is still due to primary forces. The primary and secondary forces are expected to recover approximately 45% of the oil in place.1,2 The huge volume of unrecoverable oil will be the target of enhanced oil recovery techniques that have become attractive because of the higher oil prices. If the heavy oil sands of Alberta can become economically attractive, then the much lighter oil in Kuwait oil reservoirs will certainly present a more attractive target for enhanced oil recovery processes. Due to the large current production capacity of Kuwait oil fields, no published study has been undertaken previously to investigate the Enhanced Oil Recovery (EOR) potential in Kuwait, as the capacity due to primary and secondary techniques is more than enough to meet the daily production requirements. In this paper we investigate the applicability of EOR processes in Kuwait. The potential of applying EOR in the various oil reservoirs is done by applying the screening criteria of the different EOR processes.3–5 Once a reservoir was found to be suitable for one or more enhanced oil recovery processes, the incremental recovery over primary and secondary was calculated for each process. Using the incremental recovery figures and the costs for the specific process, we conducted a preliminary economic analysis. The incremental recovery calculations and the economic comparisons were based on the simple models developed by U.S Department of Energy.6 The fluid and reservoir data were gathered from the literature; assumptions were made for unavailable data when required.7–17 Enhanced Oil Recovery Methods Oil reservoirs, following discovery, undergo production of oil by primary recovery. In primary recovery, the energy and pressures inherent in the reservoir are used to force the oil through the complex pore network toward the producing wells. The primary recovery mechanism can differ from one reservoir to another, depending on the type of reservoir and the properties of the rock and the hydrocarbons present. Following the primary stage, the reservoir energy is supplemented by the injection of water and/or gas in a "secondary recovery" stage. The ultimate oil recovery by primary and secondary recovery usually ranges from 25-45% of OOIP.1 As the secondary recovery approaches its economic limit of operation, the "tertiary recovery" process begins.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".