Acid Gas Sequestration During Tertiary Oil Recovery: Optimal Injection Strategies and Importance of Operational Parameters
Bibliographic record
Abstract
Abstract This paper reports on an optimization study for acid gas injection into a fully depleted oil reservoir by numerical modeling. As a special case, the Zama Keg River Z3Z Oil Pool with one horizontal production well and previous acid gas disposal was considered. Acid gas generation (60 – 80% CO2 and 20 – 40% H2S) and safe geological disposal, or conversion to elemental sulphur with associated emissions, is an ongoing concern at Apache's Zama Gas Plant operations. The opportunity for a possible enhanced oil recovery application in the Zama field was foreseen given that use of CO2 in combination with H2S (acid gas) is known to reduce the minimum miscibility pressure with reservoir oils relative to using pure CO2 as a miscible agent. Storing H2S with the CO2 in underground reservoirs will double the benefit for the environment in terms of both short- (mainly H2S) and long-term (mainly CO2) effects to the environment. Ten (10) pinnacles were selected as potential candidates for a pilot project of acid gas injection (sequestration and EOR). Optimal conditions that maximize the oil recovery and the amount of acid gas sequestered were identified for one of these ten pinnacles-the Zama Keg River Z3Z Pool. Special attention was given to breakthrough times, incremental oil recovery and CO2/H2S sequestration volumes. After constructing the static reservoir model using the available data with stochastic/geostatistical techniques, history matching was performed. The compositional simulation option of a commercial simulator (ECLIPSE) was used for this purpose. Available PVT data were used and other data needed were generated using correlations. A number of different injection scenarios were then tested for the combination of optimum incremental oil recovery and acid gas sequestration. The following parameters were considered in the optimization study:miscibility;gravity override;cyclic injection;injection rate; and,injection and production well constraints (completion). Optimum injection strategies yielding maximum oil recovery and maximum acid gas storage, as well as delaying breakthrough time, were evaluated for these cases. Introduction The natural gas sweetening process produces sales gas and acid gas (CO2 and H2S) as a waste with a high percentage of CO2 in the Zama Field. The catalytic conversion of H2S into element sulphur, commercially called a Clause process, is a good economic process during times of high demand and high prices for sulphur. Reduction in world price of sulphur and the environmental hazard of stockpiling elemental sulphur in large blocks is a cause for concern in the oil and gas industry. Energy producers around the world are focusing on a value-added approach to enhanced oil recovery (EOR) or enhanced gas recovery (EGR) for greenhouse gas (GHG) disposal(1–4). Different injection strategies for CO2 injection, flue gas injection and Water Altering Gas (WAG) with CO2 have been studied and implemented for EOR since the 1970's(5–10). Acid gas was found to be an effective EOR agent since H2S reduces the minimum miscibility pressure (MMP) of CO2(11–13). By 2003, approximately 2.5 Mt CO2 and 2.0 Mt H2S have been stored in depleted oil/gas reservoirs or deep saline aquifers(14).
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".