Acid Gas Sequestration During Tertiary Oil Recovery: Optimal Injection Strategies and Importance of Operational Parameters
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».