Chemical Stimulation at a Heavy-Oil Field: Key Considerations, Work Flow, and Results
Notice bibliographique
Résumé
This article, written by Special Publications Editor Adam Wilson, contains highlights of paper SPE 184974, “Chemical-Stimulation Pilot at a Heavy-Oil Field: Key Considerations, Work Flow, and Results,” by Mauricio Gutierrez, Fernando Bonilla, Layonel Gil, and Wilmer Parra, Ecopetrol, and Pablo Campo, SPE, Alex Orozco, and Monica Garcia, Halliburton, prepared for the 2017 SPE Canada Heavy Oil Technical Conference, Calgary, 15–16 February. The paper has not been peer reviewed. Because of current oil and gas industry economics, evaluating the return on investment for any well-intervention campaign is crucial, as is applying an assurance process to help quantify desired production improvement. This paper presents the planning and execution of a matrix-stimulation pilot project in the heavy-oil Chichimene Field in Colombia. The approach is based primarily on a work flow that includes characterizing formation damage, reviewing laboratory tests, validating well selection, and determining economically viable placement and diversion techniques. Damage Mechanisms Heavy-oil reservoirs are prone to almost every formation-damage mechanism known. Damage mechanisms encountered include fines migration, paraffin and asphaltene deposition, various forms of scale, and clay swelling. Many of these damage mechanisms are compounded by the methods used to produce heavy oil, including slotted liners, screens, and gravel packs, which can plug off as a result of any of the damage mechanisms and, over time, further reduce inflow and well performance. A process to identify and characterize formation damage in the Chichimene Field was established. For this purpose, several wells were selected to analyze formation-damage distribution. Reservoir-property data were uploaded into a simulator with a dynamic model to quantify the effects of formation damage attributed to pressure drop in the reservoir. The following damage mechanisms were observed. Drilling- and Workover-Induced Damage. Water, solids, or both, when used in drilling or during workovers, tend to decrease the effective permeability of the formation. Water from drilling fluids contains additives that produce chemical reactions with the formation, which can generate precipitates that plug pore throats. Solids from the drilling and completion fluids also can physically plug or bridge pore throats. Organic Deposition. Organic deposition usually occurs in two forms—paraffin and asphaltenes. Those hydrocarbons classified as paraffins are generally inert. They are resistant to dissolving in acids, bases, and oxidizing agents. Additionally, paraffin deposits often include other materials, such as scale, sand particles, or asphaltenes. Asphaltene deposition is a more subtle form of deposition. It is not usually visible in the field, and extensive laboratory testing is necessary for its detection. Inorganic Deposition. Common forms of inorganic deposition include calcium carbonate scale and gypsum scale. Less common, but more difficult to treat, are iron-rich deposits and silica scale. Numerous methods exist for the removal of inorganic scale, including simple mechanical methods, such as jet washes and complex acid/solvent washes.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».